{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XX73UTIZCHPQRGSVJHQTK3W7Y2","short_pith_number":"pith:XX73UTIZ","schema_version":"1.0","canonical_sha256":"bdffba4d1911df089a5549e1356edfc68e5a50ab7c9270427cd83e9ca8a0acce","source":{"kind":"arxiv","id":"2503.22304","version":1},"attestation_state":"computed","paper":{"title":"ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.chem-ph"],"primary_cat":"cond-mat.dis-nn","authors_text":"Aniruddha Panda, Kaushic Kalyanaraman, Stanislav Jaso, Subodh Madhav Joshi, Taras Demchuk, Tymofii Nikolaienko","submitted_at":"2025-03-28T10:26:52Z","abstract_excerpt":"This study introduces a physics-informed machine learning framework to accelerate the computation of the microkinetic model of Fischer-Tropsch synthesis. A neural network, trained within the NVIDIA Modulus framework, approximates the fraction of vacant catalytic sites with high accuracy. The combination of implicit differentiation and the Newton-Raphson method enhances derivative calculations, ensuring physical consistency. Computational efficiency improves significantly, with speedups up to $ 10^4 $ times on a GPU. This versatile methodology generalizes across catalysts and reactors, offering"},"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":"2503.22304","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2025-03-28T10:26:52Z","cross_cats_sorted":["physics.chem-ph"],"title_canon_sha256":"022024c38608efee37908c177dea212f2e23552c0fc8c92c1800b39de49678e8","abstract_canon_sha256":"0b9a3afa92089c66cbc4f3bd2d38c543e9d877fb826b73ea08c11ba6a60d9c93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:40:52.022631Z","signature_b64":"SYAXpknggrg0y86j+LS4kZPyp0jMqz9ninihxCJj+cEjBhuo06+Pv8TW9YVR8QsMYam/8zAvwi3mMl1lv16vBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdffba4d1911df089a5549e1356edfc68e5a50ab7c9270427cd83e9ca8a0acce","last_reissued_at":"2026-07-05T10:40:52.022121Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:40:52.022121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.chem-ph"],"primary_cat":"cond-mat.dis-nn","authors_text":"Aniruddha Panda, Kaushic Kalyanaraman, Stanislav Jaso, Subodh Madhav Joshi, Taras Demchuk, Tymofii Nikolaienko","submitted_at":"2025-03-28T10:26:52Z","abstract_excerpt":"This study introduces a physics-informed machine learning framework to accelerate the computation of the microkinetic model of Fischer-Tropsch synthesis. A neural network, trained within the NVIDIA Modulus framework, approximates the fraction of vacant catalytic sites with high accuracy. The combination of implicit differentiation and the Newton-Raphson method enhances derivative calculations, ensuring physical consistency. Computational efficiency improves significantly, with speedups up to $ 10^4 $ times on a GPU. This versatile methodology generalizes across catalysts and reactors, offering"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.22304","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/2503.22304/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":"2503.22304","created_at":"2026-07-05T10:40:52.022181+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.22304v1","created_at":"2026-07-05T10:40:52.022181+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22304","created_at":"2026-07-05T10:40:52.022181+00:00"},{"alias_kind":"pith_short_12","alias_value":"XX73UTIZCHPQ","created_at":"2026-07-05T10:40:52.022181+00:00"},{"alias_kind":"pith_short_16","alias_value":"XX73UTIZCHPQRGSV","created_at":"2026-07-05T10:40:52.022181+00:00"},{"alias_kind":"pith_short_8","alias_value":"XX73UTIZ","created_at":"2026-07-05T10:40:52.022181+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/XX73UTIZCHPQRGSVJHQTK3W7Y2","json":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2.json","graph_json":"https://pith.science/api/pith-number/XX73UTIZCHPQRGSVJHQTK3W7Y2/graph.json","events_json":"https://pith.science/api/pith-number/XX73UTIZCHPQRGSVJHQTK3W7Y2/events.json","paper":"https://pith.science/paper/XX73UTIZ"},"agent_actions":{"view_html":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2","download_json":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2.json","view_paper":"https://pith.science/paper/XX73UTIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.22304&json=true","fetch_graph":"https://pith.science/api/pith-number/XX73UTIZCHPQRGSVJHQTK3W7Y2/graph.json","fetch_events":"https://pith.science/api/pith-number/XX73UTIZCHPQRGSVJHQTK3W7Y2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2/action/storage_attestation","attest_author":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2/action/author_attestation","sign_citation":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2/action/citation_signature","submit_replication":"https://pith.science/pith/XX73UTIZCHPQRGSVJHQTK3W7Y2/action/replication_record"}},"created_at":"2026-07-05T10:40:52.022181+00:00","updated_at":"2026-07-05T10:40:52.022181+00:00"}