{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:UW3S6WM4BLS4KNZECYXEP4B6VG","short_pith_number":"pith:UW3S6WM4","canonical_record":{"source":{"id":"2605.31144","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-29T10:51:43Z","cross_cats_sorted":[],"title_canon_sha256":"f9c0047a13d7f24ba5c268090b8d268cec3429e8b58ca8a005a1c40e97d784e3","abstract_canon_sha256":"ad735265da2aaab77530807ab14f43816cf7859271d78d90aad16aa85f8adc43"},"schema_version":"1.0"},"canonical_sha256":"a5b72f599c0ae5c53724162e47f03ea9bfe9de80a61478e3a18cc998b33fc2ed","source":{"kind":"arxiv","id":"2605.31144","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.31144","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"arxiv_version","alias_value":"2605.31144v1","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31144","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"pith_short_12","alias_value":"UW3S6WM4BLS4","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"pith_short_16","alias_value":"UW3S6WM4BLS4KNZE","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"pith_short_8","alias_value":"UW3S6WM4","created_at":"2026-06-01T01:04:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:UW3S6WM4BLS4KNZECYXEP4B6VG","target":"record","payload":{"canonical_record":{"source":{"id":"2605.31144","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-29T10:51:43Z","cross_cats_sorted":[],"title_canon_sha256":"f9c0047a13d7f24ba5c268090b8d268cec3429e8b58ca8a005a1c40e97d784e3","abstract_canon_sha256":"ad735265da2aaab77530807ab14f43816cf7859271d78d90aad16aa85f8adc43"},"schema_version":"1.0"},"canonical_sha256":"a5b72f599c0ae5c53724162e47f03ea9bfe9de80a61478e3a18cc998b33fc2ed","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:04:00.933836Z","signature_b64":"TnZ9eF6yJI7ABG4PkphbiEOs/1Eq0zkZzM7g9Df1PbyJy8QjM8tc0J83iCmei59u1n+OczVeLXI7v/Acs05qDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5b72f599c0ae5c53724162e47f03ea9bfe9de80a61478e3a18cc998b33fc2ed","last_reissued_at":"2026-06-01T01:04:00.933098Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:04:00.933098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.31144","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":"2026-06-01T01:04:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+g/oonzCfpw9oAh8Wf1ukvCXAZJFy/0aOQEc5ncHq2mZzckMUzyEFEDh83wTAjXREEGJeAmUScqNs2HmaTYaDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T11:11:24.152846Z"},"content_sha256":"0dc896a0ece8cea0e2b44714a465097218c7f6eb9f3b06299153a96bb7b58d30","schema_version":"1.0","event_id":"sha256:0dc896a0ece8cea0e2b44714a465097218c7f6eb9f3b06299153a96bb7b58d30"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:UW3S6WM4BLS4KNZECYXEP4B6VG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Flyura Djurabekova, Jyri Kimari, Kostas Sarakinos","submitted_at":"2026-05-29T10:51:43Z","abstract_excerpt":"We present a kinetic Monte Carlo (KMC) simulation framework parameterized by automatically sampling machine-learning (ML) for modeling thin-film growth atom by atom. Given an interatomic potential energy function, the KMC algorithm builds an ML-based regression model for rate parameters on runtime, being trained on the local atomic environments encountered during the system evolution. New environments are continuously added to the training set in a self-evolving manner at points where the ML model estimates high uncertainty. As the simulation progresses, the ML model gains confidence, and the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31144","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/2605.31144/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":"2026-06-01T01:04:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OzIPyWhd6abSvrO5lFZ2G+bQI2tctofk5VoWT21bQYpey5jMuV6MODR+kZlx+dfuBMVGcuPLfqkoyIuCkA/VCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T11:11:24.153213Z"},"content_sha256":"7e6d66bd26a2b4683cb2d9a14be13756265b657a8489e49ef1475f864703dddf","schema_version":"1.0","event_id":"sha256:7e6d66bd26a2b4683cb2d9a14be13756265b657a8489e49ef1475f864703dddf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UW3S6WM4BLS4KNZECYXEP4B6VG/bundle.json","state_url":"https://pith.science/pith/UW3S6WM4BLS4KNZECYXEP4B6VG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UW3S6WM4BLS4KNZECYXEP4B6VG/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":"2026-06-09T11:11:24Z","links":{"resolver":"https://pith.science/pith/UW3S6WM4BLS4KNZECYXEP4B6VG","bundle":"https://pith.science/pith/UW3S6WM4BLS4KNZECYXEP4B6VG/bundle.json","state":"https://pith.science/pith/UW3S6WM4BLS4KNZECYXEP4B6VG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UW3S6WM4BLS4KNZECYXEP4B6VG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UW3S6WM4BLS4KNZECYXEP4B6VG","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":"ad735265da2aaab77530807ab14f43816cf7859271d78d90aad16aa85f8adc43","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-29T10:51:43Z","title_canon_sha256":"f9c0047a13d7f24ba5c268090b8d268cec3429e8b58ca8a005a1c40e97d784e3"},"schema_version":"1.0","source":{"id":"2605.31144","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.31144","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"arxiv_version","alias_value":"2605.31144v1","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31144","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"pith_short_12","alias_value":"UW3S6WM4BLS4","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"pith_short_16","alias_value":"UW3S6WM4BLS4KNZE","created_at":"2026-06-01T01:04:00Z"},{"alias_kind":"pith_short_8","alias_value":"UW3S6WM4","created_at":"2026-06-01T01:04:00Z"}],"graph_snapshots":[{"event_id":"sha256:7e6d66bd26a2b4683cb2d9a14be13756265b657a8489e49ef1475f864703dddf","target":"graph","created_at":"2026-06-01T01:04:00Z","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/2605.31144/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a kinetic Monte Carlo (KMC) simulation framework parameterized by automatically sampling machine-learning (ML) for modeling thin-film growth atom by atom. Given an interatomic potential energy function, the KMC algorithm builds an ML-based regression model for rate parameters on runtime, being trained on the local atomic environments encountered during the system evolution. New environments are continuously added to the training set in a self-evolving manner at points where the ML model estimates high uncertainty. As the simulation progresses, the ML model gains confidence, and the ","authors_text":"Flyura Djurabekova, Jyri Kimari, Kostas Sarakinos","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-29T10:51:43Z","title":"A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31144","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":"sha256:0dc896a0ece8cea0e2b44714a465097218c7f6eb9f3b06299153a96bb7b58d30","target":"record","created_at":"2026-06-01T01:04:00Z","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":"ad735265da2aaab77530807ab14f43816cf7859271d78d90aad16aa85f8adc43","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-29T10:51:43Z","title_canon_sha256":"f9c0047a13d7f24ba5c268090b8d268cec3429e8b58ca8a005a1c40e97d784e3"},"schema_version":"1.0","source":{"id":"2605.31144","kind":"arxiv","version":1}},"canonical_sha256":"a5b72f599c0ae5c53724162e47f03ea9bfe9de80a61478e3a18cc998b33fc2ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5b72f599c0ae5c53724162e47f03ea9bfe9de80a61478e3a18cc998b33fc2ed","first_computed_at":"2026-06-01T01:04:00.933098Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T01:04:00.933098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TnZ9eF6yJI7ABG4PkphbiEOs/1Eq0zkZzM7g9Df1PbyJy8QjM8tc0J83iCmei59u1n+OczVeLXI7v/Acs05qDQ==","signature_status":"signed_v1","signed_at":"2026-06-01T01:04:00.933836Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.31144","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0dc896a0ece8cea0e2b44714a465097218c7f6eb9f3b06299153a96bb7b58d30","sha256:7e6d66bd26a2b4683cb2d9a14be13756265b657a8489e49ef1475f864703dddf"],"state_sha256":"63f1b47245f9fc79822db945c2b9a59b77bf8310fce722ae1c47df5578043b05"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0OUFygEj3oWsemVQzernmfRTGvPgLoFP+czsD9GTPvX4DU1kZjjwcjodCHeZZK15sRP9oYpXoGEm0904P1WoDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T11:11:24.155167Z","bundle_sha256":"c7391bb75858be274a9b074676a782961153d4ba3c2c3bb619a6cadd247ac0c7"}}