{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LJMZGAVO7CFQMVNTULB6KAHTE2","short_pith_number":"pith:LJMZGAVO","schema_version":"1.0","canonical_sha256":"5a599302aef88b0655b3a2c3e500f326ad1033ecf06adc7003466f777a6ee2e7","source":{"kind":"arxiv","id":"1902.01269","version":1},"attestation_state":"computed","paper":{"title":"ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Abdulmonem Obaied, Aleksei Egorov, Brandon Bocklund, Irina Roslyakova, Richard Otis, Zi-Kui Liu","submitted_at":"2019-02-04T15:57:48Z","abstract_excerpt":"The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refine the model parameters using phase equilibrium data through Bayesian optimization within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu-Mg system down to 0 K using unary descriptions based on segmented regression. The mod"},"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":"1902.01269","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2019-02-04T15:57:48Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"667a2bd6b5b8c677f7da1bf4055414bb8279f18812924c40fd6399ad3cc74e45","abstract_canon_sha256":"d5cbd71b9d4259939706cd9c26c67d936f205e049266b0d3d41d7b36e0fbcbc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:24.923389Z","signature_b64":"fuNzjEzIhanov1Bz7oJbvsAVExWoa6LeT56/8u7ReK1sr9Dfbwih3U+nbs/EyCXf1unQeChlqRqMs5wjSL/wAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a599302aef88b0655b3a2c3e500f326ad1033ecf06adc7003466f777a6ee2e7","last_reissued_at":"2026-05-17T23:39:24.922732Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:24.922732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Abdulmonem Obaied, Aleksei Egorov, Brandon Bocklund, Irina Roslyakova, Richard Otis, Zi-Kui Liu","submitted_at":"2019-02-04T15:57:48Z","abstract_excerpt":"The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refine the model parameters using phase equilibrium data through Bayesian optimization within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu-Mg system down to 0 K using unary descriptions based on segmented regression. The mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.01269","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.01269","created_at":"2026-05-17T23:39:24.922835+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.01269v1","created_at":"2026-05-17T23:39:24.922835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.01269","created_at":"2026-05-17T23:39:24.922835+00:00"},{"alias_kind":"pith_short_12","alias_value":"LJMZGAVO7CFQ","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"LJMZGAVO7CFQMVNT","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"LJMZGAVO","created_at":"2026-05-18T12:33:21.387695+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/LJMZGAVO7CFQMVNTULB6KAHTE2","json":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2.json","graph_json":"https://pith.science/api/pith-number/LJMZGAVO7CFQMVNTULB6KAHTE2/graph.json","events_json":"https://pith.science/api/pith-number/LJMZGAVO7CFQMVNTULB6KAHTE2/events.json","paper":"https://pith.science/paper/LJMZGAVO"},"agent_actions":{"view_html":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2","download_json":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2.json","view_paper":"https://pith.science/paper/LJMZGAVO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.01269&json=true","fetch_graph":"https://pith.science/api/pith-number/LJMZGAVO7CFQMVNTULB6KAHTE2/graph.json","fetch_events":"https://pith.science/api/pith-number/LJMZGAVO7CFQMVNTULB6KAHTE2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2/action/storage_attestation","attest_author":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2/action/author_attestation","sign_citation":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2/action/citation_signature","submit_replication":"https://pith.science/pith/LJMZGAVO7CFQMVNTULB6KAHTE2/action/replication_record"}},"created_at":"2026-05-17T23:39:24.922835+00:00","updated_at":"2026-05-17T23:39:24.922835+00:00"}