{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:TNVWNMAZWAD7ZMTIGPUTRCJULC","short_pith_number":"pith:TNVWNMAZ","canonical_record":{"source":{"id":"1410.3353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2014-10-10T18:24:05Z","cross_cats_sorted":[],"title_canon_sha256":"3f2c69a6778c686a7fd9542da2daebbf9625bd6dd7a9140105adba3ad34582a9","abstract_canon_sha256":"8953e69e40dc9dbe388bf7fcf6d97b2998d6c410e3ba1bf486b2054ef4744af1"},"schema_version":"1.0"},"canonical_sha256":"9b6b66b019b007fcb26833e93889345881121ca1187f5782bee0172c07988c0d","source":{"kind":"arxiv","id":"1410.3353","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.3353","created_at":"2026-05-18T02:40:11Z"},{"alias_kind":"arxiv_version","alias_value":"1410.3353v1","created_at":"2026-05-18T02:40:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.3353","created_at":"2026-05-18T02:40:11Z"},{"alias_kind":"pith_short_12","alias_value":"TNVWNMAZWAD7","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_16","alias_value":"TNVWNMAZWAD7ZMTI","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_8","alias_value":"TNVWNMAZ","created_at":"2026-05-18T12:28:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:TNVWNMAZWAD7ZMTIGPUTRCJULC","target":"record","payload":{"canonical_record":{"source":{"id":"1410.3353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2014-10-10T18:24:05Z","cross_cats_sorted":[],"title_canon_sha256":"3f2c69a6778c686a7fd9542da2daebbf9625bd6dd7a9140105adba3ad34582a9","abstract_canon_sha256":"8953e69e40dc9dbe388bf7fcf6d97b2998d6c410e3ba1bf486b2054ef4744af1"},"schema_version":"1.0"},"canonical_sha256":"9b6b66b019b007fcb26833e93889345881121ca1187f5782bee0172c07988c0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:11.491800Z","signature_b64":"SGLkxZqI1ZQIpDenIbb90ZODbZIMvdut8OVJedSl/ncx4AdXGARhGrKzdqQCDW03XO1DsphFWXq21zxnwzYVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b6b66b019b007fcb26833e93889345881121ca1187f5782bee0172c07988c0d","last_reissued_at":"2026-05-18T02:40:11.491344Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:11.491344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1410.3353","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":"2026-05-18T02:40:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X6u3m1kE77mUVURXqHslO5oa/tUL9nw+vlhPvZ3xiFlsWPXvyhgq6TbiIwuYcoSvyzIXCaPJM3oAokx2AgWPAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T19:10:43.876003Z"},"content_sha256":"de21bba7d9308f76b8066dd47a6887a938130fbee3f2741bf0bb014be4fdd781","schema_version":"1.0","event_id":"sha256:de21bba7d9308f76b8066dd47a6887a938130fbee3f2741bf0bb014be4fdd781"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:TNVWNMAZWAD7ZMTIGPUTRCJULC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Ab-Initio Molecular Dynamics Acceleration Scheme with an Adaptive Machine Learning Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Rampi Ramprasad, Venkatesh Botu","submitted_at":"2014-10-10T18:24:05Z","abstract_excerpt":"Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in such simulations lead to significant bottlenecks. Here, we lay the foundations for such an accelerated ab-initio MD approach integrated with a machine learning framework. The proposed algorithm learns from previously visited configurations in a continuous and adaptive manner on-the-fly, and predicts (with chemical accuracy) the energies and atomic forces of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.3353","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":""},"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":"2026-05-18T02:40:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H4x2Y6w1v8FoB/z076+JgbVqdAiH8KHHP20S54hhYBUiamy2c5lhgFBqxduxDNV/rg4QIjqI32SrGCHpj9O4CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T19:10:43.876366Z"},"content_sha256":"93ef22222ad2c1a06239ec990fa36030947bad421b4aef83996bee5b1a879509","schema_version":"1.0","event_id":"sha256:93ef22222ad2c1a06239ec990fa36030947bad421b4aef83996bee5b1a879509"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TNVWNMAZWAD7ZMTIGPUTRCJULC/bundle.json","state_url":"https://pith.science/pith/TNVWNMAZWAD7ZMTIGPUTRCJULC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TNVWNMAZWAD7ZMTIGPUTRCJULC/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":"2026-06-30T19:10:43Z","links":{"resolver":"https://pith.science/pith/TNVWNMAZWAD7ZMTIGPUTRCJULC","bundle":"https://pith.science/pith/TNVWNMAZWAD7ZMTIGPUTRCJULC/bundle.json","state":"https://pith.science/pith/TNVWNMAZWAD7ZMTIGPUTRCJULC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TNVWNMAZWAD7ZMTIGPUTRCJULC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:TNVWNMAZWAD7ZMTIGPUTRCJULC","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":"8953e69e40dc9dbe388bf7fcf6d97b2998d6c410e3ba1bf486b2054ef4744af1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2014-10-10T18:24:05Z","title_canon_sha256":"3f2c69a6778c686a7fd9542da2daebbf9625bd6dd7a9140105adba3ad34582a9"},"schema_version":"1.0","source":{"id":"1410.3353","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1410.3353","created_at":"2026-05-18T02:40:11Z"},{"alias_kind":"arxiv_version","alias_value":"1410.3353v1","created_at":"2026-05-18T02:40:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.3353","created_at":"2026-05-18T02:40:11Z"},{"alias_kind":"pith_short_12","alias_value":"TNVWNMAZWAD7","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_16","alias_value":"TNVWNMAZWAD7ZMTI","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_8","alias_value":"TNVWNMAZ","created_at":"2026-05-18T12:28:49Z"}],"graph_snapshots":[{"event_id":"sha256:93ef22222ad2c1a06239ec990fa36030947bad421b4aef83996bee5b1a879509","target":"graph","created_at":"2026-05-18T02:40:11Z","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":"Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in such simulations lead to significant bottlenecks. Here, we lay the foundations for such an accelerated ab-initio MD approach integrated with a machine learning framework. The proposed algorithm learns from previously visited configurations in a continuous and adaptive manner on-the-fly, and predicts (with chemical accuracy) the energies and atomic forces of","authors_text":"Rampi Ramprasad, Venkatesh Botu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2014-10-10T18:24:05Z","title":"Ab-Initio Molecular Dynamics Acceleration Scheme with an Adaptive Machine Learning Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.3353","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:de21bba7d9308f76b8066dd47a6887a938130fbee3f2741bf0bb014be4fdd781","target":"record","created_at":"2026-05-18T02:40:11Z","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":"8953e69e40dc9dbe388bf7fcf6d97b2998d6c410e3ba1bf486b2054ef4744af1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2014-10-10T18:24:05Z","title_canon_sha256":"3f2c69a6778c686a7fd9542da2daebbf9625bd6dd7a9140105adba3ad34582a9"},"schema_version":"1.0","source":{"id":"1410.3353","kind":"arxiv","version":1}},"canonical_sha256":"9b6b66b019b007fcb26833e93889345881121ca1187f5782bee0172c07988c0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b6b66b019b007fcb26833e93889345881121ca1187f5782bee0172c07988c0d","first_computed_at":"2026-05-18T02:40:11.491344Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:40:11.491344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SGLkxZqI1ZQIpDenIbb90ZODbZIMvdut8OVJedSl/ncx4AdXGARhGrKzdqQCDW03XO1DsphFWXq21zxnwzYVDg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:40:11.491800Z","signed_message":"canonical_sha256_bytes"},"source_id":"1410.3353","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:de21bba7d9308f76b8066dd47a6887a938130fbee3f2741bf0bb014be4fdd781","sha256:93ef22222ad2c1a06239ec990fa36030947bad421b4aef83996bee5b1a879509"],"state_sha256":"0b0a1b66c5d9f4ea382e8a1dc8ce1536ab29b1aa96118832750627f5787d6d79"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fDaK5Gi0rTkUB3gvPUC6NF8f4Y27fMfRBjbyBeyklLSS9alCxxTAFXiHZCuOBuHmSPHB0HQ+78lQ14WorKL9Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T19:10:43.878451Z","bundle_sha256":"73d7c249ae971157501d832fc7f5eb809b0830177483a76124ebef2492842d4a"}}