{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UXNTCANQ4Z3O76Z2NIAESSTEWW","short_pith_number":"pith:UXNTCANQ","schema_version":"1.0","canonical_sha256":"a5db3101b0e676effb3a6a00494a64b589c15d9e972ccd9de1e19225960cc802","source":{"kind":"arxiv","id":"2106.01309","version":1},"attestation_state":"computed","paper":{"title":"Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.data-an"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Aldair E. Gongora, Armi Tiihonen, Benji Maruyama, Daniil Bash, Flore Mekki-Berrada, James R. Deneault, John Fisher III, Kedar Hippalgaonkar, Keith A. Brown, Qiaohao Liang, Saif A. Khan, Shijing Sun, Tonio Buonassisi, Zekun Ren, Zhe Liu","submitted_at":"2021-05-23T22:04:07Z","abstract_excerpt":"In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, si"},"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":"2106.01309","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2021-05-23T22:04:07Z","cross_cats_sorted":["cs.LG","physics.data-an"],"title_canon_sha256":"831dcc18bc45a39795bfe2b8c0e6c5679b717cec1703c10378ae14acec4e5e18","abstract_canon_sha256":"fd06e0d8df749569b056f00d18edd2b228a4910ba335819aad1947c5360e63df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:45:45.902833Z","signature_b64":"62K1vw0omsYlJGknFZ4+eZobiuDOnd2TTX5mV+LLxpYSYcBdBRW0IZT2hisT1ahw/EOR9xn9tDCccbhA/6xuAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5db3101b0e676effb3a6a00494a64b589c15d9e972ccd9de1e19225960cc802","last_reissued_at":"2026-07-05T02:45:45.902404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:45:45.902404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.data-an"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Aldair E. Gongora, Armi Tiihonen, Benji Maruyama, Daniil Bash, Flore Mekki-Berrada, James R. Deneault, John Fisher III, Kedar Hippalgaonkar, Keith A. Brown, Qiaohao Liang, Saif A. Khan, Shijing Sun, Tonio Buonassisi, Zekun Ren, Zhe Liu","submitted_at":"2021-05-23T22:04:07Z","abstract_excerpt":"In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.01309","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/2106.01309/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":"2106.01309","created_at":"2026-07-05T02:45:45.902474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.01309v1","created_at":"2026-07-05T02:45:45.902474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.01309","created_at":"2026-07-05T02:45:45.902474+00:00"},{"alias_kind":"pith_short_12","alias_value":"UXNTCANQ4Z3O","created_at":"2026-07-05T02:45:45.902474+00:00"},{"alias_kind":"pith_short_16","alias_value":"UXNTCANQ4Z3O76Z2","created_at":"2026-07-05T02:45:45.902474+00:00"},{"alias_kind":"pith_short_8","alias_value":"UXNTCANQ","created_at":"2026-07-05T02:45:45.902474+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/UXNTCANQ4Z3O76Z2NIAESSTEWW","json":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW.json","graph_json":"https://pith.science/api/pith-number/UXNTCANQ4Z3O76Z2NIAESSTEWW/graph.json","events_json":"https://pith.science/api/pith-number/UXNTCANQ4Z3O76Z2NIAESSTEWW/events.json","paper":"https://pith.science/paper/UXNTCANQ"},"agent_actions":{"view_html":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW","download_json":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW.json","view_paper":"https://pith.science/paper/UXNTCANQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.01309&json=true","fetch_graph":"https://pith.science/api/pith-number/UXNTCANQ4Z3O76Z2NIAESSTEWW/graph.json","fetch_events":"https://pith.science/api/pith-number/UXNTCANQ4Z3O76Z2NIAESSTEWW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW/action/storage_attestation","attest_author":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW/action/author_attestation","sign_citation":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW/action/citation_signature","submit_replication":"https://pith.science/pith/UXNTCANQ4Z3O76Z2NIAESSTEWW/action/replication_record"}},"created_at":"2026-07-05T02:45:45.902474+00:00","updated_at":"2026-07-05T02:45:45.902474+00:00"}