{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4YE2MMSBPVI7NXXUCTBZB2AZ65","short_pith_number":"pith:4YE2MMSB","schema_version":"1.0","canonical_sha256":"e609a632417d51f6def414c390e819f7628727e4da7f38b7527be34b3e4b7585","source":{"kind":"arxiv","id":"2501.16051","version":2},"attestation_state":"computed","paper":{"title":"A generative material transformer using Wyckoff representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Gian-Marco Rignanese, Hashim A. Piracha, Miguel A. L. Marques, Pierre-Paul De Breuck","submitted_at":"2025-01-27T13:46:00Z","abstract_excerpt":"Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally expensive, or lack symmetry. In this work we present Matra-Genoa, an autoregressive transformer model built on invertible tokenized representations of symmetrized crystals, including free coordinates. This approach enables sampling from a hybrid action space. The model is trained across the periodic table and spa"},"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":"2501.16051","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-01-27T13:46:00Z","cross_cats_sorted":[],"title_canon_sha256":"6aad9630ee95706bce715b8cba6e949f567ae91a924a7718541aab138e27e131","abstract_canon_sha256":"ca0208d1440052259e58e4aa97ad7352e7b8ce60ba3588e3d1fc3901b99eaa05"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:47.838696Z","signature_b64":"BxPaODq5yGhZhBh8DP6/gDA9P/5H4R5rOtp1teDQ7PMbUkEW8kTroVVoZGj0gBN20J+hrqDGDJoUUYh0IfZ+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e609a632417d51f6def414c390e819f7628727e4da7f38b7527be34b3e4b7585","last_reissued_at":"2026-07-05T10:06:47.838227Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:47.838227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A generative material transformer using Wyckoff representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Gian-Marco Rignanese, Hashim A. Piracha, Miguel A. L. Marques, Pierre-Paul De Breuck","submitted_at":"2025-01-27T13:46:00Z","abstract_excerpt":"Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally expensive, or lack symmetry. In this work we present Matra-Genoa, an autoregressive transformer model built on invertible tokenized representations of symmetrized crystals, including free coordinates. This approach enables sampling from a hybrid action space. The model is trained across the periodic table and spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16051","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/2501.16051/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":"2501.16051","created_at":"2026-07-05T10:06:47.838283+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.16051v2","created_at":"2026-07-05T10:06:47.838283+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16051","created_at":"2026-07-05T10:06:47.838283+00:00"},{"alias_kind":"pith_short_12","alias_value":"4YE2MMSBPVI7","created_at":"2026-07-05T10:06:47.838283+00:00"},{"alias_kind":"pith_short_16","alias_value":"4YE2MMSBPVI7NXXU","created_at":"2026-07-05T10:06:47.838283+00:00"},{"alias_kind":"pith_short_8","alias_value":"4YE2MMSB","created_at":"2026-07-05T10:06:47.838283+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.27709","citing_title":"Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65","json":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65.json","graph_json":"https://pith.science/api/pith-number/4YE2MMSBPVI7NXXUCTBZB2AZ65/graph.json","events_json":"https://pith.science/api/pith-number/4YE2MMSBPVI7NXXUCTBZB2AZ65/events.json","paper":"https://pith.science/paper/4YE2MMSB"},"agent_actions":{"view_html":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65","download_json":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65.json","view_paper":"https://pith.science/paper/4YE2MMSB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.16051&json=true","fetch_graph":"https://pith.science/api/pith-number/4YE2MMSBPVI7NXXUCTBZB2AZ65/graph.json","fetch_events":"https://pith.science/api/pith-number/4YE2MMSBPVI7NXXUCTBZB2AZ65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65/action/storage_attestation","attest_author":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65/action/author_attestation","sign_citation":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65/action/citation_signature","submit_replication":"https://pith.science/pith/4YE2MMSBPVI7NXXUCTBZB2AZ65/action/replication_record"}},"created_at":"2026-07-05T10:06:47.838283+00:00","updated_at":"2026-07-05T10:06:47.838283+00:00"}