{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:XWJ7HHX56YCC53MVMD2I23K4ZL","short_pith_number":"pith:XWJ7HHX5","canonical_record":{"source":{"id":"2303.16597","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-03-29T11:18:15Z","cross_cats_sorted":[],"title_canon_sha256":"1342a134417d96177316653d851ecf125105315cd02edf7f8aef820d053f2427","abstract_canon_sha256":"de078167e62b382b49eeab6a34f03fa598a9d8199fb5cd7a59f3a577c28fb035"},"schema_version":"1.0"},"canonical_sha256":"bd93f39efdf6042eed9560f48d6d5ccae94702cac26e325610164c6478d2e273","source":{"kind":"arxiv","id":"2303.16597","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.16597","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"2303.16597v1","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.16597","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"XWJ7HHX56YCC","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"pith_short_16","alias_value":"XWJ7HHX56YCC53MV","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"pith_short_8","alias_value":"XWJ7HHX5","created_at":"2026-07-05T06:51:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:XWJ7HHX56YCC53MVMD2I23K4ZL","target":"record","payload":{"canonical_record":{"source":{"id":"2303.16597","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-03-29T11:18:15Z","cross_cats_sorted":[],"title_canon_sha256":"1342a134417d96177316653d851ecf125105315cd02edf7f8aef820d053f2427","abstract_canon_sha256":"de078167e62b382b49eeab6a34f03fa598a9d8199fb5cd7a59f3a577c28fb035"},"schema_version":"1.0"},"canonical_sha256":"bd93f39efdf6042eed9560f48d6d5ccae94702cac26e325610164c6478d2e273","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:51:24.029356Z","signature_b64":"nRzSFfax+6H7RpzuwUM+PbauQrS5dRqFa0PO2CbpPhmBWEFviJnJgolcXAtpgBc02FTHk07nh66VoxrvaQZkAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd93f39efdf6042eed9560f48d6d5ccae94702cac26e325610164c6478d2e273","last_reissued_at":"2026-07-05T06:51:24.028955Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:51:24.028955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.16597","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":"2026-07-05T06:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7X/B/qw7c0DqZi5v0ZFsU7nY4hy/Jjeptq8h3Z4MjP9mErTkW8as7StXBe9AUz3izXJo03B+DyXQtzV+8J/BCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:03:00.381999Z"},"content_sha256":"b2a9777cf606f228d36e84a3e10dc7632bab6bc2d93d1785ea5e9fe371369b6f","schema_version":"1.0","event_id":"sha256:b2a9777cf606f228d36e84a3e10dc7632bab6bc2d93d1785ea5e9fe371369b6f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:XWJ7HHX56YCC53MVMD2I23K4ZL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Hugo Rossignol, Matteo Cobelli, Michael Minotakis, Stefano Sanvito","submitted_at":"2023-03-29T11:18:15Z","abstract_excerpt":"The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semi-local functionals. Here, we explore the possibility of integrating machine-learning models in the workflow for the construction of ternary convex hull diagrams. In particular, we train a set of spectral neighbour-analysis potentials (SNAPs) over readily available binary phases and we e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.16597","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/2303.16597/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":"2026-07-05T06:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iS0VqxZATmsCm/sywyWwpN2M928cA7HHzPB+A4xNL4SEQ64p4x9G+DwCSLaRfcqcvOVvai6vy0Ngrv6PXyaoAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:03:00.382706Z"},"content_sha256":"b9e00c8191c140e765f9d3c5ec3d72e1bf8ecca78643a76cda9b5fee863bb2f7","schema_version":"1.0","event_id":"sha256:b9e00c8191c140e765f9d3c5ec3d72e1bf8ecca78643a76cda9b5fee863bb2f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XWJ7HHX56YCC53MVMD2I23K4ZL/bundle.json","state_url":"https://pith.science/pith/XWJ7HHX56YCC53MVMD2I23K4ZL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XWJ7HHX56YCC53MVMD2I23K4ZL/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":"2026-08-12T12:03:00Z","links":{"resolver":"https://pith.science/pith/XWJ7HHX56YCC53MVMD2I23K4ZL","bundle":"https://pith.science/pith/XWJ7HHX56YCC53MVMD2I23K4ZL/bundle.json","state":"https://pith.science/pith/XWJ7HHX56YCC53MVMD2I23K4ZL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XWJ7HHX56YCC53MVMD2I23K4ZL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:XWJ7HHX56YCC53MVMD2I23K4ZL","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":"de078167e62b382b49eeab6a34f03fa598a9d8199fb5cd7a59f3a577c28fb035","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-03-29T11:18:15Z","title_canon_sha256":"1342a134417d96177316653d851ecf125105315cd02edf7f8aef820d053f2427"},"schema_version":"1.0","source":{"id":"2303.16597","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.16597","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"2303.16597v1","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.16597","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"XWJ7HHX56YCC","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"pith_short_16","alias_value":"XWJ7HHX56YCC53MV","created_at":"2026-07-05T06:51:24Z"},{"alias_kind":"pith_short_8","alias_value":"XWJ7HHX5","created_at":"2026-07-05T06:51:24Z"}],"graph_snapshots":[{"event_id":"sha256:b9e00c8191c140e765f9d3c5ec3d72e1bf8ecca78643a76cda9b5fee863bb2f7","target":"graph","created_at":"2026-07-05T06:51:24Z","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/2303.16597/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semi-local functionals. Here, we explore the possibility of integrating machine-learning models in the workflow for the construction of ternary convex hull diagrams. In particular, we train a set of spectral neighbour-analysis potentials (SNAPs) over readily available binary phases and we e","authors_text":"Hugo Rossignol, Matteo Cobelli, Michael Minotakis, Stefano Sanvito","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-03-29T11:18:15Z","title":"Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.16597","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:b2a9777cf606f228d36e84a3e10dc7632bab6bc2d93d1785ea5e9fe371369b6f","target":"record","created_at":"2026-07-05T06:51:24Z","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":"de078167e62b382b49eeab6a34f03fa598a9d8199fb5cd7a59f3a577c28fb035","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2023-03-29T11:18:15Z","title_canon_sha256":"1342a134417d96177316653d851ecf125105315cd02edf7f8aef820d053f2427"},"schema_version":"1.0","source":{"id":"2303.16597","kind":"arxiv","version":1}},"canonical_sha256":"bd93f39efdf6042eed9560f48d6d5ccae94702cac26e325610164c6478d2e273","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bd93f39efdf6042eed9560f48d6d5ccae94702cac26e325610164c6478d2e273","first_computed_at":"2026-07-05T06:51:24.028955Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:51:24.028955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nRzSFfax+6H7RpzuwUM+PbauQrS5dRqFa0PO2CbpPhmBWEFviJnJgolcXAtpgBc02FTHk07nh66VoxrvaQZkAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:51:24.029356Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.16597","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b2a9777cf606f228d36e84a3e10dc7632bab6bc2d93d1785ea5e9fe371369b6f","sha256:b9e00c8191c140e765f9d3c5ec3d72e1bf8ecca78643a76cda9b5fee863bb2f7"],"state_sha256":"6ffd885f202ec70310fbabd160cfc6426a22cab9aa75e276f7ff1e1f87a2329e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uqnwUTvx3CEdquZ7oDdxmHHB+a3eg5CklUcn3eBP9yHYcnguY+WoGmhm6K015fftAMF4EQg9F2gdZBB5xgQ/Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T12:03:00.386783Z","bundle_sha256":"445ec33163d84a423daa8a700bddd8df346afbf6ef8756cce8e3fba62fbaa16f"}}