{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EA5AWKWJ6NESLQCXIIQW4RL4ZM","short_pith_number":"pith:EA5AWKWJ","schema_version":"1.0","canonical_sha256":"203a0b2ac9f34925c05742216e457ccb3975921980858469b1681e975d139fbd","source":{"kind":"arxiv","id":"2307.07609","version":1},"attestation_state":"computed","paper":{"title":"Interpretable machine learning to understand the performance of semi local density functionals for materials thermochemistry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Christopher J. Bartel, Christopher Sutton, Santosh Adhikari","submitted_at":"2023-07-14T20:09:12Z","abstract_excerpt":"This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 1011 solid-state compounds. The ML model uses a set of 25 properties that characterize the electronic structure as calculated using PBE and SCAN. The ML model significantly decreases the error in PBE-calculated Hf values from an mean absolute error (MAE) of 195 meV/atom to an MAE = 80 meV/atom when compared to the experiment. For PBE, the PDP+GAM analysis shows compounds with a high ionicity (I), i.e., I>0.22, have err"},"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":"2307.07609","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-07-14T20:09:12Z","cross_cats_sorted":[],"title_canon_sha256":"f095b55c1d738890f8161d24a9cf5fade8f4048f1b4acf7c0269dc7b6c8f07fc","abstract_canon_sha256":"e36257753044707133a025d278c5d9614011b78f9efaeb6cf6f962c4e5be0b88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:30:59.837071Z","signature_b64":"X22ey8SJOxaaTwK4LteU0x5llgPXE0120HBlPb5mB/U29lZudoKKgBt77GDlx6K3NZXbvKgX/Z0ZLKOvT9NvDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"203a0b2ac9f34925c05742216e457ccb3975921980858469b1681e975d139fbd","last_reissued_at":"2026-07-05T06:30:59.836533Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:30:59.836533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpretable machine learning to understand the performance of semi local density functionals for materials thermochemistry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Christopher J. Bartel, Christopher Sutton, Santosh Adhikari","submitted_at":"2023-07-14T20:09:12Z","abstract_excerpt":"This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 1011 solid-state compounds. The ML model uses a set of 25 properties that characterize the electronic structure as calculated using PBE and SCAN. The ML model significantly decreases the error in PBE-calculated Hf values from an mean absolute error (MAE) of 195 meV/atom to an MAE = 80 meV/atom when compared to the experiment. For PBE, the PDP+GAM analysis shows compounds with a high ionicity (I), i.e., I>0.22, have err"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.07609","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/2307.07609/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":"2307.07609","created_at":"2026-07-05T06:30:59.836592+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.07609v1","created_at":"2026-07-05T06:30:59.836592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.07609","created_at":"2026-07-05T06:30:59.836592+00:00"},{"alias_kind":"pith_short_12","alias_value":"EA5AWKWJ6NES","created_at":"2026-07-05T06:30:59.836592+00:00"},{"alias_kind":"pith_short_16","alias_value":"EA5AWKWJ6NESLQCX","created_at":"2026-07-05T06:30:59.836592+00:00"},{"alias_kind":"pith_short_8","alias_value":"EA5AWKWJ","created_at":"2026-07-05T06:30:59.836592+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM","json":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM.json","graph_json":"https://pith.science/api/pith-number/EA5AWKWJ6NESLQCXIIQW4RL4ZM/graph.json","events_json":"https://pith.science/api/pith-number/EA5AWKWJ6NESLQCXIIQW4RL4ZM/events.json","paper":"https://pith.science/paper/EA5AWKWJ"},"agent_actions":{"view_html":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM","download_json":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM.json","view_paper":"https://pith.science/paper/EA5AWKWJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.07609&json=true","fetch_graph":"https://pith.science/api/pith-number/EA5AWKWJ6NESLQCXIIQW4RL4ZM/graph.json","fetch_events":"https://pith.science/api/pith-number/EA5AWKWJ6NESLQCXIIQW4RL4ZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM/action/storage_attestation","attest_author":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM/action/author_attestation","sign_citation":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM/action/citation_signature","submit_replication":"https://pith.science/pith/EA5AWKWJ6NESLQCXIIQW4RL4ZM/action/replication_record"}},"created_at":"2026-07-05T06:30:59.836592+00:00","updated_at":"2026-07-05T06:30:59.836592+00:00"}