{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LTFHQNFMPCOPSZ3RLMCECFEYGI","short_pith_number":"pith:LTFHQNFM","schema_version":"1.0","canonical_sha256":"5cca7834ac789cf967715b04411498323f431b7815cff20a74b7826395f4552c","source":{"kind":"arxiv","id":"2211.13408","version":1},"attestation_state":"computed","paper":{"title":"Graph Contrastive Learning for Materials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Keegan Quigley, Lin Li, Nathan C. Frey, Teddy Koker, Will Spaeth","submitted_at":"2022-11-24T04:15:47Z","abstract_excerpt":"Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks. With the addition of a novel loss function, our framework is able to learn representations competi"},"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":"2211.13408","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-24T04:15:47Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"6db72566f902b638f36e8713ba09983ddcfe438f9e4e82398362f933e40f380f","abstract_canon_sha256":"2f3ee1129c430e851bd0a721faa42303b632859b986971f2361f77e570489ee9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:19:03.239231Z","signature_b64":"ZZmcZZ/KmNKEZRK271JpSy57EN6pWWCqxmnd9vtcvWJJWA91RC2VKkWuNvKh9zmSXV59YjwtI1djwcFsVbI0CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cca7834ac789cf967715b04411498323f431b7815cff20a74b7826395f4552c","last_reissued_at":"2026-07-05T05:19:03.238770Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:19:03.238770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Contrastive Learning for Materials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Keegan Quigley, Lin Li, Nathan C. Frey, Teddy Koker, Will Spaeth","submitted_at":"2022-11-24T04:15:47Z","abstract_excerpt":"Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks. With the addition of a novel loss function, our framework is able to learn representations competi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13408","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/2211.13408/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":"2211.13408","created_at":"2026-07-05T05:19:03.238827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.13408v1","created_at":"2026-07-05T05:19:03.238827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13408","created_at":"2026-07-05T05:19:03.238827+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTFHQNFMPCOP","created_at":"2026-07-05T05:19:03.238827+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTFHQNFMPCOPSZ3R","created_at":"2026-07-05T05:19:03.238827+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTFHQNFM","created_at":"2026-07-05T05:19:03.238827+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2503.10471","citing_title":"Siamese Foundation Models for Crystal Structure Prediction","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI","json":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI.json","graph_json":"https://pith.science/api/pith-number/LTFHQNFMPCOPSZ3RLMCECFEYGI/graph.json","events_json":"https://pith.science/api/pith-number/LTFHQNFMPCOPSZ3RLMCECFEYGI/events.json","paper":"https://pith.science/paper/LTFHQNFM"},"agent_actions":{"view_html":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI","download_json":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI.json","view_paper":"https://pith.science/paper/LTFHQNFM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.13408&json=true","fetch_graph":"https://pith.science/api/pith-number/LTFHQNFMPCOPSZ3RLMCECFEYGI/graph.json","fetch_events":"https://pith.science/api/pith-number/LTFHQNFMPCOPSZ3RLMCECFEYGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI/action/storage_attestation","attest_author":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI/action/author_attestation","sign_citation":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI/action/citation_signature","submit_replication":"https://pith.science/pith/LTFHQNFMPCOPSZ3RLMCECFEYGI/action/replication_record"}},"created_at":"2026-07-05T05:19:03.238827+00:00","updated_at":"2026-07-05T05:19:03.238827+00:00"}