{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:YFUENBKHDARISQDV2E3IJUQNYS","short_pith_number":"pith:YFUENBKH","canonical_record":{"source":{"id":"1610.08935","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2016-10-27T19:07:23Z","cross_cats_sorted":[],"title_canon_sha256":"9a78d8803b282211c3b2cb785af305c8b108034da2c50e148da89bb21eb6f0f3","abstract_canon_sha256":"b296e3055994354f82d21a5e9da06577e167cf9b7682370ba17e118301914f6a"},"schema_version":"1.0"},"canonical_sha256":"c1684685471822894075d13684d20dc48146823bf6b2dcf1162048c2251e31a2","source":{"kind":"arxiv","id":"1610.08935","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08935","created_at":"2026-05-18T00:51:05Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08935v4","created_at":"2026-05-18T00:51:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08935","created_at":"2026-05-18T00:51:05Z"},{"alias_kind":"pith_short_12","alias_value":"YFUENBKHDARI","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YFUENBKHDARISQDV","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YFUENBKH","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:YFUENBKHDARISQDV2E3IJUQNYS","target":"record","payload":{"canonical_record":{"source":{"id":"1610.08935","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2016-10-27T19:07:23Z","cross_cats_sorted":[],"title_canon_sha256":"9a78d8803b282211c3b2cb785af305c8b108034da2c50e148da89bb21eb6f0f3","abstract_canon_sha256":"b296e3055994354f82d21a5e9da06577e167cf9b7682370ba17e118301914f6a"},"schema_version":"1.0"},"canonical_sha256":"c1684685471822894075d13684d20dc48146823bf6b2dcf1162048c2251e31a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:05.571736Z","signature_b64":"kYCeJHnAWNhhcbigrpeAnzgLihVoB4zySCNwFoHG0BDHdIKIu0wpiAMjD8vL2aRd+PSkdlDXQiCmeOaCERdICg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1684685471822894075d13684d20dc48146823bf6b2dcf1162048c2251e31a2","last_reissued_at":"2026-05-18T00:51:05.571242Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:05.571242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.08935","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:51:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kNoizfEh2Yx0d4f1lBQRlWpUZgxO65aAf2xu7PCsGxNXhFNLSFUuRbvv08vS3HOjnRk1xDzUDcBdshTi1HNUAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T16:18:00.977756Z"},"content_sha256":"9ff8c5db73c69f77735c7ac2681feb407c45c60a48f9dc012805f08e4174d79c","schema_version":"1.0","event_id":"sha256:9ff8c5db73c69f77735c7ac2681feb407c45c60a48f9dc012805f08e4174d79c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:YFUENBKHDARISQDV2E3IJUQNYS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Adrian E. Roitberg, Justin S. Smith, Olexandr Isayev","submitted_at":"2016-10-27T19:07:23Z","abstract_excerpt":"Deep learning is revolutionizing many areas of science and technology, especially image, text and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and fully transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI in short. ANI is a new method and procedure for training neural network potentials that utilizes a highly modified version of the Behler and Parrinello symmetry functions to build single-atom atomic enviro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08935","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:51:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nNF0E574SjfIa2f4D6ZULsbCQSX4IGCsxCw45lqLN+uvJcqV+pkyWn1EfJf5ITg7DGqAWfK5BbN5XvxppqmNBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T16:18:00.978460Z"},"content_sha256":"ce9bee68181c67c1fd92de27cce56911b9fdf8e3c39931690a517a88673a79d4","schema_version":"1.0","event_id":"sha256:ce9bee68181c67c1fd92de27cce56911b9fdf8e3c39931690a517a88673a79d4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YFUENBKHDARISQDV2E3IJUQNYS/bundle.json","state_url":"https://pith.science/pith/YFUENBKHDARISQDV2E3IJUQNYS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YFUENBKHDARISQDV2E3IJUQNYS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-29T16:18:00Z","links":{"resolver":"https://pith.science/pith/YFUENBKHDARISQDV2E3IJUQNYS","bundle":"https://pith.science/pith/YFUENBKHDARISQDV2E3IJUQNYS/bundle.json","state":"https://pith.science/pith/YFUENBKHDARISQDV2E3IJUQNYS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YFUENBKHDARISQDV2E3IJUQNYS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:YFUENBKHDARISQDV2E3IJUQNYS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b296e3055994354f82d21a5e9da06577e167cf9b7682370ba17e118301914f6a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2016-10-27T19:07:23Z","title_canon_sha256":"9a78d8803b282211c3b2cb785af305c8b108034da2c50e148da89bb21eb6f0f3"},"schema_version":"1.0","source":{"id":"1610.08935","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08935","created_at":"2026-05-18T00:51:05Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08935v4","created_at":"2026-05-18T00:51:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08935","created_at":"2026-05-18T00:51:05Z"},{"alias_kind":"pith_short_12","alias_value":"YFUENBKHDARI","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YFUENBKHDARISQDV","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YFUENBKH","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:ce9bee68181c67c1fd92de27cce56911b9fdf8e3c39931690a517a88673a79d4","target":"graph","created_at":"2026-05-18T00:51:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep learning is revolutionizing many areas of science and technology, especially image, text and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and fully transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI in short. ANI is a new method and procedure for training neural network potentials that utilizes a highly modified version of the Behler and Parrinello symmetry functions to build single-atom atomic enviro","authors_text":"Adrian E. Roitberg, Justin S. Smith, Olexandr Isayev","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2016-10-27T19:07:23Z","title":"ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08935","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9ff8c5db73c69f77735c7ac2681feb407c45c60a48f9dc012805f08e4174d79c","target":"record","created_at":"2026-05-18T00:51:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b296e3055994354f82d21a5e9da06577e167cf9b7682370ba17e118301914f6a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2016-10-27T19:07:23Z","title_canon_sha256":"9a78d8803b282211c3b2cb785af305c8b108034da2c50e148da89bb21eb6f0f3"},"schema_version":"1.0","source":{"id":"1610.08935","kind":"arxiv","version":4}},"canonical_sha256":"c1684685471822894075d13684d20dc48146823bf6b2dcf1162048c2251e31a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1684685471822894075d13684d20dc48146823bf6b2dcf1162048c2251e31a2","first_computed_at":"2026-05-18T00:51:05.571242Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:51:05.571242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kYCeJHnAWNhhcbigrpeAnzgLihVoB4zySCNwFoHG0BDHdIKIu0wpiAMjD8vL2aRd+PSkdlDXQiCmeOaCERdICg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:51:05.571736Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.08935","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ff8c5db73c69f77735c7ac2681feb407c45c60a48f9dc012805f08e4174d79c","sha256:ce9bee68181c67c1fd92de27cce56911b9fdf8e3c39931690a517a88673a79d4"],"state_sha256":"eb954cea2eb96a1f77b64c758b621c67479326662bf3657a31749df1ed0c8f19"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k5H92rtixxzwSAVlp+yqPQXnmdNe+AWMplhodvv9hRwyAvzb+rpuZuFh2NpOxH8FdjksEyT8iEOA3rDiElCEBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T16:18:00.982278Z","bundle_sha256":"dcda860c370636f766264cad0d3d716acc07df2e892f1f1fd08a426fb6baa3ad"}}