{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:IFJ4FC3YMUU6PH47ZS4G3H6W6F","short_pith_number":"pith:IFJ4FC3Y","schema_version":"1.0","canonical_sha256":"4153c28b786529e79f9fccb86d9fd6f17b543354215e61bd7607e7a976c740eb","source":{"kind":"arxiv","id":"2002.05912","version":1},"attestation_state":"computed","paper":{"title":"Automatically growing global reactive neural network potential energy surfaces: a trajectory free active learning strategy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Bin Jiang, Bin Zhao, Qidong Lin, Yaolong Zhang","submitted_at":"2020-02-14T08:21:12Z","abstract_excerpt":"An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of points in the minima of this NSDS can be iteratively added into the training set to improve the PES. The c"},"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":"2002.05912","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2020-02-14T08:21:12Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"7ca656be4b50f660812f39504155831609dadcabb93b807295fc9c933ac06ca9","abstract_canon_sha256":"2f6e676fef3bf831160c23b454fe4857f117f29805812bfbf044894a39c7bb46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:04:15.753816Z","signature_b64":"th8srRkXRfm3Sscdz+86vxEAbeuwi3dizJLGrxLp6y3VVHCE0MhRb+lzgLFaqBS7N9/sES0HiD9ULLuFzzbqAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4153c28b786529e79f9fccb86d9fd6f17b543354215e61bd7607e7a976c740eb","last_reissued_at":"2026-07-05T01:04:15.753412Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:04:15.753412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatically growing global reactive neural network potential energy surfaces: a trajectory free active learning strategy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Bin Jiang, Bin Zhao, Qidong Lin, Yaolong Zhang","submitted_at":"2020-02-14T08:21:12Z","abstract_excerpt":"An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of points in the minima of this NSDS can be iteratively added into the training set to improve the PES. The c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.05912","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/2002.05912/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":"2002.05912","created_at":"2026-07-05T01:04:15.753474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.05912v1","created_at":"2026-07-05T01:04:15.753474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.05912","created_at":"2026-07-05T01:04:15.753474+00:00"},{"alias_kind":"pith_short_12","alias_value":"IFJ4FC3YMUU6","created_at":"2026-07-05T01:04:15.753474+00:00"},{"alias_kind":"pith_short_16","alias_value":"IFJ4FC3YMUU6PH47","created_at":"2026-07-05T01:04:15.753474+00:00"},{"alias_kind":"pith_short_8","alias_value":"IFJ4FC3Y","created_at":"2026-07-05T01:04:15.753474+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/IFJ4FC3YMUU6PH47ZS4G3H6W6F","json":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F.json","graph_json":"https://pith.science/api/pith-number/IFJ4FC3YMUU6PH47ZS4G3H6W6F/graph.json","events_json":"https://pith.science/api/pith-number/IFJ4FC3YMUU6PH47ZS4G3H6W6F/events.json","paper":"https://pith.science/paper/IFJ4FC3Y"},"agent_actions":{"view_html":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F","download_json":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F.json","view_paper":"https://pith.science/paper/IFJ4FC3Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.05912&json=true","fetch_graph":"https://pith.science/api/pith-number/IFJ4FC3YMUU6PH47ZS4G3H6W6F/graph.json","fetch_events":"https://pith.science/api/pith-number/IFJ4FC3YMUU6PH47ZS4G3H6W6F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F/action/storage_attestation","attest_author":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F/action/author_attestation","sign_citation":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F/action/citation_signature","submit_replication":"https://pith.science/pith/IFJ4FC3YMUU6PH47ZS4G3H6W6F/action/replication_record"}},"created_at":"2026-07-05T01:04:15.753474+00:00","updated_at":"2026-07-05T01:04:15.753474+00:00"}