{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GHDVHOT55OGIQFJDFJYX7MZNBA","short_pith_number":"pith:GHDVHOT5","schema_version":"1.0","canonical_sha256":"31c753ba7deb8c8815232a717fb32d08155c3c89148297ea6b2fecdb07a10c96","source":{"kind":"arxiv","id":"2407.09674","version":1},"attestation_state":"computed","paper":{"title":"Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Chris Wolverton, Huiju Lee, Vinay I. Hegde, Yi Xia","submitted_at":"2024-07-12T20:15:06Z","abstract_excerpt":"Phonons play a critical role in determining various material properties, but conventional methods for phonon calculations are computationally intensive, limiting their broad applicability. In this study, we present an approach to accelerate high-throughput harmonic phonon calculations using machine learning universal potentials. We train a state-of-the-art machine learning interatomic potential, based on multi-atomic cluster expansion (MACE), on a comprehensive dataset of 2,738 crystal structures with 77 elements, totaling 15,670 supercell structures, computed using high-fidelity density funct"},"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":"2407.09674","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-07-12T20:15:06Z","cross_cats_sorted":[],"title_canon_sha256":"911bbb9497292587c24666b3545be840ea180e6f17426bc90370514f0867490c","abstract_canon_sha256":"5b515cbb8cd58f3d177739b6564d8fbfa40c317ebec19cee88d0d507b15b59ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:33.441733Z","signature_b64":"gfDe89PvIwB8v8v+yt0OL6Dsh2M3xrTXvEKGeXFcFb3GB1hazarFaYwJcK+ZUq/OPAU2upk9EdPvp7KN0ul4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31c753ba7deb8c8815232a717fb32d08155c3c89148297ea6b2fecdb07a10c96","last_reissued_at":"2026-07-05T08:43:33.441298Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:33.441298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Chris Wolverton, Huiju Lee, Vinay I. Hegde, Yi Xia","submitted_at":"2024-07-12T20:15:06Z","abstract_excerpt":"Phonons play a critical role in determining various material properties, but conventional methods for phonon calculations are computationally intensive, limiting their broad applicability. In this study, we present an approach to accelerate high-throughput harmonic phonon calculations using machine learning universal potentials. We train a state-of-the-art machine learning interatomic potential, based on multi-atomic cluster expansion (MACE), on a comprehensive dataset of 2,738 crystal structures with 77 elements, totaling 15,670 supercell structures, computed using high-fidelity density funct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.09674","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/2407.09674/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":"2407.09674","created_at":"2026-07-05T08:43:33.441366+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.09674v1","created_at":"2026-07-05T08:43:33.441366+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.09674","created_at":"2026-07-05T08:43:33.441366+00:00"},{"alias_kind":"pith_short_12","alias_value":"GHDVHOT55OGI","created_at":"2026-07-05T08:43:33.441366+00:00"},{"alias_kind":"pith_short_16","alias_value":"GHDVHOT55OGIQFJD","created_at":"2026-07-05T08:43:33.441366+00:00"},{"alias_kind":"pith_short_8","alias_value":"GHDVHOT5","created_at":"2026-07-05T08:43:33.441366+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.03578","citing_title":"Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA","json":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA.json","graph_json":"https://pith.science/api/pith-number/GHDVHOT55OGIQFJDFJYX7MZNBA/graph.json","events_json":"https://pith.science/api/pith-number/GHDVHOT55OGIQFJDFJYX7MZNBA/events.json","paper":"https://pith.science/paper/GHDVHOT5"},"agent_actions":{"view_html":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA","download_json":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA.json","view_paper":"https://pith.science/paper/GHDVHOT5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.09674&json=true","fetch_graph":"https://pith.science/api/pith-number/GHDVHOT55OGIQFJDFJYX7MZNBA/graph.json","fetch_events":"https://pith.science/api/pith-number/GHDVHOT55OGIQFJDFJYX7MZNBA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA/action/storage_attestation","attest_author":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA/action/author_attestation","sign_citation":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA/action/citation_signature","submit_replication":"https://pith.science/pith/GHDVHOT55OGIQFJDFJYX7MZNBA/action/replication_record"}},"created_at":"2026-07-05T08:43:33.441366+00:00","updated_at":"2026-07-05T08:43:33.441366+00:00"}