{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NV7F2HMKCLQITCBKDFJL74UNRZ","short_pith_number":"pith:NV7F2HMK","canonical_record":{"source":{"id":"2206.09944","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2022-06-20T18:04:29Z","cross_cats_sorted":[],"title_canon_sha256":"6aa9f4a3c83ab9f9da28fc50bb2f65bccdff1b78829182c372631de3b1c789ac","abstract_canon_sha256":"b78bece95eb68585e23f7714fcca43e238ed468766d20ed58002433bc044f2d9"},"schema_version":"1.0"},"canonical_sha256":"6d7e5d1d8a12e089882a1952bff28d8e65b3b1cbf7643434323eebbcb854c7fb","source":{"kind":"arxiv","id":"2206.09944","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.09944","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"2206.09944v1","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.09944","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"NV7F2HMKCLQI","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"pith_short_16","alias_value":"NV7F2HMKCLQITCBK","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"pith_short_8","alias_value":"NV7F2HMK","created_at":"2026-07-05T05:01:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NV7F2HMKCLQITCBKDFJL74UNRZ","target":"record","payload":{"canonical_record":{"source":{"id":"2206.09944","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2022-06-20T18:04:29Z","cross_cats_sorted":[],"title_canon_sha256":"6aa9f4a3c83ab9f9da28fc50bb2f65bccdff1b78829182c372631de3b1c789ac","abstract_canon_sha256":"b78bece95eb68585e23f7714fcca43e238ed468766d20ed58002433bc044f2d9"},"schema_version":"1.0"},"canonical_sha256":"6d7e5d1d8a12e089882a1952bff28d8e65b3b1cbf7643434323eebbcb854c7fb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:01:25.811298Z","signature_b64":"rsW6XAZdtCCXxehn8YqTAu035UqoASKaeeZx1RwJfYTM4Q3YMZ4t4VPN9ibFnDueqpyg7ag+IW09IMo9NvaYCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d7e5d1d8a12e089882a1952bff28d8e65b3b1cbf7643434323eebbcb854c7fb","last_reissued_at":"2026-07-05T05:01:25.810838Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:01:25.810838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.09944","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":"2026-07-05T05:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h3VXPd7zNwWwOefhPYKt88Vn5dcwunSweAdTIIrF1IC4IoPRkwwQ5ftW3wnlfoIhhlQEW04s0gphOh+lN5lPCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T07:24:00.093981Z"},"content_sha256":"fe37016a498083abf3872b72bb25096dd63b54399a286d25713fe5392fb65a13","schema_version":"1.0","event_id":"sha256:fe37016a498083abf3872b72bb25096dd63b54399a286d25713fe5392fb65a13"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NV7F2HMKCLQITCBKDFJL74UNRZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Integrating Machine Learning with Mechanistic Models for Predicting the Yield Strength of High Entropy Alloys","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Kyungtae Lee, Prasanna V. Balachandran, Shunshun Liu","submitted_at":"2022-06-20T18:04:29Z","abstract_excerpt":"Accelerating the design of materials with targeted properties is one of the key materials informatics tasks. The most common approach takes a data-driven motivation, where the underlying knowledge is incorporated in the form of domain-inspired input features. Machine learning (ML) models are then built to establish the input-output relationships. An alternative approach involves leveraging mechanistic models, where the domain knowledge is incorporated in a predefined functional form. These mechanistic models are meticulously formulated through observations to validate specific hypotheses, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.09944","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/2206.09944/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":"2026-07-05T05:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ouR7cDL0Tws9dWbDsT4hqpO4wQ0K0WlxyqqzjmJHwOll0xB6343SJPk2Dsbbk0ruKNvTQ+j6paA+wecrjHUBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T07:24:00.094352Z"},"content_sha256":"700ac98932b18817b3615245b9a9c180d1f861fcda98152f88b72ca4923ccc4e","schema_version":"1.0","event_id":"sha256:700ac98932b18817b3615245b9a9c180d1f861fcda98152f88b72ca4923ccc4e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NV7F2HMKCLQITCBKDFJL74UNRZ/bundle.json","state_url":"https://pith.science/pith/NV7F2HMKCLQITCBKDFJL74UNRZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NV7F2HMKCLQITCBKDFJL74UNRZ/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":"2026-07-10T07:24:00Z","links":{"resolver":"https://pith.science/pith/NV7F2HMKCLQITCBKDFJL74UNRZ","bundle":"https://pith.science/pith/NV7F2HMKCLQITCBKDFJL74UNRZ/bundle.json","state":"https://pith.science/pith/NV7F2HMKCLQITCBKDFJL74UNRZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NV7F2HMKCLQITCBKDFJL74UNRZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NV7F2HMKCLQITCBKDFJL74UNRZ","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":"b78bece95eb68585e23f7714fcca43e238ed468766d20ed58002433bc044f2d9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2022-06-20T18:04:29Z","title_canon_sha256":"6aa9f4a3c83ab9f9da28fc50bb2f65bccdff1b78829182c372631de3b1c789ac"},"schema_version":"1.0","source":{"id":"2206.09944","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.09944","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"2206.09944v1","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.09944","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"NV7F2HMKCLQI","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"pith_short_16","alias_value":"NV7F2HMKCLQITCBK","created_at":"2026-07-05T05:01:25Z"},{"alias_kind":"pith_short_8","alias_value":"NV7F2HMK","created_at":"2026-07-05T05:01:25Z"}],"graph_snapshots":[{"event_id":"sha256:700ac98932b18817b3615245b9a9c180d1f861fcda98152f88b72ca4923ccc4e","target":"graph","created_at":"2026-07-05T05:01:25Z","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/2206.09944/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accelerating the design of materials with targeted properties is one of the key materials informatics tasks. The most common approach takes a data-driven motivation, where the underlying knowledge is incorporated in the form of domain-inspired input features. Machine learning (ML) models are then built to establish the input-output relationships. An alternative approach involves leveraging mechanistic models, where the domain knowledge is incorporated in a predefined functional form. These mechanistic models are meticulously formulated through observations to validate specific hypotheses, and ","authors_text":"Kyungtae Lee, Prasanna V. Balachandran, Shunshun Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2022-06-20T18:04:29Z","title":"Integrating Machine Learning with Mechanistic Models for Predicting the Yield Strength of High Entropy Alloys"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.09944","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:fe37016a498083abf3872b72bb25096dd63b54399a286d25713fe5392fb65a13","target":"record","created_at":"2026-07-05T05:01:25Z","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":"b78bece95eb68585e23f7714fcca43e238ed468766d20ed58002433bc044f2d9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2022-06-20T18:04:29Z","title_canon_sha256":"6aa9f4a3c83ab9f9da28fc50bb2f65bccdff1b78829182c372631de3b1c789ac"},"schema_version":"1.0","source":{"id":"2206.09944","kind":"arxiv","version":1}},"canonical_sha256":"6d7e5d1d8a12e089882a1952bff28d8e65b3b1cbf7643434323eebbcb854c7fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d7e5d1d8a12e089882a1952bff28d8e65b3b1cbf7643434323eebbcb854c7fb","first_computed_at":"2026-07-05T05:01:25.810838Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:01:25.810838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rsW6XAZdtCCXxehn8YqTAu035UqoASKaeeZx1RwJfYTM4Q3YMZ4t4VPN9ibFnDueqpyg7ag+IW09IMo9NvaYCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:01:25.811298Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.09944","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fe37016a498083abf3872b72bb25096dd63b54399a286d25713fe5392fb65a13","sha256:700ac98932b18817b3615245b9a9c180d1f861fcda98152f88b72ca4923ccc4e"],"state_sha256":"b27d58ea0c4a170039676d3f5c0073de1770fc8c615c47f61c664308c916feea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SkqALyGlMPv771X3dNe1crX8VB3YYBKJSTYBW5YoKN09UURpTE2lxI8LIxBFBfwSWwnrYSO8F1ivCX+SYc40Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T07:24:00.096394Z","bundle_sha256":"2adc3989b8f65856b8e8812ca38fbf21cab868c60da4a062e3b0abed15255fbc"}}