{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:UHHK2FVQHR2N4VCANA3SXZ5PH3","short_pith_number":"pith:UHHK2FVQ","canonical_record":{"source":{"id":"2412.16736","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-12-21T18:57:31Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"0939ea8517fa270586e88b27b196b6f92be2eca15749ee421d6cc9b648640541","abstract_canon_sha256":"3b61f7389b5b5eeed2d4eefe8f0296c72d81eac44713811ca8507921bbc2729e"},"schema_version":"1.0"},"canonical_sha256":"a1cead16b03c74de544068372be7af3efc3d4022374f8e48a61056e5f7567777","source":{"kind":"arxiv","id":"2412.16736","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.16736","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"arxiv_version","alias_value":"2412.16736v1","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16736","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"pith_short_12","alias_value":"UHHK2FVQHR2N","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"pith_short_16","alias_value":"UHHK2FVQHR2N4VCA","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"pith_short_8","alias_value":"UHHK2FVQ","created_at":"2026-07-05T09:53:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:UHHK2FVQHR2N4VCANA3SXZ5PH3","target":"record","payload":{"canonical_record":{"source":{"id":"2412.16736","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-12-21T18:57:31Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"0939ea8517fa270586e88b27b196b6f92be2eca15749ee421d6cc9b648640541","abstract_canon_sha256":"3b61f7389b5b5eeed2d4eefe8f0296c72d81eac44713811ca8507921bbc2729e"},"schema_version":"1.0"},"canonical_sha256":"a1cead16b03c74de544068372be7af3efc3d4022374f8e48a61056e5f7567777","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:12.390636Z","signature_b64":"I4uWgCbalF/6eNd1hXHgtmqtoo2HdbAkSW+QV8ua2zOKvz5k2F98/JoUxMb9OtSGG6thqf3glm9MUAuQvfzSDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1cead16b03c74de544068372be7af3efc3d4022374f8e48a61056e5f7567777","last_reissued_at":"2026-07-05T09:53:12.390153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:12.390153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.16736","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:53:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7DsIrU7FpGKv+0Qa7DCBBpkKxldGGAwlNqP3MUUSc3hlHvwePMG4Pt/FsrnTHHvV8GRQxxP21TnlsvqZObFVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T12:25:28.702657Z"},"content_sha256":"a1facde2872f43d0e07e86dab85fa98fda930b45029da45ecd78c74c30e74519","schema_version":"1.0","event_id":"sha256:a1facde2872f43d0e07e86dab85fa98fda930b45029da45ecd78c74c30e74519"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:UHHK2FVQHR2N4VCANA3SXZ5PH3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An automated framework for exploring and learning potential-energy surfaces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"physics.comp-ph","authors_text":"Aakash A. Naik, Christina Ertural, Janine George, Joe D. Morrow, John L. A. Gardner, Natascia L. Fragapane, Volker L. Deringer, Yuanbin Liu, Yuxing Zhou","submitted_at":"2024-12-21T18:57:31Z","abstract_excerpt":"Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex (`automatic potential-landscape explorer'). We discuss design choices, particularly the int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16736","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/2412.16736/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:53:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C4KdWbMK9XGFvUzUK2Kqvidn9JshHcxzumoDzlVCZH6MQdD7fae9spBCkaJ2cofgHlNenhPUWmGp2cmve2hWDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T12:25:28.703027Z"},"content_sha256":"31f9fb7c66935c14bebe8feaa33fff5e0925cd6df8a81d853203871903c4de36","schema_version":"1.0","event_id":"sha256:31f9fb7c66935c14bebe8feaa33fff5e0925cd6df8a81d853203871903c4de36"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UHHK2FVQHR2N4VCANA3SXZ5PH3/bundle.json","state_url":"https://pith.science/pith/UHHK2FVQHR2N4VCANA3SXZ5PH3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UHHK2FVQHR2N4VCANA3SXZ5PH3/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":"2026-07-06T12:25:28Z","links":{"resolver":"https://pith.science/pith/UHHK2FVQHR2N4VCANA3SXZ5PH3","bundle":"https://pith.science/pith/UHHK2FVQHR2N4VCANA3SXZ5PH3/bundle.json","state":"https://pith.science/pith/UHHK2FVQHR2N4VCANA3SXZ5PH3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UHHK2FVQHR2N4VCANA3SXZ5PH3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UHHK2FVQHR2N4VCANA3SXZ5PH3","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":"3b61f7389b5b5eeed2d4eefe8f0296c72d81eac44713811ca8507921bbc2729e","cross_cats_sorted":["cond-mat.mtrl-sci"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-12-21T18:57:31Z","title_canon_sha256":"0939ea8517fa270586e88b27b196b6f92be2eca15749ee421d6cc9b648640541"},"schema_version":"1.0","source":{"id":"2412.16736","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.16736","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"arxiv_version","alias_value":"2412.16736v1","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16736","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"pith_short_12","alias_value":"UHHK2FVQHR2N","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"pith_short_16","alias_value":"UHHK2FVQHR2N4VCA","created_at":"2026-07-05T09:53:12Z"},{"alias_kind":"pith_short_8","alias_value":"UHHK2FVQ","created_at":"2026-07-05T09:53:12Z"}],"graph_snapshots":[{"event_id":"sha256:31f9fb7c66935c14bebe8feaa33fff5e0925cd6df8a81d853203871903c4de36","target":"graph","created_at":"2026-07-05T09:53:12Z","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/2412.16736/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex (`automatic potential-landscape explorer'). We discuss design choices, particularly the int","authors_text":"Aakash A. Naik, Christina Ertural, Janine George, Joe D. Morrow, John L. A. Gardner, Natascia L. Fragapane, Volker L. Deringer, Yuanbin Liu, Yuxing Zhou","cross_cats":["cond-mat.mtrl-sci"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-12-21T18:57:31Z","title":"An automated framework for exploring and learning potential-energy surfaces"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16736","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:a1facde2872f43d0e07e86dab85fa98fda930b45029da45ecd78c74c30e74519","target":"record","created_at":"2026-07-05T09:53:12Z","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":"3b61f7389b5b5eeed2d4eefe8f0296c72d81eac44713811ca8507921bbc2729e","cross_cats_sorted":["cond-mat.mtrl-sci"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-12-21T18:57:31Z","title_canon_sha256":"0939ea8517fa270586e88b27b196b6f92be2eca15749ee421d6cc9b648640541"},"schema_version":"1.0","source":{"id":"2412.16736","kind":"arxiv","version":1}},"canonical_sha256":"a1cead16b03c74de544068372be7af3efc3d4022374f8e48a61056e5f7567777","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a1cead16b03c74de544068372be7af3efc3d4022374f8e48a61056e5f7567777","first_computed_at":"2026-07-05T09:53:12.390153Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:53:12.390153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"I4uWgCbalF/6eNd1hXHgtmqtoo2HdbAkSW+QV8ua2zOKvz5k2F98/JoUxMb9OtSGG6thqf3glm9MUAuQvfzSDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:53:12.390636Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.16736","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a1facde2872f43d0e07e86dab85fa98fda930b45029da45ecd78c74c30e74519","sha256:31f9fb7c66935c14bebe8feaa33fff5e0925cd6df8a81d853203871903c4de36"],"state_sha256":"80e3d0d16c52922bbc4226850c35c8d8eff05c61c4d078dea8e648907461184a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zPLUl02VER769yoZV5G1tv+ndUSCUFzAccny1s8UFbVi8e1oU40/zr3p+Aay4AFWaHb/CRlX3vipTf126RCqAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T12:25:28.705181Z","bundle_sha256":"0c6ca23c21c67f588902fa29a25de9d1f2945433b0b049a2110509d37f13fc87"}}