{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KE5KYBVXHUJRO6RFQKNTAFJAGE","short_pith_number":"pith:KE5KYBVX","schema_version":"1.0","canonical_sha256":"513aac06b73d13177a25829b301520310572e6d199dcba619453112363b355fc","source":{"kind":"arxiv","id":"2505.00376","version":1},"attestation_state":"computed","paper":{"title":"Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Hekai Bu, Jianbin Xu, Penghua Ying, Ting Liang, Wengen Ouyang, Wenwu Jiang, Zheyong Fan","submitted_at":"2025-05-01T08:02:12Z","abstract_excerpt":"Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions. However, accurately modeling their thermal transport properties remains a significant challenge, primarily due to the computational limitations of density functional theory (DFT) and the inaccuracies of classical force fields in non-equilibrium regimes. To address this, we use a recently developed hybrid computational framework that combines machine learning potential (MLP) for intralayer interactions"},"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":"2505.00376","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2025-05-01T08:02:12Z","cross_cats_sorted":[],"title_canon_sha256":"f83ab35b3508f8a49ea64b50f63964e035acfed069821fcea4e07502ae12c371","abstract_canon_sha256":"3ed3f8382191990996bb8c06cd8d4d5fc95558aee59f04432945f8709f0de166"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:12.502879Z","signature_b64":"Mk8zbIG4U7NPZS2TYbzuxOm8bTP5QmQGb0D+ByLK5thMZy3R99pxxP09vWsWx6Gg++VGc/pKcuAcu817L8jIDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"513aac06b73d13177a25829b301520310572e6d199dcba619453112363b355fc","last_reissued_at":"2026-07-05T10:57:12.502413Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:12.502413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Hekai Bu, Jianbin Xu, Penghua Ying, Ting Liang, Wengen Ouyang, Wenwu Jiang, Zheyong Fan","submitted_at":"2025-05-01T08:02:12Z","abstract_excerpt":"Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions. However, accurately modeling their thermal transport properties remains a significant challenge, primarily due to the computational limitations of density functional theory (DFT) and the inaccuracies of classical force fields in non-equilibrium regimes. To address this, we use a recently developed hybrid computational framework that combines machine learning potential (MLP) for intralayer interactions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00376","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/2505.00376/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":"2505.00376","created_at":"2026-07-05T10:57:12.502473+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.00376v1","created_at":"2026-07-05T10:57:12.502473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00376","created_at":"2026-07-05T10:57:12.502473+00:00"},{"alias_kind":"pith_short_12","alias_value":"KE5KYBVXHUJR","created_at":"2026-07-05T10:57:12.502473+00:00"},{"alias_kind":"pith_short_16","alias_value":"KE5KYBVXHUJRO6RF","created_at":"2026-07-05T10:57:12.502473+00:00"},{"alias_kind":"pith_short_8","alias_value":"KE5KYBVX","created_at":"2026-07-05T10:57:12.502473+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.21490","citing_title":"Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE","json":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE.json","graph_json":"https://pith.science/api/pith-number/KE5KYBVXHUJRO6RFQKNTAFJAGE/graph.json","events_json":"https://pith.science/api/pith-number/KE5KYBVXHUJRO6RFQKNTAFJAGE/events.json","paper":"https://pith.science/paper/KE5KYBVX"},"agent_actions":{"view_html":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE","download_json":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE.json","view_paper":"https://pith.science/paper/KE5KYBVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.00376&json=true","fetch_graph":"https://pith.science/api/pith-number/KE5KYBVXHUJRO6RFQKNTAFJAGE/graph.json","fetch_events":"https://pith.science/api/pith-number/KE5KYBVXHUJRO6RFQKNTAFJAGE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE/action/storage_attestation","attest_author":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE/action/author_attestation","sign_citation":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE/action/citation_signature","submit_replication":"https://pith.science/pith/KE5KYBVXHUJRO6RFQKNTAFJAGE/action/replication_record"}},"created_at":"2026-07-05T10:57:12.502473+00:00","updated_at":"2026-07-05T10:57:12.502473+00:00"}