{"as_of":"2026-08-07T10:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bc92e6022b3367160073e8b09cdc11c714c7f7d72af5a9639ba95f43012b9329","coverage":[{"denominator":86,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":86,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:14:04.857514Z","state":"measured"},{"denominator":90,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":90,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:49:41.948805Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-10T12:15:01.137692Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.14302","snapshot_observed_at":"2026-08-06T22:49:41.948805Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20605","last_updated":"2025-09-20T04:00:14Z","snapshot_observed_at":"2026-08-06T22:42:17.320100Z","submitted_at":"2025-06-25T16:49:05Z","title":"Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T22:49:41.948805Z"},"links":{"cited_paper":"/paper/2507.14302","citing_paper":"/paper/2506.20605"},"observation_digest":"sha256:2eac958efd04887050fa2df8ea80b4528452da1e9ebdbeb8528d6cc55c8431f6","observation_id":"feee2e65-26aa-4b80-a7bd-fb2ba6d2f9cd","resolution":{"observed_at":"2026-08-06T22:49:41.948805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"cited_work":{"arxiv_id":"2507.14302","doi":"10.48550/arxiv.2507.14302","metadata_source":"arxiv_reference","pith_arxiv_id":"2507.14302","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"(17) Rhodes, B.; Vandenhaute, S.; Šimkus, V.; Gin, J.; Godwin, J.; Duignan, T.; Neumann, M","venue":null,"work_id":"730036a1-a0f9-42da-a7bb-4a3357af8cb8","year":2025},"citing_paper":{"arxiv_id":"2601.03656","last_updated":"2026-05-18T20:26:53Z","snapshot_observed_at":"2026-08-02T20:04:33.034871Z","submitted_at":"2026-01-07T07:12:45Z","title":"Simultaneous Learning of Static and Dynamic Charges","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-21T16:50:56.804356Z"},"links":{"cited_paper":"/paper/2507.14302","citing_paper":"/paper/2601.03656"},"observation_digest":"sha256:a7bc2d757d882424bcacba479042a709e1b7306cc405a585763c391463130a22","observation_id":"d66ba25e-4ef9-4a38-9ab9-31095f2c10e0","resolution":{"observed_at":"2026-05-21T16:54:16.411940Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"cited_work":{"arxiv_id":"2507.14302","doi":"10.48550/arxiv.2507.14302","metadata_source":"arxiv_reference","pith_arxiv_id":"2507.14302","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"(17) Rhodes, B.; Vandenhaute, S.; Šimkus, V.; Gin, J.; Godwin, J.; Duignan, T.; Neumann, M","venue":null,"work_id":"730036a1-a0f9-42da-a7bb-4a3357af8cb8","year":2025},"citing_paper":{"arxiv_id":"2601.16331","last_updated":"2026-04-20T22:33:02Z","snapshot_observed_at":"2026-07-06T22:42:48.338004Z","submitted_at":"2026-01-22T21:32:16Z","title":"Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T11:28:28.935726Z"},"links":{"cited_paper":"/paper/2507.14302","citing_paper":"/paper/2601.16331"},"observation_digest":"sha256:bb0da3d6d7ac6f1e277e94aa5225f7f87f22d0c7bbd341325efe6c3b01e2c22e","observation_id":"71d45caa-54aa-4959-92fb-6ca69b984590","resolution":{"observed_at":"2026-05-16T11:30:52.654527Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"cited_work":{"arxiv_id":"2507.14302","doi":"10.48550/arxiv.2507.14302","metadata_source":"arxiv_reference","pith_arxiv_id":"2507.14302","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"(17) Rhodes, B.; Vandenhaute, S.; Šimkus, V.; Gin, J.; Godwin, J.; Duignan, T.; Neumann, M","venue":null,"work_id":"730036a1-a0f9-42da-a7bb-4a3357af8cb8","year":2025},"citing_paper":{"arxiv_id":"2606.07327","last_updated":"2026-06-10T08:53:54Z","snapshot_observed_at":"2026-07-06T23:47:00.425715Z","submitted_at":"2026-06-05T14:45:06Z","title":"Six Open Questions in Machine-Learned Interatomic Potential Foundation Models","version":2},"reference_index":154,"source":"pdf_text","source_observed_at":"2026-06-27T21:27:50.941166Z"},"links":{"cited_paper":"/paper/2507.14302","citing_paper":"/paper/2606.07327"},"observation_digest":"sha256:357ac43f43426d1c2d245f7c6ddc9e9486e219e79d3409bf964f070da1991ba8","observation_id":"924429c6-e568-4865-882c-cdc9d6606eb6","resolution":{"observed_at":"2026-07-02T19:37:19.160570Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.14302/citation-record","integrity":"/paper/2507.14302/integrity","json":"/paper/2507.14302/citation-record.json","paper":"/paper/2507.14302"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:51.251773Z","title":"Combining machine learning and computational chemistry for predictive insights into chemical systems,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:51.251773Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:78acae44d3648702ef2d1d2c67f04f8e34d34ec7c8aa6c0ae28501648c98a0b5","observation_id":"02690dcc-9d5c-4144-9182-de04c1ca4d5c","resolution":{"observed_at":"2026-08-06T16:13:51.251773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:51.374380Z","title":"Machine learning force fields,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:51.374380Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:230e165dad5fe9b403c2686fbe188536f4e706d6d8380f6a25344d5764689571","observation_id":"da3886ca-6280-472b-b039-182e014e1eef","resolution":{"observed_at":"2026-08-06T16:13:51.374380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:51.557467Z","title":"Learning intermolecular forces at liquid–vapor interfaces,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:51.557467Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:7e9af0114a2a3da4676e10509fe39cfa7d94da3af11a02945c42495d95311f80","observation_id":"886388b4-66f8-4194-b694-9e978d6ab4b8","resolution":{"observed_at":"2026-08-06T16:13:51.557467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:51.704078Z","title":"Incorporating long-range physics in atomic-scale machine learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:51.704078Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:e1860d12c1df9c998a9da1652e9417eaa53a2e8b0c1131726632c92020027953","observation_id":"e98dee59-ac6e-4c43-bebd-9c2303af83ec","resolution":{"observed_at":"2026-08-06T16:13:51.704078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:51.889186Z","title":"Physics-inspired equivariant descriptors of nonbonded interactions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:51.889186Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:237ac8b80722e65c8074b53ce90ceb6830b909fc91fae95a4f9f53c557217cdb","observation_id":"fec1a74e-0439-4706-ace2-063ec3d5f5d0","resolution":{"observed_at":"2026-08-06T16:13:51.889186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:17.064801Z","title":"A deep potential model with long-range electrostatic interactions,","venue":null,"work_id":"af5023b0-17c4-4f43-9cab-f4865370d9e6","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:52.067891Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:52fe31d1755e7d6ea8aeebfb241fb4a666fd955a4fe80549626fa32a7c8bf2e5","observation_id":"2ce0bd18-f19e-4c02-8411-af003a11c8f7","resolution":{"observed_at":"2026-08-06T16:14:17.128712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01642","last_updated":"2024-12-02T15:55:38Z","snapshot_observed_at":"2026-08-03T18:04:30.051759Z","submitted_at":"2024-12-02T15:55:38Z","title":"Electrostatic interactions in atomistic and machine-learned potentials for polar materials","version":1},"cited_work":{"arxiv_id":"2412.01642","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.01642","snapshot_observed_at":"2026-08-06T16:14:06.835630Z","title":"Electrostatic interactions in atomistic and machine-learned potentials for polar materials","venue":"cond-mat.mtrl-sci","work_id":"f2f7b926-efcb-4ce6-a059-00afbaad3aca","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:52.200940Z"},"links":{"cited_paper":"/paper/2412.01642","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:08b59237ba8ac26df4c3e969b6dec7aeff7a12068611bdab363aca9adb5c1bcc","observation_id":"ae86c640-2c39-4de9-bd6a-e9c81cc06962","resolution":{"observed_at":"2026-08-06T16:14:06.901792Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:16.888394Z","title":"Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges,","venue":null,"work_id":"8a6f0726-aae5-4bc8-9765-74585b654310","year":2019},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:52.349688Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:f3498129186bc3c55760bcf1e35a59c6f3a5caaecab71fdf11789244946b566c","observation_id":"4c252dec-4b16-49bf-abde-e2cda9642589","resolution":{"observed_at":"2026-08-06T16:14:16.971434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:16.714446Z","title":"A fourth-generation high- dimensional neural network potential with accurate electrostatics including non-local charge transfer,","venue":null,"work_id":"868a5349-5470-4e3a-bb6c-81484465d412","year":2021},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:52.472098Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:145825e398778f440bbf7145e658b9959d781442425beb681f85edcd4d66a847","observation_id":"0e5ce8d0-e561-4441-99bb-b5d2169b8d7e","resolution":{"observed_at":"2026-08-06T16:14:16.800757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:16.515647Z","title":"Self-consistent determination of long-range electrostatics in neural network potentials,","venue":null,"work_id":"c12c1964-a142-4de5-9b07-e1f76fd92621","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:52.654345Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:328c2750dd7d84329128cd60aed6cb3c81829102fd4c9a2e998a71c5c97925dd","observation_id":"ce6407be-4cbe-40d6-ba0b-d0f112bc8250","resolution":{"observed_at":"2026-08-06T16:14:16.600338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:16.213673Z","title":"Discovering a transferable charge assignment model using machine learning,","venue":null,"work_id":"182bf909-5ff1-4a40-9b92-89b425fe2a4e","year":2018},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:52.809317Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:e596506e79ae7d3953dcc92ef68d085753a3280a43185de5a25d78b58fd7f358","observation_id":"24677251-3d7c-4893-b3a7-0ca105dfd2d2","resolution":{"observed_at":"2026-08-06T16:14:16.345842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:15.941907Z","title":"A predictive machine learning force-field framework for liquid electrolyte development,","venue":null,"work_id":"f999ebb4-17ef-4149-afbd-405f488203e8","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:52.957328Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:0019da37039fd92309b8b7e73125b7a5026de7753fc86f9693550a38a8895afe","observation_id":"9fc12a52-08c1-4905-941e-527e26c011b5","resolution":{"observed_at":"2026-08-06T16:14:16.091929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:15.713721Z","title":"Incorporating long-range electrostatics in neural network potentials via variational charge equilibration from shortsighted ingredients,","venue":null,"work_id":"40b97c83-457a-484f-a5d4-d58c3684a8e9","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:53.099399Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:779d23c6a7484897a10237e25a3fc58147b63509c1197239f9d8fed9388208e2","observation_id":"8c18abd1-52fe-42a9-a52a-6e6cd242f819","resolution":{"observed_at":"2026-08-06T16:14:15.825441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.16684","last_updated":"2022-11-30T02:10:48Z","snapshot_observed_at":"2026-07-06T14:24:49.653414Z","submitted_at":"2022-11-30T02:10:48Z","title":"Capturing long-range interaction with reciprocal space neural network","version":1},"cited_work":{"arxiv_id":"2211.16684","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.16684","snapshot_observed_at":"2026-08-06T16:14:06.636350Z","title":"Capturing long-range interaction with reciprocal space neural network","venue":"cond-mat.mtrl-sci","work_id":"13c2a3fe-08cc-4452-9ea5-363b89d57601","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:53.241506Z"},"links":{"cited_paper":"/paper/2211.16684","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:30dec1594598c8eedecdd94d63743f9f466561de5e0c1e738d22e772677bac6c","observation_id":"d38cabfc-0183-4a0f-b47c-21a57da03606","resolution":{"observed_at":"2026-08-06T16:14:06.702503Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:15.518853Z","title":"Ewald-based long-range message passing for molecular graphs,","venue":null,"work_id":"377ae983-d8b7-418e-97d3-d7cb709af7ce","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:53.382577Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:efebd3e4e1bbee55e12a5bdce9bf6b4a860e8d2a1c56ade6321a7651bb119a79","observation_id":"7d962923-fb39-4dfb-addf-ebf89ceb1feb","resolution":{"observed_at":"2026-08-06T16:14:15.600913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:15.334081Z","title":"Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects,","venue":null,"work_id":"2dc235ee-60dd-4669-972b-30538455592f","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:53.520774Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:63d11d2caf27f37f038608c5fea6af593144c53905d854d6f4b4fbae4762680e","observation_id":"11a8e887-3ab9-4723-b0f8-87cbd331f6de","resolution":{"observed_at":"2026-08-06T16:14:15.415842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:15.085689Z","title":"Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects,","venue":null,"work_id":"272b4b22-9f8c-4d4e-8872-ba4c5d0a4098","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:53.634328Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:9b5c0411acde887c890cf044e1162a680d977b4f6af25ac8435a3a21a3b27200","observation_id":"d8e4e0d5-a667-4a2b-bcb6-bed726c0f2a0","resolution":{"observed_at":"2026-08-06T16:14:15.209309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13797","last_updated":"2025-02-20T11:07:01Z","snapshot_observed_at":"2026-07-06T20:39:15.111209Z","submitted_at":"2025-02-19T15:05:47Z","title":"Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding","version":2},"cited_work":{"arxiv_id":"2502.13797","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.13797","snapshot_observed_at":"2026-08-06T16:14:06.454198Z","title":"Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding","venue":"physics.comp-ph","work_id":"32a11924-c098-4a34-96bb-1b857c6e292d","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:53.799700Z"},"links":{"cited_paper":"/paper/2502.13797","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:f2d3399d0622b491b274bc67567cdb652fddf1b37a129c6d9f564358b50ff0d1","observation_id":"55e57eef-7726-4cb9-bd41-924989ce6cf5","resolution":{"observed_at":"2026-08-06T16:14:06.552304Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.916737Z","title":"Charge-constrained atomic cluster expansion,","venue":null,"work_id":"c80d7122-e187-4a20-82e0-0277627a6722","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:53.913173Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:048138e1deb9be535801163d9d7cbcb309f8278c260bc23ab31bdebc26eb9314","observation_id":"9d529a38-82bf-4bd3-a8a3-9c15ced0f917","resolution":{"observed_at":"2026-08-06T16:14:14.996930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.768942Z","title":"Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs,","venue":null,"work_id":"a6f9015c-792c-4987-b99e-edd441503800","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:54.021009Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:4d44c02f0614a030223c3b6f72b05c5376042fd44171bda73513b3d2b1ac8405","observation_id":"b03f3354-88b2-4e0d-9f63-bd5e951cdacd","resolution":{"observed_at":"2026-08-06T16:14:14.852715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.552309Z","title":"Molecular simulations with a pretrained neural network and universal pairwise force fields,","venue":null,"work_id":"b9e994b1-2e4a-40c3-88eb-cf9f29b1b870","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:54.146675Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:46d8f949d27f5374ac0caec7c5da85dede8b602a126a24f3026bfd817520c77e","observation_id":"3969ee2f-7c07-45f6-b3df-3797622b680c","resolution":{"observed_at":"2026-08-06T16:14:14.668998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.441900Z","title":"Density-based long-range electrostatic descriptors for machine learning force fields,","venue":null,"work_id":"1c751133-6663-4786-aa16-4c30a935e48f","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:54.299013Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:2dcdfd9314f05dd2e971ab64894fc147159b1431636135706d91621c9786b746","observation_id":"d8f53b71-e973-47e2-9434-7a701bbc1a5c","resolution":{"observed_at":"2026-08-06T16:14:14.484710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.370771Z","title":"A foundation model for accurate atomistic simulations in drug design,","venue":null,"work_id":"ee04eec0-76b8-4138-bb4f-599bd933e980","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:54.469261Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:1035e699adc4f868c716677caed70247a26168e7f5ad143181c4158fad966455","observation_id":"f005c5b0-edfc-40cb-9343-fd1fc5ae7eb6","resolution":{"observed_at":"2026-08-06T16:14:14.403576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.287771Z","title":"Fast and flexible long-range models for atomistic machine learning,","venue":null,"work_id":"63a831c1-6754-4d47-869b-cf1a1ab56d04","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:54.616923Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:7ed22e101be08416116eb29ca9c0007f964854aec25e5a11acc2a45b802b4fdb","observation_id":"5d7676d8-d162-4257-85dd-eb0011aec8af","resolution":{"observed_at":"2026-08-06T16:14:14.317444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.198993Z","title":"Latent ewald summation for machine learning of long-range interactions,","venue":null,"work_id":"d16c4651-8c61-450e-b515-e74660c490b9","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:54.805513Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:fb0e03c4ed63f0ba3df605b67e012a056ecea8622a07625ee04ed8ec11776822","observation_id":"cda29447-6fdc-411a-b3f5-cffa8d10cb87","resolution":{"observed_at":"2026-08-06T16:14:14.244128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15455","last_updated":"2024-12-19T23:24:44Z","snapshot_observed_at":"2026-08-04T15:50:43.240418Z","submitted_at":"2024-12-19T23:24:44Z","title":"Learning charges and long-range interactions from energies and forces","version":1},"cited_work":{"arxiv_id":"2412.15455","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.15455","snapshot_observed_at":"2026-08-06T16:14:06.257417Z","title":"Learning charges and long-range interactions from energies and forces","venue":"physics.comp-ph","work_id":"42d10968-bd62-4dcb-99c0-cda843f504e2","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:54.965793Z"},"links":{"cited_paper":"/paper/2412.15455","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:c563cd8431e5fee44a61890d6b05d90cf601948ca2a4ff2b2511ab58704a1cd4","observation_id":"f697515d-0c43-4937-ba37-31d0556a8ea6","resolution":{"observed_at":"2026-08-06T16:14:06.337480Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05169","last_updated":"2025-04-07T15:14:07Z","snapshot_observed_at":"2026-07-06T21:05:29.698498Z","submitted_at":"2025-04-07T15:14:07Z","title":"Machine learning interatomic potential can infer electrical response","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05169","snapshot_observed_at":"2026-08-06T16:13:55.116000Z","title":"Machine learning interatomic potential can infer electrical response,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:55.116000Z"},"links":{"cited_paper":"/paper/2504.05169","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:edace467cf22c52d0ee50ba824a6c12f6981d43d6b50d623da2fb140029d0633","observation_id":"65647b1d-91ca-4072-82bc-fd1ced5a15c2","resolution":{"observed_at":"2026-08-06T16:13:55.116000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.146888Z","title":"Generalized neural- network representation of high-dimensional potential- energy surfaces,","venue":null,"work_id":"c04e7cb0-73cc-4cfc-980f-c3220670ac3e","year":2007},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:55.230925Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:5c3581c3ad8593ec2c9cbbb5694172e790a2f2c1b817204a3a038ca8cb3aca8d","observation_id":"48dc191d-78b5-4765-8ec4-c7bb831eaf97","resolution":{"observed_at":"2026-08-06T16:14:14.166096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:14.068487Z","title":"Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons,","venue":null,"work_id":"4f3eb76e-7f81-4d02-95a6-0f4ba818d392","year":2010},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:55.349939Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:260b874e967617f214879815c2bfe646a41773bbe0bb930928f1635238824b25","observation_id":"cb1e335a-1a51-4d4f-9a79-ed93cfc44a50","resolution":{"observed_at":"2026-08-06T16:14:14.104771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:51.133585Z","title":"The FES was computed from reweighting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:51.133585Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:6441c9a1734096f33064f4f4c598711f62d0003d2fde34e4091eb5ae4a20da2d","observation_id":"4c7fe8e0-5322-48e8-82f3-35f56dd3cdf2","resolution":{"observed_at":"2026-08-06T16:13:51.133585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.993259Z","title":"Moment tensor potentials: A class of systematically improvable interatomic potentials,","venue":null,"work_id":"51f2596e-5064-45e1-bd03-3181bdb8a259","year":2016},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:55.461480Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:3f73b4dc65e403d7e6f8016b63e74ce40d40ed13bb7426de06f952396df04d93","observation_id":"62a09745-889c-415f-87e5-b3b36c7e3efc","resolution":{"observed_at":"2026-08-06T16:14:14.033106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.915094Z","title":"Atomic cluster expansion for accurate and transferable interatomic potentials,","venue":null,"work_id":"2d9d48bf-b897-4f3e-b96e-9dd38a537b5f","year":2019},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:55.622355Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:641d13999725d483e83af78e7eecf604b5892b892d06f8aa93ccf9f956a3804e","observation_id":"1a510bd1-177a-4110-8234-0a5c08362282","resolution":{"observed_at":"2026-08-06T16:14:13.950227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.825162Z","title":"E (3)-equivariant graph neural networks for data- efficient and accurate interatomic potentials,","venue":null,"work_id":"7d17bcf2-be44-4fd3-8101-dd71b28a10a3","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:55.752989Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:48d9e85dff86d1871cc59777aca6783721b66d3be18b3a5e48b35b79f5a68217","observation_id":"9d1dfeb6-a06d-4277-a283-503d17a5817a","resolution":{"observed_at":"2026-08-06T16:14:13.867082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.740982Z","title":"Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,","venue":null,"work_id":"62bc79a0-c89a-4381-9dcd-46480ff3fa3b","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:55.955778Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:47fc1ae27d208cd6eef9d0defc8dc367ef9f9349ebe22d6ed3c2a1c48477d0bd","observation_id":"e111455e-c73c-46be-be87-2cd10469af5d","resolution":{"observed_at":"2026-08-06T16:14:13.768224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.668326Z","title":"Cartesian atomic cluster expansion for machine learning interatomic potentials,","venue":null,"work_id":"92060973-2ad0-43fc-8caa-5af6f876aa1d","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:56.121400Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:1c92eb286c8d94da41d636704c3b11bcac1218bf817b277db5b2ac8d19645f03","observation_id":"a9038303-3cd5-4a93-aab0-6d6d68e76800","resolution":{"observed_at":"2026-08-06T16:14:13.696994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.558459Z","title":"The importance of being scalable: Improving the speed and accuracy of neural network interatomic potentials across chemical domains,","venue":null,"work_id":"f4941159-aad6-4a78-98a3-3910cb106d94","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:56.285597Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:2098769c575d6e188349d1c9de17200704c5eeb0b60fbeb0b92fcd9919132a5a","observation_id":"ab361a2b-107c-4e9d-8e33-86172a7b0ab2","resolution":{"observed_at":"2026-08-06T16:14:13.609106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.03837","last_updated":"2025-03-05T19:03:21Z","snapshot_observed_at":"2026-07-06T20:47:28.072925Z","submitted_at":"2025-03-05T19:03:21Z","title":"Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry","version":1},"cited_work":{"arxiv_id":"2503.03837","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.03837","snapshot_observed_at":"2026-08-06T16:14:06.013708Z","title":"Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry","venue":"cond-mat.mtrl-sci","work_id":"0b70dd34-62a0-458a-8e34-93736a3c0383","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:56.428140Z"},"links":{"cited_paper":"/paper/2503.03837","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:db81fbc64fd3e9eacafe78c6c045040aebcd0c7b5d1d9ac10ed21c0bf8e67c27","observation_id":"b1df27e3-e431-40b8-b8a6-14de1c62fbe3","resolution":{"observed_at":"2026-08-06T16:14:06.125181Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12147","last_updated":"2025-04-23T05:37:35Z","snapshot_observed_at":"2026-07-06T20:38:06.085744Z","submitted_at":"2025-02-17T18:57:32Z","title":"Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12147","snapshot_observed_at":"2026-08-06T16:13:56.569049Z","title":"Learning smooth and expressive interatomic potentials for physical property prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:56.569049Z"},"links":{"cited_paper":"/paper/2502.12147","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:152f737f2e84fbae9a662016a179faa66cb1e1e3604307a13e8bba6037bcdccf","observation_id":"785095c3-0cd8-4bed-a906-89159cc9557b","resolution":{"observed_at":"2026-08-06T16:13:56.569049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.454538Z","title":"Smooth, exact rotational symmetrization for deep learning on point clouds,","venue":null,"work_id":"e1ee68fd-a3c5-491f-8297-7ff2383f2b39","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:56.740671Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:8cc70d0ff5a40260383bfb6857f907e530d3c57ec0a21f4fa084e2c22eb4a67d","observation_id":"846abfeb-0cac-4256-a165-8bdd00ba6d48","resolution":{"observed_at":"2026-08-06T16:14:13.495087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.364486Z","title":"Torchmd-net 2.0: Fast neural network potentials for molecular simulations,","venue":null,"work_id":"a6bf7da6-db80-4ae1-b069-efbac874bca9","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:56.910709Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:2d743e503f523cc2f5a218b7b44624d8484825f8d7a962fdf968d40615293a98","observation_id":"a30127b2-02ae-4fe7-9b6c-22b893b28f77","resolution":{"observed_at":"2026-08-06T16:14:13.402956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.278605Z","title":"Newtonnet: A newtonian message passing network for deep learning of interatomic potentials and forces,","venue":null,"work_id":"b2ac3d9b-b9ec-4734-b8ef-b837c2e447f2","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:57.080003Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:91ec77b8c7a398e29ed7840a599074506c1b221c48d2b0e3f1f43f607f6dbd23","observation_id":"7a36ebb3-17cb-4fb1-ab45-1525864e8c45","resolution":{"observed_at":"2026-08-06T16:14:13.317400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.175314Z","title":"Equiformer: Equivariant graph attention transformer for 3d atomistic graphs,","venue":null,"work_id":"60c13f52-fa59-44bd-aa13-67eca9e0c416","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:57.176871Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:d754663f94525bd55ee8d28e3c7611b7e2fd4e5f6448a1753f581e7e86bb153e","observation_id":"df6595fe-8316-4e7a-b369-bb0c174b7527","resolution":{"observed_at":"2026-08-06T16:14:13.230695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:13.088191Z","title":"Spice, a dataset of drug-like molecules and peptides for training machine learning potentials,","venue":null,"work_id":"1165af54-2e17-4ffa-aa42-051cdb80b7eb","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:57.306315Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:3cba617e639c09a371f129b3362c61f2ad3320edbfebd8e15fc5b41b6aaf9b50","observation_id":"785f1e9c-a77b-487a-923c-8ccd91f43a8c","resolution":{"observed_at":"2026-08-06T16:14:13.116565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:12.990637Z","title":"Mace-off: Short- range transferable machine learning force fields for organic molecules,","venue":null,"work_id":"7aead258-760b-4c61-ac8e-e7ddc5e1855b","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:57.438711Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:b3bce3f8542bd3edb52c9b0d49abd30638cd6ab8b193db6384d2d26f54bb681c","observation_id":"e1776b33-d0a0-4acd-886c-a6f163200cac","resolution":{"observed_at":"2026-08-06T16:14:13.037962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:12.889630Z","title":"Deepmd-kit: A deep learning package for many- body potential energy representation and molecular dynamics,","venue":null,"work_id":"7aee26df-4e71-44ec-9dcd-58643601f255","year":2018},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:57.569158Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:e85fc890982487ef21763999eb707111fc13d107d533313e77e0fbe494861106","observation_id":"cd9dd717-36dc-4688-bebb-539a46bd04a1","resolution":{"observed_at":"2026-08-06T16:14:12.934463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:12.764810Z","title":"Schnet: A continuous-filter convolutional neural network for modeling quantum interactions,","venue":null,"work_id":"107c027a-a2bf-4de2-b421-b7c0d50019e4","year":2017},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:57.689950Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:7f9956fa63810bd44cef786ff9b94d978879e899f807c05f2001a9d259be3c9e","observation_id":"bc9f8ebd-6079-4380-818e-bfb06c079288","resolution":{"observed_at":"2026-08-06T16:14:12.819215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:57.866290Z","title":"Dynamical matrices, born effective charges, dielectric permittivity tensors, and interatomic force constants from density-functional perturbation theory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:57.866290Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:e2597d3954bd7614e8aced2e19115c7a433ede645c1dcaae9ecca401a0a55b44","observation_id":"eaa9baad-a7a4-48f2-b2fc-77eeaf4d35e4","resolution":{"observed_at":"2026-08-06T16:13:57.866290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:12.593208Z","title":"A universal graph deep learning interatomic potential for the periodic table,","venue":null,"work_id":"77b9a0d9-053c-46f5-81ff-a9ea883ec43f","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:58.054233Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:a5885166310527588c8216b9d3e861f03c42c94949e8581bc53b5a78e8ae4d30","observation_id":"e68de911-4e4b-4711-b402-97b5dec02136","resolution":{"observed_at":"2026-08-06T16:14:12.670095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:12.425941Z","title":"Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling,","venue":null,"work_id":"58208348-e540-4e8b-b7dd-83fbf9a5a995","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:58.171431Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:968cca5f1099bc686707b16d009464c8b1e2729d2f0d0945f1c13ac486d145e3","observation_id":"a9c89471-7d12-4beb-aceb-ba84e55a29c0","resolution":{"observed_at":"2026-08-06T16:14:12.512219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:12.241023Z","title":"Deconstructing classical water models at interfaces and in bulk,","venue":null,"work_id":"59adfb5f-9e88-4c74-ab5f-f3b01b3537f7","year":2011},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:58.298875Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:8daf2282a391350c3f3859ab9a93c7e10ccefcec579507b84d2dba1cf233dd9c","observation_id":"4c6097a2-29b6-44d9-9c88-11066daa6629","resolution":{"observed_at":"2026-08-06T16:14:12.334764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:12.071842Z","title":"Short solvent model for ion correlations and hydrophobic association,","venue":null,"work_id":"2e931b05-f6d9-4ac2-9b50-47a7a5c777f8","year":2020},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:58.452044Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:bf88eaa59350cd726f02b18ffc0c7f201ebc396fae9fa0f9464233b17f4503de","observation_id":"045104ba-53b0-42fa-811a-5d38073e6677","resolution":{"observed_at":"2026-08-06T16:14:12.164438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:11.942892Z","title":"Derivative learning of tensorial quantities—predicting finite temperature infrared spectra from first principles,","venue":null,"work_id":"ed114617-d937-462d-b4af-4cac282c6d8d","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:58.583503Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:9610cd8f1a32d800ae1ff1b988bc2f48d1ae1008b3562df58bab96b785c3a000","observation_id":"1c827f6c-f6f6-4b8e-b485-53f01d4ce840","resolution":{"observed_at":"2026-08-06T16:14:12.014123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:11.779295Z","title":"Intrinsic backbone preferences are fully present in blocked amino acids,","venue":null,"work_id":"27d1d4da-4b78-4cb7-891a-9e9015a3d685","year":2006},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:58.749656Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:ce849e0d5ac0c3c6a215701876adccee3e246678fdac156f6e5500f2b8ab0b8a","observation_id":"d67226c6-7452-4d70-bdf6-222f0bf3ccc5","resolution":{"observed_at":"2026-08-06T16:14:11.853734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.10538","last_updated":"2025-06-24T13:47:48Z","snapshot_observed_at":"2026-08-02T14:17:09.607500Z","submitted_at":"2025-03-13T16:52:12Z","title":"Foundation Models for Atomistic Simulation of Chemistry and Materials","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.10538","snapshot_observed_at":"2026-08-06T16:13:58.900110Z","title":"Foundation models for atomistic simulation of chemistry and materials,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:58.900110Z"},"links":{"cited_paper":"/paper/2503.10538","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:f0431c2581d594fad169128825156b809110e672a8bfda461f40abdc6ec6750d","observation_id":"cf2432f8-c355-4f46-9bd5-a16400735680","resolution":{"observed_at":"2026-08-06T16:13:58.900110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:11.619743Z","title":"Qmugs, quantum mechanical properties of drug-like molecules,","venue":null,"work_id":"31a89f7a-b1b2-45b0-a3b5-7c9ad70fcc24","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:59.088212Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:dc9edcb5e43488b95d09e976b0d492ac613893cd3cd536b0d5fe4e42e3b3eee6","observation_id":"fd00ea29-62c0-48f5-9653-ea71c92ce548","resolution":{"observed_at":"2026-08-06T16:14:11.724851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:11.397657Z","title":"The structure of water around the compressibility minimum,","venue":null,"work_id":"2948a95c-52ea-440f-8f69-eb784d738d16","year":2014},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:59.206011Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:2e5ddc5e0fe5fd565d55c3e8fe345e03c80f59b7530ffe1d3b77b092e70418d2","observation_id":"28a13026-1d62-4dc8-a65c-575f1c4d5a5c","resolution":{"observed_at":"2026-08-06T16:14:11.497162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:11.251312Z","title":null,"venue":null,"work_id":"c08e85ca-9b1b-45c8-9507-7548481bf079","year":1996},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:59.329670Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:79d65af78dff880415992e4e53d1b07615fefad0a8d778e7119537d8c27ba30f","observation_id":"b35426d9-ff32-4aa9-a1bf-06a7e32327b8","resolution":{"observed_at":"2026-08-06T16:14:11.334262Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:11.033594Z","title":"Quantum dynamics and spectroscopy of ab initio liquid water: The interplay of nuclear and electronic quantum effects,","venue":null,"work_id":"fffcce68-5330-4435-9170-abea14ab4816","year":2017},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:59.487879Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:5c1fce4be22cb4b84eac1e74c1fc3b8c8cd0c937c0fd810c3fceee9916a2ac27","observation_id":"a6d1e179-469f-4312-a271-e6c5e636c677","resolution":{"observed_at":"2026-08-06T16:14:11.122941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:10.851730Z","title":"Quantum dynamics using path integral coarse-graining,","venue":null,"work_id":"85c6502c-5134-4377-9c5b-880b9c1598d3","year":2022},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:59.614259Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:f1cfddd3b72894e661b3c6b861d4ce7b263c6baf964e3b0cc1e0367d8ede4114","observation_id":"d0976479-7676-4228-8868-02488f45f859","resolution":{"observed_at":"2026-08-06T16:14:10.923369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:10.777193Z","title":null,"venue":null,"work_id":"1757d2e5-0ee6-4002-baf9-69e81391bbb8","year":2016},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:59.729732Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:a152fe766d187a06cb8b07cedf4162250c8617cc7c5fd52c0b050d4277a8eb04","observation_id":"e8d0b3eb-b2f2-4cc9-9767-bba3365ebad0","resolution":{"observed_at":"2026-08-06T16:14:10.806264Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:10.708093Z","title":"Nist chemistry webbook, nist standard reference database number 69,","venue":null,"work_id":"a6688c5a-060e-4a19-b2ed-24f50680cc06","year":1998},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:59.897050Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:b235f1de4591982bc41c8a236c4448c8ebe20ecbe60a0b1e81b0868c312e6a11","observation_id":"134a4355-9f8a-4224-850c-e2729d39e696","resolution":{"observed_at":"2026-08-06T16:14:10.741172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:10.552075Z","title":"Machine learning force fields for molecular liquids: Ethylene carbonate/ethyl methyl carbonate binary solvent,","venue":null,"work_id":"3f29e9b9-6b82-4108-9541-60c4a0c2f427","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:00.019306Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:f07ab74e1f90fda8aff578015b2e5acaf45ccd48e64b2ef239ea943adae31266","observation_id":"a231bbba-a278-4c8e-8707-1237a600b8df","resolution":{"observed_at":"2026-08-06T16:14:10.661493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:10.393318Z","title":"De Novo Protein Design: Fully Automated Sequence Selection,","venue":null,"work_id":"1903bccc-1c21-44b5-8e8f-7aa65cbaa58f","year":1997},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:00.224615Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:5e1543246fa86a5dced8927279cd28dce6b3261da148c707ba7c0882884c6bc4","observation_id":"30ba51f1-4771-46df-b083-2f1d2a5b8c44","resolution":{"observed_at":"2026-08-06T16:14:10.462772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:10.198345Z","title":"Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method,","venue":null,"work_id":"3350844f-133b-4162-a394-f6294da23cd5","year":2008},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:00.356543Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:441e3414a47b17160ba9f632bd6b3988c814689dc3dd07ab88fed832266d2eef","observation_id":"5f55364f-6e3e-43bf-88b2-4b08d25319c5","resolution":{"observed_at":"2026-08-06T16:14:10.307015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:09.921612Z","title":"Do Molecular Dynamics Force Fields Capture Conformational Dynamics of Alanine in Water?","venue":null,"work_id":"0c694600-eb03-4ae6-8f83-e4940d56637f","year":2020},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:00.473299Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:474d85557fac3ce943cf194d25d2ccf01a93fe04b7d7eb64b0d7f67b7373ca16","observation_id":"feddb8c1-a14e-4e0f-b98d-b2501b0d7b47","resolution":{"observed_at":"2026-08-06T16:14:10.031582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:09.705287Z","title":"Solvation effects on alanine dipeptide: A MP2/cc-pVTZ//MP2/6-31G** study of ( ϕ, ψ) energy maps and conformers in the gas phase, ether, and water,","venue":null,"work_id":"af0386c0-ae56-4dc3-8193-a6a0c0c21f9f","year":2004},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:00.592789Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:6ce33ea3b3a2f9ed34dd3b420c405dc4f2945e64a4b6b75024258319364d51e8","observation_id":"14dd417e-3814-4aab-8196-cb634d83de3f","resolution":{"observed_at":"2026-08-06T16:14:09.797066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:09.584456Z","title":"Improved side-chain torsion potentials for the Amber ff99SB protein force field,","venue":null,"work_id":"214b8e24-dd5a-4631-b722-eb07add58a41","year":2010},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:00.745941Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:c93ff27280183cf5ef9e68eea1cd049b5a5da5613497449fd26ae232d3cfa2a9","observation_id":"56821cbc-7821-48a4-82a6-72cd4cfad5cf","resolution":{"observed_at":"2026-08-06T16:14:09.641497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:09.398799Z","title":"Commentary: The materials project: A materials genome approach to accelerating materials innovation,","venue":null,"work_id":"da095775-0fac-4714-a8c0-badcb2db49ad","year":2013},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:00.903782Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:4198c28ce573bd9815831bed941d6ffcd06c05969613fee0d8a463ac174f877a","observation_id":"8f5c9d73-84bc-4bdc-8fa0-cbde3c89ee0d","resolution":{"observed_at":"2026-08-06T16:14:09.490935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:01.032015Z","title":"The open molecules 2025 (omol25) dataset, evaluations, and models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:01.032015Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:d01f8d8946cff266ac83289f29e208afce1c1da60424f594d1cad26096fa9f37","observation_id":"956b794b-9e18-4e9c-be50-599c2a096149","resolution":{"observed_at":"2026-08-06T16:14:01.032015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:09.252184Z","title":"The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts,","venue":null,"work_id":"3c03c818-666b-4395-bee0-36342239ce22","year":2023},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:01.166432Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:a0baea6d8d83d9dbfdc84e4074fc3d5db0521389769ef48df7668128ca55fc54","observation_id":"8b498455-b29e-4d6e-b459-841220afbd7a","resolution":{"observed_at":"2026-08-06T16:14:09.306398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12771","last_updated":"2026-05-20T00:46:02Z","snapshot_observed_at":"2026-08-06T01:59:40.411635Z","submitted_at":"2024-10-16T17:48:34Z","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12771","snapshot_observed_at":"2026-08-06T16:14:01.332453Z","title":"Open materials 2024 (omat24) inorganic materials dataset and models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:01.332453Z"},"links":{"cited_paper":"/paper/2410.12771","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:4e6a5af488179178f2f9176ac48a7f9fb6b0c3264fa309903410f4df40e28003","observation_id":"5eeffbe3-ec4e-42a7-a049-998a46eee0d3","resolution":{"observed_at":"2026-08-06T16:14:01.332453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.06462","last_updated":"2025-08-01T20:39:57Z","snapshot_observed_at":"2026-07-31T17:19:17.143245Z","submitted_at":"2025-05-09T23:06:55Z","title":"Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties","version":2},"cited_work":{"arxiv_id":"2505.06462","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.06462","snapshot_observed_at":"2026-08-06T16:14:05.677455Z","title":"Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties","venue":"physics.chem-ph","work_id":"91f2e5c8-5e9b-401a-9cca-041f607d1308","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:01.518977Z"},"links":{"cited_paper":"/paper/2505.06462","citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:73748b474bec07d6699e56bee1974b785ed68ec5d918cd3ca53112aa1e694375","observation_id":"9b885076-f71b-45d1-b851-4a2481866e40","resolution":{"observed_at":"2026-08-06T16:14:05.767093Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:09.061861Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"25aab13a-1d48-4ea9-bf84-2e1737f7a421","year":2019},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:01.809341Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:c527b4d17f9d5afffa747babfdb641548f7526f61c32f3b384d77b4866f76d4c","observation_id":"4d709984-f964-49a2-a76b-f8ba8f03a918","resolution":{"observed_at":"2026-08-06T16:14:09.175982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"records/1097522","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:05.424062Z","title":"Spice 2.0.1,","venue":null,"work_id":"95d65bf7-678a-420a-b185-6fce956d7206","year":2024},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:01.948856Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:423d4af82fa7bbcb053d616b33763822c36808aca2bf2af95812e66571dfed0b","observation_id":"5f765cb2-cca4-422f-8f07-b522bf6942aa","resolution":{"observed_at":"2026-08-06T16:14:05.547397Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:08.871172Z","title":"Pyscf: the python-based simulations of chemistry framework,","venue":null,"work_id":"281d005c-17e5-4942-b5c4-7d420d1bb671","year":2018},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:02.112029Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:50bb0f7ab29f27aa3dbf0c575e45c5f95ff17073131e58880c74cfd5b10ef08d","observation_id":"3137ee04-ac77-4bdc-86d4-0d367d903874","resolution":{"observed_at":"2026-08-06T16:14:08.960329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:08.673443Z","title":"The atomic simulation environment—a Python library for working with atoms,","venue":null,"work_id":"133ce862-adcb-4238-9298-3418cad02f14","year":2017},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:02.246417Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:afe3f18a952f535f9361150e110cc20e9756a47f007891bce898268863e50e20","observation_id":"94c991a9-8995-4ebc-8cc3-cc7b07ceb3f2","resolution":{"observed_at":"2026-08-06T16:14:08.741095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:08.472201Z","title":"Pubchem 2025 update,","venue":null,"work_id":"2e21dc12-e994-4b0f-922d-16711b6ad283","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:02.355296Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:1a4cce7dcfa9fe2d43d6cd7460dba532e4169881467c5f94aa270ee383a2d85b","observation_id":"ac4ae995-0db4-47dd-b275-cd2ee9c8c372","resolution":{"observed_at":"2026-08-06T16:14:08.566311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"records/1528601","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:05.119768Z","title":"rdkit/rdkit: 2025 03 2 (q1 2025) release,","venue":null,"work_id":"e6961aec-a8f7-44c2-a78b-0f9f8bb50458","year":2025},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:02.472026Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:1da853debdae6364c9b5cbbf065337c3c4218c70f7dbea9f23da400dbe52b59c","observation_id":"086f1c7d-929b-4804-9814-8ac8f6db3a73","resolution":{"observed_at":"2026-08-06T16:14:05.208858Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:08.309873Z","title":"Gromacs: A message-passing parallel molecular dynamics implementation,","venue":null,"work_id":"8ccb0e48-5862-41ce-9be8-e58701324704","year":1995},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:03.199687Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:447d0b0c87c2bc80a988f1e10313b4191cd640d276de44b48b2fd87f7843bfe5","observation_id":"78339b09-17eb-4011-8e9b-710cc08e02e6","resolution":{"observed_at":"2026-08-06T16:14:08.375574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:08.111868Z","title":"Automatic atom type and bond type perception in molecular mechanical calculations,","venue":null,"work_id":"012fc2e3-1ce0-4b98-8945-8d62ba4b84b9","year":2006},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:03.985172Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:77ff00d83ae0499186c90054694d5a92b9a9a185e9cbede4779d93eebf32d8da","observation_id":"3058501e-57ea-400e-84d4-806cb62c6754","resolution":{"observed_at":"2026-08-06T16:14:08.189962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:07.903649Z","title":"Construction of higher order symplectic integrators,","venue":null,"work_id":"749622df-1b17-460f-ab01-f3c7869c8678","year":1990},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:04.139902Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:da8496a3697db810fc41fdea2aeda3d7f31754b6ae5a41755f6f6dccf5cace3b","observation_id":"5202c426-cba7-4f08-ab16-bf77ce4a1c64","resolution":{"observed_at":"2026-08-06T16:14:08.019690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:07.753353Z","title":"Comparison of simple potential functions for simulating liquid water,","venue":null,"work_id":"9053ab1a-6c2a-4823-a51b-397e2929dcbd","year":1983},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:04.285730Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:1df053fe57276607a6cdc73a35d814010ff0e7878988915bf7039678ec091792","observation_id":"dd1cc0d3-5fb4-4d67-a5ff-16cd07f20180","resolution":{"observed_at":"2026-08-06T16:14:07.821070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:07.600768Z","title":"Promoting transparency and reproducibility in enhanced molecular simulations,","venue":null,"work_id":"b8acaafc-3cd8-43a6-9723-92228a319c0f","year":2019},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:04.414805Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:5184678351cc0321d34ab8d6af56285d1ce577bfdfc1a89d2ef352ff76dae039","observation_id":"f86e9493-35d0-4d05-90bd-e7a780ab0a99","resolution":{"observed_at":"2026-08-06T16:14:07.651607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:07.488426Z","title":"PLUMED 2: New feathers for an old bird,","venue":null,"work_id":"a34077f2-66de-434b-8b82-8a2fba621048","year":2014},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:04.546026Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:8120d207eb93ed5919555851b790138e8a4d0ef5fb3aea52f5214d27b52cff9e","observation_id":"c3984368-3248-47ea-ab32-fa737a790b74","resolution":{"observed_at":"2026-08-06T16:14:07.582384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:07.258966Z","title":"Metadynamics with adaptive gaussians,","venue":null,"work_id":"1c7fed7f-9583-4592-98a8-b4870cc20d83","year":2012},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:04.750017Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:e2ffe420708cc7450ad08b81f95f1d85177c633e40f2a09b1f10d7103dc84e16","observation_id":"2881fa03-8ccf-4dbf-940e-9557ac14136c","resolution":{"observed_at":"2026-08-06T16:14:07.377620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:14:07.005907Z","title":"Structural relaxation made simple,","venue":null,"work_id":"8145c6d2-549c-43fd-97f1-2870d48f8ae2","year":2006},"citing_paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T16:14:04.857514Z"},"links":{"citing_paper":"/paper/2507.14302"},"observation_digest":"sha256:a908f0d5003dcf6b2e9615de56160b54f46365271ff22b8b52f00c51a9bce132","observation_id":"7297083c-3115-40dd-be84-7a1075d0d711","resolution":{"observed_at":"2026-08-06T16:14:07.143191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.14302","last_updated":"2025-07-18T18:21:45Z","latest_version":1,"primary_category":"physics.chem-ph","snapshot_observed_at":"2026-08-06T15:57:13.955348Z","submitted_at":"2025-07-18T18:21:45Z","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials"},"reference_resolution":{"displayed":86,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":8,"verified_fuzzy":64},"total_outbound_references":86},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 86 of 86 outbound references and 4 inbound Pith citation observations for arXiv:2507.14302."}