{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7ULEHL76PW3R6KZPJJEEQFUGJ3","short_pith_number":"pith:7ULEHL76","canonical_record":{"source":{"id":"2312.11487","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-11-30T15:56:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"35a41db313a295cca7ccfd3df3be281b2b380cc858f183406e27af583ecca046","abstract_canon_sha256":"14b68b6a1b732c628bbf21095b73e8ff2e367af6e621d5a8299c7bd3be9f67f0"},"schema_version":"1.0"},"canonical_sha256":"fd1643affe7db71f2b2f4a484816864eedf5af43d1e7280c19c13da667a82e90","source":{"kind":"arxiv","id":"2312.11487","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.11487","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"arxiv_version","alias_value":"2312.11487v1","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11487","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"pith_short_12","alias_value":"7ULEHL76PW3R","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"pith_short_16","alias_value":"7ULEHL76PW3R6KZP","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"pith_short_8","alias_value":"7ULEHL76","created_at":"2026-07-05T07:25:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7ULEHL76PW3R6KZPJJEEQFUGJ3","target":"record","payload":{"canonical_record":{"source":{"id":"2312.11487","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-11-30T15:56:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"35a41db313a295cca7ccfd3df3be281b2b380cc858f183406e27af583ecca046","abstract_canon_sha256":"14b68b6a1b732c628bbf21095b73e8ff2e367af6e621d5a8299c7bd3be9f67f0"},"schema_version":"1.0"},"canonical_sha256":"fd1643affe7db71f2b2f4a484816864eedf5af43d1e7280c19c13da667a82e90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:25:50.226170Z","signature_b64":"pD68pSGSFYFgM7jyrhPEK/kMhIpj0batLXIsjHEUWDo9yLBLn/wq2oCUCr2vAFFTqxBPjZv+dm37i/Gc2alqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd1643affe7db71f2b2f4a484816864eedf5af43d1e7280c19c13da667a82e90","last_reissued_at":"2026-07-05T07:25:50.225714Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:25:50.225714Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.11487","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:25:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cQUsOYBhf0iFCnQRhnonxhQsrQYB6ocjHPdBYvJBWfOrc9G+NZwUthkeP6AMbX6ueRD/s3mHZqHmAcKyQxTQAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:55:47.402943Z"},"content_sha256":"9a7e6392e3cd97474a53e352f310635b9395a0f73d51611c75389899c4facfd6","schema_version":"1.0","event_id":"sha256:9a7e6392e3cd97474a53e352f310635b9395a0f73d51611c75389899c4facfd6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7ULEHL76PW3R6KZPJJEEQFUGJ3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Symbolic Learning for Material Discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Daniel Cunnington, Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira","submitted_at":"2023-11-30T15:56:00Z","abstract_excerpt":"Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare. A typical task in materials discovery is to search for a material in a database which maximises the value of a function. That function is often expensive to evaluate, and can rely upon a simulation or an experiment. Here, we introduce SyMDis, a sample efficient optimisation method based on symbolic learning, that discovers near-optimal materials in a large database. SyMDis performs comparably to a state-of-the-art optimiser, whilst learning interpretable rules to aid physical and chemic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11487","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/2312.11487/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:25:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"haDRUOWwsGC5tUQUxuZiPL0Pc1xv5ysYNPMmAhfoo9Mngv0iB7FUncRKDK4u/A/gECTK6IlOt6Tu0c4g0EFVBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:55:47.403347Z"},"content_sha256":"adb2bbac0d61676140c8c68b9eba1c8937cba014915db90c25568b29a3797d7b","schema_version":"1.0","event_id":"sha256:adb2bbac0d61676140c8c68b9eba1c8937cba014915db90c25568b29a3797d7b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7ULEHL76PW3R6KZPJJEEQFUGJ3/bundle.json","state_url":"https://pith.science/pith/7ULEHL76PW3R6KZPJJEEQFUGJ3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7ULEHL76PW3R6KZPJJEEQFUGJ3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T07:55:47Z","links":{"resolver":"https://pith.science/pith/7ULEHL76PW3R6KZPJJEEQFUGJ3","bundle":"https://pith.science/pith/7ULEHL76PW3R6KZPJJEEQFUGJ3/bundle.json","state":"https://pith.science/pith/7ULEHL76PW3R6KZPJJEEQFUGJ3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7ULEHL76PW3R6KZPJJEEQFUGJ3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7ULEHL76PW3R6KZPJJEEQFUGJ3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"14b68b6a1b732c628bbf21095b73e8ff2e367af6e621d5a8299c7bd3be9f67f0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-11-30T15:56:00Z","title_canon_sha256":"35a41db313a295cca7ccfd3df3be281b2b380cc858f183406e27af583ecca046"},"schema_version":"1.0","source":{"id":"2312.11487","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.11487","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"arxiv_version","alias_value":"2312.11487v1","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11487","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"pith_short_12","alias_value":"7ULEHL76PW3R","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"pith_short_16","alias_value":"7ULEHL76PW3R6KZP","created_at":"2026-07-05T07:25:50Z"},{"alias_kind":"pith_short_8","alias_value":"7ULEHL76","created_at":"2026-07-05T07:25:50Z"}],"graph_snapshots":[{"event_id":"sha256:adb2bbac0d61676140c8c68b9eba1c8937cba014915db90c25568b29a3797d7b","target":"graph","created_at":"2026-07-05T07:25:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2312.11487/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare. A typical task in materials discovery is to search for a material in a database which maximises the value of a function. That function is often expensive to evaluate, and can rely upon a simulation or an experiment. Here, we introduce SyMDis, a sample efficient optimisation method based on symbolic learning, that discovers near-optimal materials in a large database. SyMDis performs comparably to a state-of-the-art optimiser, whilst learning interpretable rules to aid physical and chemic","authors_text":"Daniel Cunnington, Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-11-30T15:56:00Z","title":"Symbolic Learning for Material Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11487","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9a7e6392e3cd97474a53e352f310635b9395a0f73d51611c75389899c4facfd6","target":"record","created_at":"2026-07-05T07:25:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"14b68b6a1b732c628bbf21095b73e8ff2e367af6e621d5a8299c7bd3be9f67f0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-11-30T15:56:00Z","title_canon_sha256":"35a41db313a295cca7ccfd3df3be281b2b380cc858f183406e27af583ecca046"},"schema_version":"1.0","source":{"id":"2312.11487","kind":"arxiv","version":1}},"canonical_sha256":"fd1643affe7db71f2b2f4a484816864eedf5af43d1e7280c19c13da667a82e90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fd1643affe7db71f2b2f4a484816864eedf5af43d1e7280c19c13da667a82e90","first_computed_at":"2026-07-05T07:25:50.225714Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:25:50.225714Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pD68pSGSFYFgM7jyrhPEK/kMhIpj0batLXIsjHEUWDo9yLBLn/wq2oCUCr2vAFFTqxBPjZv+dm37i/Gc2alqCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:25:50.226170Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.11487","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a7e6392e3cd97474a53e352f310635b9395a0f73d51611c75389899c4facfd6","sha256:adb2bbac0d61676140c8c68b9eba1c8937cba014915db90c25568b29a3797d7b"],"state_sha256":"416bc4b263c9c03a92cbf59e028e9b610dded9152da2364de6d729f5a3e0c339"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G0N7F9/cWO7sLrlR2SJQDMOSzyQziaiHqdmAYw6a2hIdg6OwGXDcZ/X/xKMm6KaDb+qiSBZ87FfK5g9b9QLuAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:55:47.405604Z","bundle_sha256":"52dce2d7ab58e07330a4d8a4cc7bf7e9903cd104ec2993d854b522cd0fa323be"}}