{"as_of":"2026-08-05T14:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8b2cb2a54fb45a873b9ea8534f34e99c151964a6c167f2258b98982074306f65","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T11:21:47.658173Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T10:56:35.079982Z","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":"2026-07-04T08:49:42.334844Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"cited_work":{"arxiv_id":"2601.06820","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.06820","snapshot_observed_at":"2026-07-10T01:18:57.261370Z","title":"Bgolearn: a Uniﬁed Bayesian Optimization Framework for Accelerating Materials Discovery","venue":null,"work_id":"186bd267-8d4d-47f7-b90f-f8cedd0959b2","year":2026},"citing_paper":{"arxiv_id":"2604.01328","last_updated":"2026-04-07T15:06:34Z","snapshot_observed_at":"2026-07-31T12:51:11.198900Z","submitted_at":"2026-04-01T19:14:34Z","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-13T22:06:48.152555Z"},"links":{"cited_paper":"/paper/2601.06820","citing_paper":"/paper/2604.01328"},"observation_digest":"sha256:7370cca1ccf865cff273ced429737ccfc49b654a13af9021a769be469b933793","observation_id":"db8f16ab-4910-484d-aaed-4071e229af09","resolution":{"observed_at":"2026-07-10T01:18:57.261370Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"cited_work":{"arxiv_id":"2601.06820","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.06820","snapshot_observed_at":"2026-07-10T01:18:57.261370Z","title":"Bgolearn: a Uniﬁed Bayesian Optimization Framework for Accelerating Materials Discovery","venue":null,"work_id":"186bd267-8d4d-47f7-b90f-f8cedd0959b2","year":2026},"citing_paper":{"arxiv_id":"2606.22425","last_updated":"2026-06-21T10:24:27Z","snapshot_observed_at":"2026-08-05T01:45:17.050239Z","submitted_at":"2026-06-21T10:24:27Z","title":"SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T10:56:35.079982Z"},"links":{"cited_paper":"/paper/2601.06820","citing_paper":"/paper/2606.22425"},"observation_digest":"sha256:112ff973904489a1c5a83221c7f1d56a20421c78e1ebed643c785c86a9fb94ad","observation_id":"0ebf69f1-7b22-4384-baf8-74febcf9c2dd","resolution":{"observed_at":"2026-07-10T01:18:57.261370Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2601.06820/citation-record","integrity":"/paper/2601.06820/integrity","json":"/paper/2601.06820/citation-record.json","paper":"/paper/2601.06820"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:21:47.100709Z","title":"Springer, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.100709Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:94fd917004fc18e5f8b178e0434cfc4594450ed5d26eedf66924e1fdffb8add5","observation_id":"da8ca321-565c-4d61-a161-5e812202c2e8","resolution":{"observed_at":"2026-08-03T11:21:47.100709Z","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":"2026-08-03T11:21:47.107664Z","title":"Active learn- ing in materials science with emphasis on adaptive sampling using uncertainties for targeted design.npj Computational Materials, 5(1):21, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.107664Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:ff96c3a4777b7b770be68e6ca58374454b7f07a0e9a01c0c6f9b6f3baa5377da","observation_id":"d7d56381-1d08-4e05-9248-1c91efe913ed","resolution":{"observed_at":"2026-08-03T11:21:47.107664Z","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":"2026-08-03T11:21:47.118162Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.118162Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:16594b66c9092ba47f3ed904e9e638bcda28f0ec527973872cf91ff64118c62a","observation_id":"f65f09e2-2dea-4338-93ee-3f4219964135","resolution":{"observed_at":"2026-08-03T11:21:47.118162Z","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":"2026-08-03T11:21:47.132584Z","title":"Bayesian optimization algorithms for accelerator physics.Physical review accelerators and beams, 27(8):084801, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.132584Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:101b91979fd329ecb15c659063a04cfa53dfdee1d68ba84c4b0eb819989d03a6","observation_id":"a9194ed6-ba50-4c51-b0c1-9eef243badf0","resolution":{"observed_at":"2026-08-03T11:21:47.132584Z","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":"2026-08-03T11:21:47.141218Z","title":"Sequential closed-loop bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery.Nature Chem- istry, 16(8):1286–1294, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.141218Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:55a498b827070f833a7bf8d6d12a0a109ca22e0c501378cbfbc0a442d5465b4c","observation_id":"3965c1da-23b2-4343-a075-ac245dc74b48","resolution":{"observed_at":"2026-08-03T11:21:47.141218Z","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":"2026-08-03T11:21:47.146949Z","title":"Best practices for multi- fidelity bayesian optimization in materials and molecular research.Nature Computational Science, pages 1–10, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.146949Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:23b70e2432e69af31a11204784ac085997f6a3b2dcf32fe0bfda0dca3060db11","observation_id":"517a439b-7652-4bca-a820-c62ce2af058d","resolution":{"observed_at":"2026-08-03T11:21:47.146949Z","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":"2026-08-03T11:21:47.155663Z","title":"Benchmarking the perfor- mance of bayesian optimization across multiple experimental materials science domains.npj Computational Materials, 7(1):188, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.155663Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:61b99e82f900c89a99acc9c0bc2c2e281a9372b76397cecfbc230d0f03607393","observation_id":"5330b632-a51a-4959-bc15-1a53bd178bc2","resolution":{"observed_at":"2026-08-03T11:21:47.155663Z","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":"2026-08-03T11:21:47.169846Z","title":"Active learning-guided accelerated discovery of ultra-efficient high-entropy thermoelectrics.Advanced Materials, page e15054, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.169846Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:2caa3d8b3ee1882f4bfb683bcc278b2f6621e58b7a6ec4c8c5961d8bfd0fcc4f","observation_id":"2fff765b-7d58-430e-9f17-0dde1e84f161","resolution":{"observed_at":"2026-08-03T11:21:47.169846Z","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":"2026-08-03T11:21:47.181567Z","title":"Materials design with target-oriented bayesian optimization.npj Computational Materials, 11(1):209, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.181567Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:93f734d8b9f137d46ae68f52a12ab4570ff357ab05c6af66ea44c8dc306b1885","observation_id":"ecddac90-f142-436c-8e5f-66af3a285e23","resolution":{"observed_at":"2026-08-03T11:21:47.181567Z","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":"2026-08-03T11:21:47.190501Z","title":"A survey and benchmark of high-dimensional bayesian optimization of discrete sequences.Advances in Neural Information Processing Systems, 37:140478–140508, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.190501Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:1a29a272e9bcd0daac25ad958fb5157b97d1a7f3c7b96644262326ce02afbc14","observation_id":"3c66c805-3988-496a-8ea6-3fcfcf5c77b5","resolution":{"observed_at":"2026-08-03T11:21:47.190501Z","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":"2026-08-03T11:21:47.200673Z","title":"Bias free multiobjective active learning for materials design and discovery.Nature communications, 12(1):2312, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.200673Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:d94a1e68da59f421c6c3c6cf81ff2c5e8ba5b60842761d1381a4b71e74e9763c","observation_id":"9cf8cf0c-c6ae-4db7-a1bb-4873a6bc1e15","resolution":{"observed_at":"2026-08-03T11:21:47.200673Z","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":"2026-08-03T11:21:47.208034Z","title":"Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via bayesian machine learning.Nature Communications, 16(1):6528, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.208034Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:a70c020e3a6d83bc68476baa27ca23453b88a347f5ca8922df787c9638c95ed9","observation_id":"9a09f501-5868-476d-a972-3301e3a00792","resolution":{"observed_at":"2026-08-03T11:21:47.208034Z","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":"2026-08-03T11:21:47.213366Z","title":"Bayesian reaction optimization as a tool for chemical synthesis.Nature, 590(7844):89–96, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.213366Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:4eb2b8d8bf8c8c6c64760af9c5b08a557051a303cd43d6eb7b554fe9417e1b25","observation_id":"7d572737-81a7-483c-ac2b-798f9069ffc6","resolution":{"observed_at":"2026-08-03T11:21:47.213366Z","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":"2026-08-03T11:21:47.223260Z","title":"Increasing certainty in systems biology models using bayesian multimodel inference.Nature Communi- cations, 16(1):7416, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.223260Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:dcd8ce7bdd184bdeab6c95458231e5d63d15f987bf5ecfc4b66b06208173179a","observation_id":"2dfa466c-54d9-4f34-b1e1-4cf13afbd6fb","resolution":{"observed_at":"2026-08-03T11:21:47.223260Z","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":"2026-08-03T11:21:47.234110Z","title":"Mlmd: a programming-free ai platform to predict and design materials.npj Computational Materials, 10(1):59, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.234110Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:fb64a13ed5b430d325a0e4094ffa7a6d009972c943c9cc3e837580eb3c7c6bb8","observation_id":"6f934e15-0773-471c-a4e9-2c1144972203","resolution":{"observed_at":"2026-08-03T11:21:47.234110Z","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":"2026-08-03T11:21:47.246664Z","title":"Efficient hyperparameter tuning for predicting student performance with bayesian optimization.Multimedia tools and applications, 83(17):52711–52735, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.246664Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:524a4fe1f9a5d7ef005d93f6b6ac86c5f06871e90cd9fce15d1bc59c5027158c","observation_id":"ef082ffd-c8f9-4ff5-9895-ad457d4d91b9","resolution":{"observed_at":"2026-08-03T11:21:47.246664Z","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":"2026-08-03T11:21:47.255515Z","title":"A bayesian active learning platform for scalable combination drug screens.Nature Communications, 16(1):156, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.255515Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:43fd8aad948764aeb9281414ec014b66f0a0da75b4180c31289f098c74d817a7","observation_id":"17c16570-1f29-4778-84c2-3a60c564207f","resolution":{"observed_at":"2026-08-03T11:21:47.255515Z","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":"2026-08-03T11:21:47.261290Z","title":"Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys.Materials & Design, 241:112921, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.261290Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:248e5781540b5524ff2293a8af5e2faa2b4dadab194633d397e015cfb94ee81d","observation_id":"b57ce355-1626-4bb8-a127-f44aa0c3bf5e","resolution":{"observed_at":"2026-08-03T11:21:47.261290Z","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":"2026-08-03T11:21:47.266807Z","title":"Botorch: A framework for efficient monte-carlo bayesian opti- mization.Advances in neural information processing systems, 33:21524–21538, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.266807Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:8e07c43ac935dd8267e83dd68da8547d0f1feb91d078799bd1bebd0160ddeff4","observation_id":"491089b2-2ed4-40e6-91b1-b230b003db9f","resolution":{"observed_at":"2026-08-03T11:21:47.266807Z","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":"2026-08-03T11:21:47.275229Z","title":"Ae: A domain-agnostic platform for adaptive experimentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.275229Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:57e6d970485c7493d2f2f6e64d5c161fcf241f647eb97aebc95d05c687245a29","observation_id":"2534e96e-3029-462a-9d2e-910d491877a2","resolution":{"observed_at":"2026-08-03T11:21:47.275229Z","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":"2026-08-03T11:21:47.285823Z","title":"GPyOpt: A bayesian optimization framework in python.http: //github.com/SheffieldML/GPyOpt, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.285823Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:2d80dd8bf050737488f80b90b7525b7a45a4408bbeb4b499139c803176c3987c","observation_id":"7e511e20-2ad6-4a8d-b77b-a53b713793e3","resolution":{"observed_at":"2026-08-03T11:21:47.285823Z","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":"2026-08-03T11:21:47.291874Z","title":"A tutorial on multiobjective optimization: fun- damentals and evolutionary methods.Natural computing, 17(3):585–609, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.291874Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:def00fdd959096514bcdf9a0ad48cf9cb883a1b73c3b73298bd26048f0e45978","observation_id":"48638ca4-e29c-44f0-9675-50862ead9490","resolution":{"observed_at":"2026-08-03T11:21:47.291874Z","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":"2026-08-03T11:21:47.299123Z","title":"Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement.Advances in neural information processing systems, 34:2187–2200, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.299123Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:378990b694076f88954aa05a942bf2f69db0222827ada8e2383be5e5d301c915","observation_id":"a3d1ef62-3e39-480f-872d-7a4181e4eb85","resolution":{"observed_at":"2026-08-03T11:21:47.299123Z","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":"2026-08-03T11:21:47.304674Z","title":"Accessed: 2025-10-30","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.304674Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:5f95845e90a37a4ec18389d7616536177204bfe0cfec24c3ef5c1de60934c49a","observation_id":"07d205ad-5dcb-476b-8c0f-6b3873bfe02d","resolution":{"observed_at":"2026-08-03T11:21:47.304674Z","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":"2026-08-03T11:21:47.314245Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.314245Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:95e0c6e2f3a1311b72bc8eda113399844f75dd2433192a31565e0c32e036ba1d","observation_id":"d67e02d5-3fa1-4c0e-a3e0-c2061fcc5f86","resolution":{"observed_at":"2026-08-03T11:21:47.314245Z","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":"2026-08-03T11:21:47.324423Z","title":"Machine learning-engineered nanozyme system for synergistic anti-tumor ferroptosis/apoptosis therapy.Small, 21(5):2408750, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.324423Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:42db8bd3f3f29b790a4361e28d81b2cb3b77f5a718caa1d94b4168fdc67dc28c","observation_id":"e7eb780e-a041-45e9-b090-94fcbc3778e3","resolution":{"observed_at":"2026-08-03T11:21:47.324423Z","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":"2026-08-03T11:21:47.331075Z","title":"Accelerated design of age-hardened mg-ca-zn alloys with enhanced mechanical properties via machine learning.Computational Materials Science, 249:113665, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.331075Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:6450797b4bd4576c156f8592ffe86ee64f64ef72ff3081bfe00a61ebe9d04079","observation_id":"2d0e6a53-03ff-46e7-b186-34f1b6ea0773","resolution":{"observed_at":"2026-08-03T11:21:47.331075Z","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":"2026-08-03T11:21:47.340988Z","title":"Bayesian active learning for accelerated design of broadband polarization- insensitive metasurfaces.Intelligent Computing, 4:0135, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.340988Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:93b96e8addb781678a654577aaa4f7dac8230a41ba0dbc9ae7ae825bb4ab8ee0","observation_id":"b5ef3244-d264-4de8-8ba2-b8a68a2e9df1","resolution":{"observed_at":"2026-08-03T11:21:47.340988Z","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":"2026-08-03T11:21:47.352658Z","title":"Active learning-based research of foaming agent for epb shield soil conditioning in gravel stratum.Measurement, 239:115509, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.352658Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:6d4a80ff7ed4228103f22bdad0b79c34c90542e0b09284aa64e6a8cc9f99c4ee","observation_id":"2cc1da0b-02fc-48bd-817b-a95ecca59acf","resolution":{"observed_at":"2026-08-03T11:21:47.352658Z","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":"2026-08-03T11:21:47.364244Z","title":"Spatial-adaptive active learning identifies ultra-durable and highly active catalysts for acidic oxygen evolution reaction.Science Bulletin, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.364244Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:b720be416d1c1f1b56da36528ecb0f1e7ccde56deab6b24615387570a805ee59","observation_id":"6b846b1f-2712-461d-aa7b-b986d6e64a43","resolution":{"observed_at":"2026-08-03T11:21:47.364244Z","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":"2026-08-03T11:21:47.369161Z","title":"Self-driving laboratory for accelerated on-surface synthesis under ultrahigh vacuum.Nano Letters, 25(30):11609–11617, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.369161Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:f172907f1bd018aa90ed986b40dd24eef83631156901cd82d8366b17326b0205","observation_id":"83eb3b7c-d39b-4b74-8a1e-ddaf837e7e2f","resolution":{"observed_at":"2026-08-03T11:21:47.369161Z","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":"2026-08-03T11:21:47.380795Z","title":"Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.380795Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:fb065a84dcc36ea7244ca35df459e379fd05d6c3fd8a253d2c29f4b90a969529","observation_id":"330787df-fe3b-4853-9115-69a6f41d3a07","resolution":{"observed_at":"2026-08-03T11:21:47.380795Z","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":"2026-08-03T11:21:47.389686Z","title":"The global optimization problem: an introduction.Towards Global Optimiation 2, pages 1–15, 1978","venue":null,"work_id":null,"year":1978},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.389686Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:5052f5ec803993ed44adb52b08e8a19d4a4bbe0ac78b06fcea936f9006769403","observation_id":"857e76ac-fe1f-466b-960c-24467de8c35f","resolution":{"observed_at":"2026-08-03T11:21:47.389686Z","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":"2026-08-03T11:21:47.394034Z","title":"Springer science & business media, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.394034Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:7e0e02d24d655c723126b281c020f756ef2874a86a3c325c085ba733db636bf0","observation_id":"079a18b7-8106-4e76-8aa6-ce656168fbe3","resolution":{"observed_at":"2026-08-03T11:21:47.394034Z","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":"2026-08-03T11:21:47.400491Z","title":"Comparison of multiobjective evolution- ary algorithms: Empirical results.Evolutionary computation, 8(2):173–195, 2000","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.400491Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:9b0d0820d75ed5c58fd2493439d3b82349739427a166bcd409dc3e288c046ebb","observation_id":"e18ab4df-4747-42a7-8a16-3af7274b5222","resolution":{"observed_at":"2026-08-03T11:21:47.400491Z","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":"2026-08-03T11:21:47.405481Z","title":"Scalable test problems for evolutionary multiobjective optimization","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.405481Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:46b2cf675cc80134171407304a239b1f90ac294512659076b30601d028319912","observation_id":"fb176c11-a4bc-484c-b7df-68af0cf7849b","resolution":{"observed_at":"2026-08-03T11:21:47.405481Z","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":"2026-08-03T11:21:47.420349Z","title":"A fast elitist non- dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.420349Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:ebbe4b6e5816db30dd1403c72f135b63b7b2cf12763115bb065de16c3d8b99e6","observation_id":"5bb92f9e-d57d-4671-b858-c7b3fab4ffd9","resolution":{"observed_at":"2026-08-03T11:21:47.420349Z","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":"2026-08-03T11:21:47.429065Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.429065Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:1228a531e7ab35420f08e62f36715e3b0dee24dc97f606bee928725f58514709","observation_id":"6289a79d-47b7-4569-8269-279496a98588","resolution":{"observed_at":"2026-08-03T11:21:47.429065Z","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":"2026-08-03T11:21:47.435906Z","title":"How atomic bonding plays the hardness behavior in the al–co–cr–cu–fe–ni high entropy family.Small Science, 4(2):2300225, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.435906Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:49685353ff535492349cefd2c4dc7b89bc5aea2d46dfa51670a1b94f1ed39021","observation_id":"ea3ef2e5-bdeb-4651-ab89-e3138df6ec80","resolution":{"observed_at":"2026-08-03T11:21:47.435906Z","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":"2026-08-03T11:21:47.440898Z","title":"Exploring high entropy alloys: a review on thermodynamic design and computational modeling strategies for advanced materials ap- plications.Heliyon, 10(22), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.440898Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:fa359eb51c7bb9e293b1c6ebedb765eaf9158a85885b61892feca1dfdc836982","observation_id":"db8a2d73-9672-4ab9-b1c7-df70fc0f2dd5","resolution":{"observed_at":"2026-08-03T11:21:47.440898Z","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":"2026-08-03T11:21:47.447144Z","title":"Review on wear resistance of laser cladding high- entropy alloy coatings.Journal of Materials Research and Technology, 28:911–934, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.447144Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:9549b6990ed5cc2e759849f799c9df54321aed15ef62a01892bce7ce577ca20f","observation_id":"d2879f2b-7e84-4a10-a6e2-aa5fe9ace5eb","resolution":{"observed_at":"2026-08-03T11:21:47.447144Z","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":"2026-08-03T11:21:47.452749Z","title":"Hot-rolled al-added medium mn steel (fe-8mn-2.85 al-1si-0.2 c): Microstructural evolution and tensile behavior.Materialia, 29:101790, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.452749Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:3cbdba4a26b7e71acbf1bd70814e17129794b5594a2284a59d312533afca1533","observation_id":"8bb9f3d6-69ef-442f-9434-0ae24c229d6e","resolution":{"observed_at":"2026-08-03T11:21:47.452749Z","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":"2026-08-03T11:21:47.460469Z","title":"Stabilizing austenite via intercritical mn partitioning in a medium mn steel.Scripta Materialia, 225:115162, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.460469Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:d745e7c773c577a800c3b46b1da7d87d09e3ef026551c0beceb5e0d4e4d37185","observation_id":"f71329f9-9272-4d56-a139-26ddbb5487b3","resolution":{"observed_at":"2026-08-03T11:21:47.460469Z","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":"2026-08-03T11:21:47.467683Z","title":"High dislocation density–induced large ductility in deformed and partitioned steels.Science, 357(6355):1029– 1032, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.467683Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:3e37dc17f36c7c0cc61932487f86858d765f268f6a436d5168d934ac10be30a7","observation_id":"5e653eb7-88e6-463d-a72a-67b61c5b7547","resolution":{"observed_at":"2026-08-03T11:21:47.467683Z","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":"2026-08-03T11:21:47.476423Z","title":"Target- driven design of high strength yet corrosion resistant medium mn steel via interpretable machine-learning.Materials & Design, page 115217, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.476423Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:e5a1030f02a1d0e379693c4b3da071135c0f64f6332d8e8722a94a5f78e08689","observation_id":"c2d6e86f-bcda-41fa-9330-e7214128a93a","resolution":{"observed_at":"2026-08-03T11:21:47.476423Z","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":"2026-08-03T11:21:47.483682Z","title":"Materials informatics: Emergence to au- tonomous discovery in the age of ai.arXiv preprint arXiv:2601.00742, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.483682Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:8fc6e23ad809fa973701d527d7723e3a6702be0e362e221dd76306c635aad2a3","observation_id":"068d365d-859e-42ae-a5e7-5220440aa3c6","resolution":{"observed_at":"2026-08-03T11:21:47.483682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0912.3995","last_updated":"2010-06-09T23:24:13Z","snapshot_observed_at":"2026-07-06T02:01:58.227489Z","submitted_at":"2009-12-21T00:08:19Z","title":"Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0912.3995","snapshot_observed_at":"2026-08-03T11:21:47.494449Z","title":"Gaussian pro- cess optimization in the bandit setting: No regret and experimental design.arXiv preprint arXiv:0912.3995, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.494449Z"},"links":{"cited_paper":"/paper/0912.3995","citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:b4c5abee25da456eb755510266441401c1cb84570443d2bad8e5873c62429b3b","observation_id":"13fbc2ee-2787-43d6-8c9c-b1a7012d5f33","resolution":{"observed_at":"2026-08-03T11:21:47.494449Z","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":"2026-08-03T11:21:47.509704Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.509704Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:bdec1ac74b5a00f34354a3da773004e46242fd9eb6a4420e3bfe4106bff6e84f","observation_id":"312923f5-abbd-4f35-9945-9f1c1b14044d","resolution":{"observed_at":"2026-08-03T11:21:47.509704Z","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":"2026-08-03T11:21:47.517189Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.517189Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:297f1c6eba97d93379c09b469500430b2355cf9c86a813554d810d13b72a3f90","observation_id":"26cddfe0-5e5a-4ba7-97be-807f3127cd0e","resolution":{"observed_at":"2026-08-03T11:21:47.517189Z","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":"2026-08-03T11:21:47.526885Z","title":"Form >2, we use Monte Carlo approxima- tion: EHVI(x)≈ 1 S SX s=1 max(0,HV(F ∪ {f(s)(x)})−HV(F))(28) wheref (s)(x)are samples from the GP posterior","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.526885Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:beb7be8b1b31337b459a2362deca263cc401e2e3d827398f365b17fcce089c6f","observation_id":"102a9b06-cfaa-4b85-baf3-ec879648c8f4","resolution":{"observed_at":"2026-08-03T11:21:47.526885Z","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":"2026-08-03T11:21:47.546156Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.546156Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:7679d3757a09651d65f96ec20336c710034b424821cb5ac907de6fb7d7455b0a","observation_id":"b8acef5e-10f1-44b1-87e2-814573ffe56f","resolution":{"observed_at":"2026-08-03T11:21:47.546156Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-03T11:21:47.564274Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.564274Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:d49287fea9328e5945a10046ba8c50ae21c075ce831f6b8ed70ea4b27fae34df","observation_id":"c8a1d906-4b63-40c3-b35f-6ac8400b8ffb","resolution":{"observed_at":"2026-08-03T11:21:47.564274Z","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":"2026-08-03T11:21:47.588940Z","title":"fromM u l t i B g o l e a r nimportbgo # qNEHVI w i t h s i n g l e p o i n t s e l e c t i o n VS rec , improvements , i d x = bgo","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.588940Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:c53fe87ef744b9c29e52a30b487f43d04a01b96fe83c1857e1ea6350e5344a79","observation_id":"c6631415-ce4b-4160-90c1-c04076c92396","resolution":{"observed_at":"2026-08-03T11:21:47.588940Z","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":"2026-08-03T11:21:47.611849Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.611849Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:42e9fdffc80b971980df632aa8980094e7afd8edab43cb5750c92d2922059026","observation_id":"3c82a45d-23d6-4270-b387-14049f6a3058","resolution":{"observed_at":"2026-08-03T11:21:47.611849Z","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":"2026-08-03T11:21:47.630291Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.630291Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:016019818216e4772678fc476d2459de14511080986dfa6d89e7ddec340eff05","observation_id":"7d73a429-88f3-4171-ad69-38b5625b606e","resolution":{"observed_at":"2026-08-03T11:21:47.630291Z","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":"2026-08-03T11:21:47.658173Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-03T11:21:47.658173Z"},"links":{"citing_paper":"/paper/2601.06820"},"observation_digest":"sha256:8f718484fa1930b63296f87d559e87b895fab7de0305527d137b3d48a3612445","observation_id":"9a5a3f5a-bc9e-4162-a27d-72fb70019f0b","resolution":{"observed_at":"2026-08-03T11:21:47.658173Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.06820","last_updated":"2026-07-09T03:29:59Z","latest_version":2,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-04T19:57:08.657289Z","submitted_at":"2026-01-11T09:09:21Z","title":"Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":54,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":56},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2601.06820."}