{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:VB23ZZ6ZQM3KMDTDGRZCO6KJAK","short_pith_number":"pith:VB23ZZ6Z","canonical_record":{"source":{"id":"1608.07374","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2016-08-26T07:26:39Z","cross_cats_sorted":[],"title_canon_sha256":"396ffc51d1a4abe51d26150a5933d9554f5f9d41ddc52edd4981f91484843716","abstract_canon_sha256":"35d66fd3a570055f10b5ac5bf48920e9a6cd016588ff43fab53f5b5467b69c3a"},"schema_version":"1.0"},"canonical_sha256":"a875bce7d98336a60e63347227794902877c443fb390be9a6f1695e86a898631","source":{"kind":"arxiv","id":"1608.07374","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07374","created_at":"2026-05-18T00:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07374v1","created_at":"2026-05-18T00:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07374","created_at":"2026-05-18T00:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"VB23ZZ6ZQM3K","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VB23ZZ6ZQM3KMDTD","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VB23ZZ6Z","created_at":"2026-05-18T12:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:VB23ZZ6ZQM3KMDTDGRZCO6KJAK","target":"record","payload":{"canonical_record":{"source":{"id":"1608.07374","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2016-08-26T07:26:39Z","cross_cats_sorted":[],"title_canon_sha256":"396ffc51d1a4abe51d26150a5933d9554f5f9d41ddc52edd4981f91484843716","abstract_canon_sha256":"35d66fd3a570055f10b5ac5bf48920e9a6cd016588ff43fab53f5b5467b69c3a"},"schema_version":"1.0"},"canonical_sha256":"a875bce7d98336a60e63347227794902877c443fb390be9a6f1695e86a898631","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:51.526812Z","signature_b64":"TlS/ItTPnDDIOyUtetPQ+c+QkH7qcVbG0hsbPrVbT29JrslGDkP2noTA3eLgAOD1gh6XKDc0zjUNM5/jZ/ozBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a875bce7d98336a60e63347227794902877c443fb390be9a6f1695e86a898631","last_reissued_at":"2026-05-18T00:21:51.526224Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:51.526224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.07374","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":"2026-05-18T00:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RkYUWSMbKWrM3DBkomDWaATdLCkGFfdK6ZhI/PR6IEcw+j1kMJAIkI/R8vZv7p8IzVuM9h71KJTVf5ZqbEmMDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T09:50:53.186416Z"},"content_sha256":"36d392e0805af1445e853e89323094b6e7bd0ee6d6857581c5403548510f99e8","schema_version":"1.0","event_id":"sha256:36d392e0805af1445e853e89323094b6e7bd0ee6d6857581c5403548510f99e8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:VB23ZZ6ZQM3KMDTDGRZCO6KJAK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Learning for Atomic Forces in a Crystalline Solid: Transferability to Various Temperatures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Ryo Tamura, Teppei Suzuki, Tsuyoshi Miyazaki","submitted_at":"2016-08-26T07:26:39Z","abstract_excerpt":"Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of atoms, we trained a machine-learning model on a crystalline silicon system in order to directly predict the atomic forces at a wide range of temperatures. Our idea is to construct a machine-learning model using a quantum-mechanical data set taken from canonical-ensemble simulations at a higher temperature, or an upper bound of the temperature range. With ou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07374","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":"2026-05-18T00:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Us0wIBIUbw1fOY4oIfo7S1Vjy47COL2wg5O123mNpKwTV9ajDE1S727BduBJYvni7x4jvEf+OihYgY0GGFBmAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T09:50:53.186781Z"},"content_sha256":"9d2921f7cdcc8417f4d4e9fa4fa47a9a2598d5fd687cac03b0169d5f1a90367e","schema_version":"1.0","event_id":"sha256:9d2921f7cdcc8417f4d4e9fa4fa47a9a2598d5fd687cac03b0169d5f1a90367e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VB23ZZ6ZQM3KMDTDGRZCO6KJAK/bundle.json","state_url":"https://pith.science/pith/VB23ZZ6ZQM3KMDTDGRZCO6KJAK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VB23ZZ6ZQM3KMDTDGRZCO6KJAK/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":"2026-06-09T09:50:53Z","links":{"resolver":"https://pith.science/pith/VB23ZZ6ZQM3KMDTDGRZCO6KJAK","bundle":"https://pith.science/pith/VB23ZZ6ZQM3KMDTDGRZCO6KJAK/bundle.json","state":"https://pith.science/pith/VB23ZZ6ZQM3KMDTDGRZCO6KJAK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VB23ZZ6ZQM3KMDTDGRZCO6KJAK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:VB23ZZ6ZQM3KMDTDGRZCO6KJAK","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":"35d66fd3a570055f10b5ac5bf48920e9a6cd016588ff43fab53f5b5467b69c3a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2016-08-26T07:26:39Z","title_canon_sha256":"396ffc51d1a4abe51d26150a5933d9554f5f9d41ddc52edd4981f91484843716"},"schema_version":"1.0","source":{"id":"1608.07374","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07374","created_at":"2026-05-18T00:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07374v1","created_at":"2026-05-18T00:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07374","created_at":"2026-05-18T00:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"VB23ZZ6ZQM3K","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VB23ZZ6ZQM3KMDTD","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VB23ZZ6Z","created_at":"2026-05-18T12:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:9d2921f7cdcc8417f4d4e9fa4fa47a9a2598d5fd687cac03b0169d5f1a90367e","target":"graph","created_at":"2026-05-18T00:21:51Z","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":"Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of atoms, we trained a machine-learning model on a crystalline silicon system in order to directly predict the atomic forces at a wide range of temperatures. Our idea is to construct a machine-learning model using a quantum-mechanical data set taken from canonical-ensemble simulations at a higher temperature, or an upper bound of the temperature range. With ou","authors_text":"Ryo Tamura, Teppei Suzuki, Tsuyoshi Miyazaki","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2016-08-26T07:26:39Z","title":"Machine Learning for Atomic Forces in a Crystalline Solid: Transferability to Various Temperatures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07374","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":"sha256:36d392e0805af1445e853e89323094b6e7bd0ee6d6857581c5403548510f99e8","target":"record","created_at":"2026-05-18T00:21:51Z","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":"35d66fd3a570055f10b5ac5bf48920e9a6cd016588ff43fab53f5b5467b69c3a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2016-08-26T07:26:39Z","title_canon_sha256":"396ffc51d1a4abe51d26150a5933d9554f5f9d41ddc52edd4981f91484843716"},"schema_version":"1.0","source":{"id":"1608.07374","kind":"arxiv","version":1}},"canonical_sha256":"a875bce7d98336a60e63347227794902877c443fb390be9a6f1695e86a898631","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a875bce7d98336a60e63347227794902877c443fb390be9a6f1695e86a898631","first_computed_at":"2026-05-18T00:21:51.526224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:51.526224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TlS/ItTPnDDIOyUtetPQ+c+QkH7qcVbG0hsbPrVbT29JrslGDkP2noTA3eLgAOD1gh6XKDc0zjUNM5/jZ/ozBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:51.526812Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.07374","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:36d392e0805af1445e853e89323094b6e7bd0ee6d6857581c5403548510f99e8","sha256:9d2921f7cdcc8417f4d4e9fa4fa47a9a2598d5fd687cac03b0169d5f1a90367e"],"state_sha256":"cd10689ae6f1c938066b8b7f3d05d8235cbaa354dbda5a2f2a03b12704fb33f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4cE5kV4SbWxdnj+2yeD7cSEjE1m6qxbiBJQupuISZrdTQqSb0wh0ozL148BEdXfQ8MIvGl2KXHj+ivUOYWNJAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T09:50:53.188630Z","bundle_sha256":"8781191a5b3df0fbaef09e497b561f07e6adc48b9ba0def89efd7466337b579e"}}