{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:RLIEQBW2ZLCOU4GSBEB3UB2XEL","short_pith_number":"pith:RLIEQBW2","canonical_record":{"source":{"id":"1807.03404","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-07-09T22:06:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a4b1e2eeae10244ea07dab88e181970413121c423dde28da1fefd7b9970e06db","abstract_canon_sha256":"f834daf9e1aa492370440f3482e6384e82734dfe2f019a32b90103c13b4bb4a8"},"schema_version":"1.0"},"canonical_sha256":"8ad04806dacac4ea70d20903ba075722e1210475e693a165fff53c83105079e3","source":{"kind":"arxiv","id":"1807.03404","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03404","created_at":"2026-05-18T00:01:14Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03404v2","created_at":"2026-05-18T00:01:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03404","created_at":"2026-05-18T00:01:14Z"},{"alias_kind":"pith_short_12","alias_value":"RLIEQBW2ZLCO","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RLIEQBW2ZLCOU4GS","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RLIEQBW2","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:RLIEQBW2ZLCOU4GSBEB3UB2XEL","target":"record","payload":{"canonical_record":{"source":{"id":"1807.03404","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-07-09T22:06:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a4b1e2eeae10244ea07dab88e181970413121c423dde28da1fefd7b9970e06db","abstract_canon_sha256":"f834daf9e1aa492370440f3482e6384e82734dfe2f019a32b90103c13b4bb4a8"},"schema_version":"1.0"},"canonical_sha256":"8ad04806dacac4ea70d20903ba075722e1210475e693a165fff53c83105079e3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:14.811199Z","signature_b64":"OGdjuoPjzfvnZ7EQAAoKkd3DQx2haqiR7CIfwNY5Bo1ig3SZrrzvjdHwv1tjYwCTAM8YRTZHwGOEmqrvXflQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ad04806dacac4ea70d20903ba075722e1210475e693a165fff53c83105079e3","last_reissued_at":"2026-05-18T00:01:14.810686Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:14.810686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.03404","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":"2026-05-18T00:01:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ybLarDF6QPcsTRL6kV9fTegH35Ppxavb0g8ilxWNllKrOupCVhJde1cqSY+tW33gMZUmu1GXse7fm5SdooC4Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T10:32:59.685878Z"},"content_sha256":"7aabc2f51ec04648aa0ad36a51a769fae0834ef980539f96b6efccf7698ebd72","schema_version":"1.0","event_id":"sha256:7aabc2f51ec04648aa0ad36a51a769fae0834ef980539f96b6efccf7698ebd72"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:RLIEQBW2ZLCOU4GSBEB3UB2XEL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Jeffrey C. Grossman, Tian Xie","submitted_at":"2018-07-09T22:06:04Z","abstract_excerpt":"The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property space. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03404","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":""},"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":"2026-05-18T00:01:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CUYGjwu8k5xczZt4XgXDokkd6dp1CAcgEliQuTX+iif+TPSiDzVtLAT7vR/zQoBNVyAIIgWjsYRI2wZlCz+hDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T10:32:59.686225Z"},"content_sha256":"f6c1230504b2325dae6f4743c84e6cd495aebdf500bbcff96bcb1c2e90e319ca","schema_version":"1.0","event_id":"sha256:f6c1230504b2325dae6f4743c84e6cd495aebdf500bbcff96bcb1c2e90e319ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RLIEQBW2ZLCOU4GSBEB3UB2XEL/bundle.json","state_url":"https://pith.science/pith/RLIEQBW2ZLCOU4GSBEB3UB2XEL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RLIEQBW2ZLCOU4GSBEB3UB2XEL/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":"2026-07-02T10:32:59Z","links":{"resolver":"https://pith.science/pith/RLIEQBW2ZLCOU4GSBEB3UB2XEL","bundle":"https://pith.science/pith/RLIEQBW2ZLCOU4GSBEB3UB2XEL/bundle.json","state":"https://pith.science/pith/RLIEQBW2ZLCOU4GSBEB3UB2XEL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RLIEQBW2ZLCOU4GSBEB3UB2XEL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:RLIEQBW2ZLCOU4GSBEB3UB2XEL","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":"f834daf9e1aa492370440f3482e6384e82734dfe2f019a32b90103c13b4bb4a8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-07-09T22:06:04Z","title_canon_sha256":"a4b1e2eeae10244ea07dab88e181970413121c423dde28da1fefd7b9970e06db"},"schema_version":"1.0","source":{"id":"1807.03404","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03404","created_at":"2026-05-18T00:01:14Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03404v2","created_at":"2026-05-18T00:01:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03404","created_at":"2026-05-18T00:01:14Z"},{"alias_kind":"pith_short_12","alias_value":"RLIEQBW2ZLCO","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RLIEQBW2ZLCOU4GS","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RLIEQBW2","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:f6c1230504b2325dae6f4743c84e6cd495aebdf500bbcff96bcb1c2e90e319ca","target":"graph","created_at":"2026-05-18T00:01:14Z","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"},"paper":{"abstract_excerpt":"The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property space. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material s","authors_text":"Jeffrey C. Grossman, Tian Xie","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-07-09T22:06:04Z","title":"Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03404","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":"sha256:7aabc2f51ec04648aa0ad36a51a769fae0834ef980539f96b6efccf7698ebd72","target":"record","created_at":"2026-05-18T00:01:14Z","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":"f834daf9e1aa492370440f3482e6384e82734dfe2f019a32b90103c13b4bb4a8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-07-09T22:06:04Z","title_canon_sha256":"a4b1e2eeae10244ea07dab88e181970413121c423dde28da1fefd7b9970e06db"},"schema_version":"1.0","source":{"id":"1807.03404","kind":"arxiv","version":2}},"canonical_sha256":"8ad04806dacac4ea70d20903ba075722e1210475e693a165fff53c83105079e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ad04806dacac4ea70d20903ba075722e1210475e693a165fff53c83105079e3","first_computed_at":"2026-05-18T00:01:14.810686Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:14.810686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OGdjuoPjzfvnZ7EQAAoKkd3DQx2haqiR7CIfwNY5Bo1ig3SZrrzvjdHwv1tjYwCTAM8YRTZHwGOEmqrvXflQDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:14.811199Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.03404","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7aabc2f51ec04648aa0ad36a51a769fae0834ef980539f96b6efccf7698ebd72","sha256:f6c1230504b2325dae6f4743c84e6cd495aebdf500bbcff96bcb1c2e90e319ca"],"state_sha256":"26731fa7571d567a9e3995c5bd2081f7fb59eb748a97c66e78daf42168176bf6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5HQulHjFxLZM4c7oGD08VM3QDIfJqI/21yIuJhFvgtmj39sE7+S0DLgbeRovNci6ch+Hit1dOHDiHi2snU0RDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T10:32:59.688259Z","bundle_sha256":"f062c78f28c7740e606d1769e7cbc16e223897fd16d024be01536aca7dcd4d80"}}