{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:727J5ZNBADLJU3EVGBQATCEK4Y","short_pith_number":"pith:727J5ZNB","canonical_record":{"source":{"id":"2607.01004","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-07-01T14:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"b1777aa3aeae663a1d043e9c595ccbfe59b6e352f7f838d3a0b26cec24282218","abstract_canon_sha256":"cea3e4636a5ca29983d10d6b8beaeb5f39537e5da32f9f4743468101f74162d0"},"schema_version":"1.0"},"canonical_sha256":"febe9ee5a100d69a6c95306009888ae60db967bc46d690766360aeb3afc9790b","source":{"kind":"arxiv","id":"2607.01004","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.01004","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"arxiv_version","alias_value":"2607.01004v1","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.01004","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"pith_short_12","alias_value":"727J5ZNBADLJ","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"pith_short_16","alias_value":"727J5ZNBADLJU3EV","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"pith_short_8","alias_value":"727J5ZNB","created_at":"2026-07-02T01:18:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:727J5ZNBADLJU3EVGBQATCEK4Y","target":"record","payload":{"canonical_record":{"source":{"id":"2607.01004","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-07-01T14:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"b1777aa3aeae663a1d043e9c595ccbfe59b6e352f7f838d3a0b26cec24282218","abstract_canon_sha256":"cea3e4636a5ca29983d10d6b8beaeb5f39537e5da32f9f4743468101f74162d0"},"schema_version":"1.0"},"canonical_sha256":"febe9ee5a100d69a6c95306009888ae60db967bc46d690766360aeb3afc9790b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:18:25.753400Z","signature_b64":"RrA8ip0N2uQPSAIQcQqdLIHvyY3Y3Zj0ObYhpBC3V16s17H+YP5BLttJsYOapYSJZUTMIqZpseAOSpmNT+k5Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"febe9ee5a100d69a6c95306009888ae60db967bc46d690766360aeb3afc9790b","last_reissued_at":"2026-07-02T01:18:25.752997Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:18:25.752997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.01004","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":"2026-07-02T01:18:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2tKzdGNZMtJvOxdEna4foa1UprBCMGpIOcz1gNEGnLkwKeH2NhP3aA4VP2riL1qou4XspOSsvBlaLTFk4l3kBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:19:34.029133Z"},"content_sha256":"1657d5cd353085fb84974ff096620a913ddce872e86c4167e65c94d52432886b","schema_version":"1.0","event_id":"sha256:1657d5cd353085fb84974ff096620a913ddce872e86c4167e65c94d52432886b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:727J5ZNBADLJU3EVGBQATCEK4Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Complex crystal structure prediction using ML-enhanced multi-minima iterative genetic algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Cai-Zhuang Wang, Ling Tang, Paul C. Canfield, Tyler J. Slade, Weiyi Xia","submitted_at":"2026-07-01T14:44:13Z","abstract_excerpt":"Current machine learning (ML) approaches for materials discovery rely heavily on known structural databases, limiting their ability to identify entirely novel structure types. In this work, we develop a multi-minima iterative genetic algorithm (MMIGA) that integrates an artificial-neural-network machine learning (ANN-ML) interatomic potential with an iterative, metadynamics-inspired penalty scheme. We demonstrate the robustness of this method on a complex ternary La-Co-Pb system, characterized by Co-Pb immiscibility and an intricate energy landscape. The ML-enhanced MMIGA successfully predicts"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.01004","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/2607.01004/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":"2026-07-02T01:18:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oMK0qHPwGV+Fv2Z6BxJet1STpl0DcUtHDNE43zvofKxO5vdbrCBif509fElfFeyYGFT1NU4ZesWsvcbMAvlpBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:19:34.029523Z"},"content_sha256":"abc6e78a4c3c49a5b46aca3d1a4c92ab4a37b999a4114d39fdc443e3c4650d07","schema_version":"1.0","event_id":"sha256:abc6e78a4c3c49a5b46aca3d1a4c92ab4a37b999a4114d39fdc443e3c4650d07"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/727J5ZNBADLJU3EVGBQATCEK4Y/bundle.json","state_url":"https://pith.science/pith/727J5ZNBADLJU3EVGBQATCEK4Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/727J5ZNBADLJU3EVGBQATCEK4Y/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":"2026-08-06T14:19:34Z","links":{"resolver":"https://pith.science/pith/727J5ZNBADLJU3EVGBQATCEK4Y","bundle":"https://pith.science/pith/727J5ZNBADLJU3EVGBQATCEK4Y/bundle.json","state":"https://pith.science/pith/727J5ZNBADLJU3EVGBQATCEK4Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/727J5ZNBADLJU3EVGBQATCEK4Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:727J5ZNBADLJU3EVGBQATCEK4Y","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":"cea3e4636a5ca29983d10d6b8beaeb5f39537e5da32f9f4743468101f74162d0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-07-01T14:44:13Z","title_canon_sha256":"b1777aa3aeae663a1d043e9c595ccbfe59b6e352f7f838d3a0b26cec24282218"},"schema_version":"1.0","source":{"id":"2607.01004","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.01004","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"arxiv_version","alias_value":"2607.01004v1","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.01004","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"pith_short_12","alias_value":"727J5ZNBADLJ","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"pith_short_16","alias_value":"727J5ZNBADLJU3EV","created_at":"2026-07-02T01:18:25Z"},{"alias_kind":"pith_short_8","alias_value":"727J5ZNB","created_at":"2026-07-02T01:18:25Z"}],"graph_snapshots":[{"event_id":"sha256:abc6e78a4c3c49a5b46aca3d1a4c92ab4a37b999a4114d39fdc443e3c4650d07","target":"graph","created_at":"2026-07-02T01:18:25Z","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/2607.01004/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current machine learning (ML) approaches for materials discovery rely heavily on known structural databases, limiting their ability to identify entirely novel structure types. In this work, we develop a multi-minima iterative genetic algorithm (MMIGA) that integrates an artificial-neural-network machine learning (ANN-ML) interatomic potential with an iterative, metadynamics-inspired penalty scheme. We demonstrate the robustness of this method on a complex ternary La-Co-Pb system, characterized by Co-Pb immiscibility and an intricate energy landscape. The ML-enhanced MMIGA successfully predicts","authors_text":"Cai-Zhuang Wang, Ling Tang, Paul C. Canfield, Tyler J. Slade, Weiyi Xia","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-07-01T14:44:13Z","title":"Complex crystal structure prediction using ML-enhanced multi-minima iterative genetic algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.01004","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":"sha256:1657d5cd353085fb84974ff096620a913ddce872e86c4167e65c94d52432886b","target":"record","created_at":"2026-07-02T01:18:25Z","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":"cea3e4636a5ca29983d10d6b8beaeb5f39537e5da32f9f4743468101f74162d0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-07-01T14:44:13Z","title_canon_sha256":"b1777aa3aeae663a1d043e9c595ccbfe59b6e352f7f838d3a0b26cec24282218"},"schema_version":"1.0","source":{"id":"2607.01004","kind":"arxiv","version":1}},"canonical_sha256":"febe9ee5a100d69a6c95306009888ae60db967bc46d690766360aeb3afc9790b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"febe9ee5a100d69a6c95306009888ae60db967bc46d690766360aeb3afc9790b","first_computed_at":"2026-07-02T01:18:25.752997Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-02T01:18:25.752997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RrA8ip0N2uQPSAIQcQqdLIHvyY3Y3Zj0ObYhpBC3V16s17H+YP5BLttJsYOapYSJZUTMIqZpseAOSpmNT+k5Cw==","signature_status":"signed_v1","signed_at":"2026-07-02T01:18:25.753400Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.01004","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1657d5cd353085fb84974ff096620a913ddce872e86c4167e65c94d52432886b","sha256:abc6e78a4c3c49a5b46aca3d1a4c92ab4a37b999a4114d39fdc443e3c4650d07"],"state_sha256":"ecddeb059b5cef95c82d9ba8500de29ad36103982718075dd236293ebe2b8bf2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IQhkCNRtwNL4mKtLTWAn8sHlr3S5e83Ij2RE07DDVvYo4885Mirm70gpOL3ZxnKtqF6iIt3uNWWSbCb6zAZQCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T14:19:34.032068Z","bundle_sha256":"f95e97bbad8e31693caa02db21f6d3f4b9e4ab0f18aac67e29ec84db5fbbb734"}}