{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CDT3SVIQM3KGLIWHL5VJSNKIGU","short_pith_number":"pith:CDT3SVIQ","schema_version":"1.0","canonical_sha256":"10e7b9551066d465a2c75f6a993548353c2e6b3e8953a23058b9ae3f4b7c09cd","source":{"kind":"arxiv","id":"1902.00140","version":2},"attestation_state":"computed","paper":{"title":"Advances of Machine Learning in Molecular Modeling and Simulation","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.data-an","authors_text":"Johannes Hachmann, Mojtaba Haghighatlari","submitted_at":"2019-02-01T00:18:59Z","abstract_excerpt":"In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make m"},"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":"1902.00140","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"physics.data-an","submitted_at":"2019-02-01T00:18:59Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"145cb55edcf35023dd382ddd613ab936a9095b0c891b336bc5401183f71f4414","abstract_canon_sha256":"623634d9b1ed875f839eaba14a5ce09ce29ca33e57165e672413d124a1f79f6e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:53:08.778852Z","signature_b64":"afT6ZiM2xYz1xtH06NiHkBJjIo0JOzG19MUMKcNM+8Q7oRBAlX5SZVk12SkurO0eSpWqRf7hrwTggbtC1WdRBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10e7b9551066d465a2c75f6a993548353c2e6b3e8953a23058b9ae3f4b7c09cd","last_reissued_at":"2026-05-17T23:53:08.778250Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:53:08.778250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advances of Machine Learning in Molecular Modeling and Simulation","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.data-an","authors_text":"Johannes Hachmann, Mojtaba Haghighatlari","submitted_at":"2019-02-01T00:18:59Z","abstract_excerpt":"In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.00140","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1902.00140","created_at":"2026-05-17T23:53:08.778372+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.00140v2","created_at":"2026-05-17T23:53:08.778372+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.00140","created_at":"2026-05-17T23:53:08.778372+00:00"},{"alias_kind":"pith_short_12","alias_value":"CDT3SVIQM3KG","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"CDT3SVIQM3KGLIWH","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"CDT3SVIQ","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU","json":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU.json","graph_json":"https://pith.science/api/pith-number/CDT3SVIQM3KGLIWHL5VJSNKIGU/graph.json","events_json":"https://pith.science/api/pith-number/CDT3SVIQM3KGLIWHL5VJSNKIGU/events.json","paper":"https://pith.science/paper/CDT3SVIQ"},"agent_actions":{"view_html":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU","download_json":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU.json","view_paper":"https://pith.science/paper/CDT3SVIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.00140&json=true","fetch_graph":"https://pith.science/api/pith-number/CDT3SVIQM3KGLIWHL5VJSNKIGU/graph.json","fetch_events":"https://pith.science/api/pith-number/CDT3SVIQM3KGLIWHL5VJSNKIGU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU/action/storage_attestation","attest_author":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU/action/author_attestation","sign_citation":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU/action/citation_signature","submit_replication":"https://pith.science/pith/CDT3SVIQM3KGLIWHL5VJSNKIGU/action/replication_record"}},"created_at":"2026-05-17T23:53:08.778372+00:00","updated_at":"2026-05-17T23:53:08.778372+00:00"}