{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:EQNKHU6KI7K6MUPBEYANCYSX4I","short_pith_number":"pith:EQNKHU6K","canonical_record":{"source":{"id":"2310.16802","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:32:23Z","cross_cats_sorted":[],"title_canon_sha256":"23ac0047e68178bc3e09b7710629a51360b69af10e2af66de8ed51e6c7786e26","abstract_canon_sha256":"4d4eff12bd720406cd98600e4fa99f6092c6ee41c30e60893e58a9a3ff40b221"},"schema_version":"1.0"},"canonical_sha256":"241aa3d3ca47d5e651e12600d16257e22b3ab56f913208b1092ce6164f393070","source":{"kind":"arxiv","id":"2310.16802","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.16802","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"arxiv_version","alias_value":"2310.16802v2","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.16802","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"pith_short_12","alias_value":"EQNKHU6KI7K6","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"pith_short_16","alias_value":"EQNKHU6KI7K6MUPB","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"pith_short_8","alias_value":"EQNKHU6K","created_at":"2026-07-05T08:15:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:EQNKHU6KI7K6MUPBEYANCYSX4I","target":"record","payload":{"canonical_record":{"source":{"id":"2310.16802","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:32:23Z","cross_cats_sorted":[],"title_canon_sha256":"23ac0047e68178bc3e09b7710629a51360b69af10e2af66de8ed51e6c7786e26","abstract_canon_sha256":"4d4eff12bd720406cd98600e4fa99f6092c6ee41c30e60893e58a9a3ff40b221"},"schema_version":"1.0"},"canonical_sha256":"241aa3d3ca47d5e651e12600d16257e22b3ab56f913208b1092ce6164f393070","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:15:40.972603Z","signature_b64":"oRbCC9aKFyNdTwweNthg1sVtOJ/QyYe6yQ/rT7w8pGnHrjv/+aQ6mqjAw1UWs5suXvCJ+Fy/eMzvgYqxRocWAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"241aa3d3ca47d5e651e12600d16257e22b3ab56f913208b1092ce6164f393070","last_reissued_at":"2026-07-05T08:15:40.972096Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:15:40.972096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.16802","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:15:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5NHoTzdN9XbPyHJ51uWeOusBtEBM4DPkuE8FOvTX9Q4lkoB3xq6HqS7ebgKQHoanfYv6Wn5Gx5jR6/2ADFxhDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:10:34.248287Z"},"content_sha256":"633aab8b0674dccab96bce949e8d7f6891953ab4bbb08684f97c559d74cce07a","schema_version":"1.0","event_id":"sha256:633aab8b0674dccab96bce949e8d7f6891953ab4bbb08684f97c559d74cce07a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:EQNKHU6KI7K6MUPBEYANCYSX4I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adeesh Kolluru, Brandon M. Wood, C. Lawrence Zitnick, John R. Kitchin, Nima Shoghi, Zachary W. Ulissi","submitted_at":"2023-10-25T17:32:23Z","abstract_excerpt":"Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\\sim$120M sy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.16802","kind":"arxiv","version":2},"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/2310.16802/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":"2026-07-05T08:15:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5z5aYSh1irfPYmgSvKpuqVx8/d8zNVgeeOTxTQpNQ3wasLcPUB2NqAuhWic2ItkhBzjAl/eILw7TMsZkq7d3DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:10:34.249644Z"},"content_sha256":"31d848a820ee51d366ce6169245f03bbd114f4f9b345b5c82edfb2d5556372fa","schema_version":"1.0","event_id":"sha256:31d848a820ee51d366ce6169245f03bbd114f4f9b345b5c82edfb2d5556372fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EQNKHU6KI7K6MUPBEYANCYSX4I/bundle.json","state_url":"https://pith.science/pith/EQNKHU6KI7K6MUPBEYANCYSX4I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EQNKHU6KI7K6MUPBEYANCYSX4I/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":"2026-08-05T14:10:34Z","links":{"resolver":"https://pith.science/pith/EQNKHU6KI7K6MUPBEYANCYSX4I","bundle":"https://pith.science/pith/EQNKHU6KI7K6MUPBEYANCYSX4I/bundle.json","state":"https://pith.science/pith/EQNKHU6KI7K6MUPBEYANCYSX4I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EQNKHU6KI7K6MUPBEYANCYSX4I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:EQNKHU6KI7K6MUPBEYANCYSX4I","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":"4d4eff12bd720406cd98600e4fa99f6092c6ee41c30e60893e58a9a3ff40b221","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:32:23Z","title_canon_sha256":"23ac0047e68178bc3e09b7710629a51360b69af10e2af66de8ed51e6c7786e26"},"schema_version":"1.0","source":{"id":"2310.16802","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.16802","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"arxiv_version","alias_value":"2310.16802v2","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.16802","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"pith_short_12","alias_value":"EQNKHU6KI7K6","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"pith_short_16","alias_value":"EQNKHU6KI7K6MUPB","created_at":"2026-07-05T08:15:40Z"},{"alias_kind":"pith_short_8","alias_value":"EQNKHU6K","created_at":"2026-07-05T08:15:40Z"}],"graph_snapshots":[{"event_id":"sha256:31d848a820ee51d366ce6169245f03bbd114f4f9b345b5c82edfb2d5556372fa","target":"graph","created_at":"2026-07-05T08:15:40Z","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/2310.16802/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\\sim$120M sy","authors_text":"Adeesh Kolluru, Brandon M. Wood, C. Lawrence Zitnick, John R. Kitchin, Nima Shoghi, Zachary W. Ulissi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:32:23Z","title":"From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.16802","kind":"arxiv","version":2},"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":"sha256:633aab8b0674dccab96bce949e8d7f6891953ab4bbb08684f97c559d74cce07a","target":"record","created_at":"2026-07-05T08:15:40Z","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":"4d4eff12bd720406cd98600e4fa99f6092c6ee41c30e60893e58a9a3ff40b221","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:32:23Z","title_canon_sha256":"23ac0047e68178bc3e09b7710629a51360b69af10e2af66de8ed51e6c7786e26"},"schema_version":"1.0","source":{"id":"2310.16802","kind":"arxiv","version":2}},"canonical_sha256":"241aa3d3ca47d5e651e12600d16257e22b3ab56f913208b1092ce6164f393070","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"241aa3d3ca47d5e651e12600d16257e22b3ab56f913208b1092ce6164f393070","first_computed_at":"2026-07-05T08:15:40.972096Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:15:40.972096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oRbCC9aKFyNdTwweNthg1sVtOJ/QyYe6yQ/rT7w8pGnHrjv/+aQ6mqjAw1UWs5suXvCJ+Fy/eMzvgYqxRocWAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:15:40.972603Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.16802","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:633aab8b0674dccab96bce949e8d7f6891953ab4bbb08684f97c559d74cce07a","sha256:31d848a820ee51d366ce6169245f03bbd114f4f9b345b5c82edfb2d5556372fa"],"state_sha256":"e9ce1730704499173f2165969806f2d64dafd371a938177a71eba60318cdff06"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y4rDwMAR5DYQ0nRxbnp9EREttVzTRa8zXiMJymdRZ08nJMZmDYDqAQOjwjEMqMFHlUhic1nXEyPX6ottcqNnDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:10:34.258209Z","bundle_sha256":"b1fd39ac09aa5f733f26864f3f38ce3dc0f0270445c1aab48a88336f7b6dcccb"}}