{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:U76Q2PQEIHXJG47FASGM7FQHYF","short_pith_number":"pith:U76Q2PQE","schema_version":"1.0","canonical_sha256":"a7fd0d3e0441ee9373e5048ccf9607c1713a5750005201fe53b16f65b0a5a4c9","source":{"kind":"arxiv","id":"2507.05559","version":1},"attestation_state":"computed","paper":{"title":"MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Aaron D. Kaplan, Daryl C. Chrzan, Kristin A. Persson, Mark Asta, Matthew C. Kuner","submitted_at":"2025-07-08T00:45:32Z","abstract_excerpt":"We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynami"},"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":"2507.05559","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-07-08T00:45:32Z","cross_cats_sorted":["cs.AI","physics.comp-ph"],"title_canon_sha256":"896d05296ee59d113c169a988647af8e2a4750d932ce94a24d5107d0829bccbd","abstract_canon_sha256":"39fbd240e58734f87f8756a5375674f01e364016f2852c806e8ab3f959c6a3e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:32.457691Z","signature_b64":"ciFaIPVPLbqDk9+MGIUe6edfGCpD1kEndqriKQCeUvUiWPCsdUmm08s0ycsunHq3derTeV1p8sD1jxY8n98nDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7fd0d3e0441ee9373e5048ccf9607c1713a5750005201fe53b16f65b0a5a4c9","last_reissued_at":"2026-07-05T11:33:32.457204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:32.457204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Aaron D. Kaplan, Daryl C. Chrzan, Kristin A. Persson, Mark Asta, Matthew C. Kuner","submitted_at":"2025-07-08T00:45:32Z","abstract_excerpt":"We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynami"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05559","kind":"arxiv","version":1},"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/2507.05559/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":"2507.05559","created_at":"2026-07-05T11:33:32.457261+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.05559v1","created_at":"2026-07-05T11:33:32.457261+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05559","created_at":"2026-07-05T11:33:32.457261+00:00"},{"alias_kind":"pith_short_12","alias_value":"U76Q2PQEIHXJ","created_at":"2026-07-05T11:33:32.457261+00:00"},{"alias_kind":"pith_short_16","alias_value":"U76Q2PQEIHXJG47F","created_at":"2026-07-05T11:33:32.457261+00:00"},{"alias_kind":"pith_short_8","alias_value":"U76Q2PQE","created_at":"2026-07-05T11:33:32.457261+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.04622","citing_title":"VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python","ref_index":261,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF","json":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF.json","graph_json":"https://pith.science/api/pith-number/U76Q2PQEIHXJG47FASGM7FQHYF/graph.json","events_json":"https://pith.science/api/pith-number/U76Q2PQEIHXJG47FASGM7FQHYF/events.json","paper":"https://pith.science/paper/U76Q2PQE"},"agent_actions":{"view_html":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF","download_json":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF.json","view_paper":"https://pith.science/paper/U76Q2PQE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.05559&json=true","fetch_graph":"https://pith.science/api/pith-number/U76Q2PQEIHXJG47FASGM7FQHYF/graph.json","fetch_events":"https://pith.science/api/pith-number/U76Q2PQEIHXJG47FASGM7FQHYF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF/action/storage_attestation","attest_author":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF/action/author_attestation","sign_citation":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF/action/citation_signature","submit_replication":"https://pith.science/pith/U76Q2PQEIHXJG47FASGM7FQHYF/action/replication_record"}},"created_at":"2026-07-05T11:33:32.457261+00:00","updated_at":"2026-07-05T11:33:32.457261+00:00"}