{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TEKTDKSLILQEBIDDATJ7IMHUZX","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":"a28add806c2f17925ca961e76812f5f2ec13426dc782d819062ae33aa4780534","cross_cats_sorted":["cond-mat.mtrl-sci"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T16:56:11Z","title_canon_sha256":"e3d89b58503e8523c1e8ff40265a2a4a1cfcc7ddca11afb1ce1ddc3d1f411e42"},"schema_version":"1.0","source":{"id":"2506.01868","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01868","created_at":"2026-07-05T11:14:20Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01868v1","created_at":"2026-07-05T11:14:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01868","created_at":"2026-07-05T11:14:20Z"},{"alias_kind":"pith_short_12","alias_value":"TEKTDKSLILQE","created_at":"2026-07-05T11:14:20Z"},{"alias_kind":"pith_short_16","alias_value":"TEKTDKSLILQEBIDD","created_at":"2026-07-05T11:14:20Z"},{"alias_kind":"pith_short_8","alias_value":"TEKTDKSL","created_at":"2026-07-05T11:14:20Z"}],"graph_snapshots":[{"event_id":"sha256:bd8d84457ae3b87f6fb94e5541bdcae403f49a4a6f42dcaa414a8f2d4d544066","target":"graph","created_at":"2026-07-05T11:14:20Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.01868/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and NepTrainKit, which are dedicated to initializing and managing training datasets to generate high-quali","authors_text":"Chengbing Chen, Gang Tang, Rui Zhao, Yutong Li, Zheyong Fan, Zhiyong Wang, Zhoulin Liu","cross_cats":["cond-mat.mtrl-sci"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T16:56:11Z","title":"NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01868","kind":"arxiv","version":1},"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:e1f3dc605ea1630485f4e5bf74efd921fbfd668af57bb008b7ef45368e41ca8d","target":"record","created_at":"2026-07-05T11:14:20Z","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":"a28add806c2f17925ca961e76812f5f2ec13426dc782d819062ae33aa4780534","cross_cats_sorted":["cond-mat.mtrl-sci"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-02T16:56:11Z","title_canon_sha256":"e3d89b58503e8523c1e8ff40265a2a4a1cfcc7ddca11afb1ce1ddc3d1f411e42"},"schema_version":"1.0","source":{"id":"2506.01868","kind":"arxiv","version":1}},"canonical_sha256":"991531aa4b42e040a06304d3f430f4cdc14a564e36594910a96d296356bc29a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"991531aa4b42e040a06304d3f430f4cdc14a564e36594910a96d296356bc29a7","first_computed_at":"2026-07-05T11:14:20.659833Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:14:20.659833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SuMot4CGpjRgLeY/ouascX18SN2EakOSarESr9G7F12cePFH6YG5+SorVoZ9ICE0HkiUSmZNGk1p1KNpZ2iaBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:14:20.660299Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.01868","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e1f3dc605ea1630485f4e5bf74efd921fbfd668af57bb008b7ef45368e41ca8d","sha256:bd8d84457ae3b87f6fb94e5541bdcae403f49a4a6f42dcaa414a8f2d4d544066"],"state_sha256":"ecb5b13f9ecf61c159e1f598a094f6d63102e4bbac4354ecf66ea8f3eef234ff"}