{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CA4BETYYLQPCPDFI65B2XIBHZ2","short_pith_number":"pith:CA4BETYY","schema_version":"1.0","canonical_sha256":"1038124f185c1e278ca8f743aba027ce979dff4e076f838ed3f9e4b82de7b34e","source":{"kind":"arxiv","id":"1901.04136","version":2},"attestation_state":"computed","paper":{"title":"Symbolic Regression in Materials Science","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"James M. Rondinelli, Nicholas Wagner, Yiqun Wang","submitted_at":"2019-01-14T05:31:27Z","abstract_excerpt":"We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, we discuss industrial applications of symbolic regression and its potential applications in materials science. We then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson-Mehl-Avrami-Kolmogorov (JMAK) form, and learning the Landau free "},"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":"1901.04136","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2019-01-14T05:31:27Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"b4764f9a5740d8fcfed81950ae9f99f2eed6162eb59a9d50683576175c1a6bd4","abstract_canon_sha256":"6c986f0466493fe2171d0cc2cd0faa15cec04332aaf9b1fc6411692cacb1830e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:08:54.284809Z","signature_b64":"3HAgSO8XoxOKyV/IJy/wIxYMiWOdlsHPktIvy6AaqQN8PM09e1AE/0gegfIX13N/zTMvwvWKae0PKRtKQK54DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1038124f185c1e278ca8f743aba027ce979dff4e076f838ed3f9e4b82de7b34e","last_reissued_at":"2026-07-05T00:08:54.284432Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:08:54.284432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Symbolic Regression in Materials Science","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"James M. Rondinelli, Nicholas Wagner, Yiqun Wang","submitted_at":"2019-01-14T05:31:27Z","abstract_excerpt":"We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, we discuss industrial applications of symbolic regression and its potential applications in materials science. We then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson-Mehl-Avrami-Kolmogorov (JMAK) form, and learning the Landau free "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.04136","kind":"arxiv","version":2},"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/1901.04136/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":"1901.04136","created_at":"2026-07-05T00:08:54.284487+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.04136v2","created_at":"2026-07-05T00:08:54.284487+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.04136","created_at":"2026-07-05T00:08:54.284487+00:00"},{"alias_kind":"pith_short_12","alias_value":"CA4BETYYLQPC","created_at":"2026-07-05T00:08:54.284487+00:00"},{"alias_kind":"pith_short_16","alias_value":"CA4BETYYLQPCPDFI","created_at":"2026-07-05T00:08:54.284487+00:00"},{"alias_kind":"pith_short_8","alias_value":"CA4BETYY","created_at":"2026-07-05T00:08:54.284487+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.13510","citing_title":"On the definition and importance of interpretability in scientific machine learning","ref_index":80,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2","json":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2.json","graph_json":"https://pith.science/api/pith-number/CA4BETYYLQPCPDFI65B2XIBHZ2/graph.json","events_json":"https://pith.science/api/pith-number/CA4BETYYLQPCPDFI65B2XIBHZ2/events.json","paper":"https://pith.science/paper/CA4BETYY"},"agent_actions":{"view_html":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2","download_json":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2.json","view_paper":"https://pith.science/paper/CA4BETYY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.04136&json=true","fetch_graph":"https://pith.science/api/pith-number/CA4BETYYLQPCPDFI65B2XIBHZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/CA4BETYYLQPCPDFI65B2XIBHZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2/action/storage_attestation","attest_author":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2/action/author_attestation","sign_citation":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2/action/citation_signature","submit_replication":"https://pith.science/pith/CA4BETYYLQPCPDFI65B2XIBHZ2/action/replication_record"}},"created_at":"2026-07-05T00:08:54.284487+00:00","updated_at":"2026-07-05T00:08:54.284487+00:00"}