{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Q2POAX5ZITMAD2HBLT22JSZUSA","short_pith_number":"pith:Q2POAX5Z","schema_version":"1.0","canonical_sha256":"869ee05fb944d801e8e15cf5a4cb34900260700cfbfdadb8df3253bdf83c9319","source":{"kind":"arxiv","id":"2404.10746","version":3},"attestation_state":"computed","paper":{"title":"Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.chem-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Jiayu Peng, Juno Nam, Rafael G\\'omez-Bombarelli","submitted_at":"2024-04-16T17:24:22Z","abstract_excerpt":"Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs represent d"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2404.10746","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-04-16T17:24:22Z","cross_cats_sorted":["cs.LG","physics.chem-ph"],"title_canon_sha256":"168f03ec25ee3602960ce130608837de19fae3ffbe0c93e63d55c4a9559e87b0","abstract_canon_sha256":"842e4d68b09bb7b52c621ac0ab33feea736633ed28b0df38cf2cff2d56d56134"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:28.808822Z","signature_b64":"56Bb6J0TBfrp+cW1lMwABZYEbv6jCoQgO5tGauMF6Dp11CmhiWvQXe4n+ldHaSROIugpBBrwzpdmvdPwpi/1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"869ee05fb944d801e8e15cf5a4cb34900260700cfbfdadb8df3253bdf83c9319","last_reissued_at":"2026-07-05T09:43:28.808408Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:28.808408Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.chem-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Jiayu Peng, Juno Nam, Rafael G\\'omez-Bombarelli","submitted_at":"2024-04-16T17:24:22Z","abstract_excerpt":"Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs represent d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10746","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.10746/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2404.10746","created_at":"2026-07-05T09:43:28.808467+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.10746v3","created_at":"2026-07-05T09:43:28.808467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10746","created_at":"2026-07-05T09:43:28.808467+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q2POAX5ZITMA","created_at":"2026-07-05T09:43:28.808467+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q2POAX5ZITMAD2HB","created_at":"2026-07-05T09:43:28.808467+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q2POAX5Z","created_at":"2026-07-05T09:43:28.808467+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.16741","citing_title":"Point defect formation at finite temperatures with machine learning force fields","ref_index":52,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA","json":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA.json","graph_json":"https://pith.science/api/pith-number/Q2POAX5ZITMAD2HBLT22JSZUSA/graph.json","events_json":"https://pith.science/api/pith-number/Q2POAX5ZITMAD2HBLT22JSZUSA/events.json","paper":"https://pith.science/paper/Q2POAX5Z"},"agent_actions":{"view_html":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA","download_json":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA.json","view_paper":"https://pith.science/paper/Q2POAX5Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.10746&json=true","fetch_graph":"https://pith.science/api/pith-number/Q2POAX5ZITMAD2HBLT22JSZUSA/graph.json","fetch_events":"https://pith.science/api/pith-number/Q2POAX5ZITMAD2HBLT22JSZUSA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA/action/storage_attestation","attest_author":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA/action/author_attestation","sign_citation":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA/action/citation_signature","submit_replication":"https://pith.science/pith/Q2POAX5ZITMAD2HBLT22JSZUSA/action/replication_record"}},"created_at":"2026-07-05T09:43:28.808467+00:00","updated_at":"2026-07-05T09:43:28.808467+00:00"}