{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VTBJKTG3KYS24DCXCYC7HCPLCC","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":"5c9468459ed1a7026e2e6d58a71e9b52be758ce6e91cd971adc70d9a1d61a293","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-03-08T12:58:14Z","title_canon_sha256":"00d3dce343b4e5be0916074aa321f9fec743c1d89c5b5a35c433149fde8f5ff5"},"schema_version":"1.0","source":{"id":"1803.03073","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.03073","created_at":"2026-05-18T00:04:13Z"},{"alias_kind":"arxiv_version","alias_value":"1803.03073v2","created_at":"2026-05-18T00:04:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03073","created_at":"2026-05-18T00:04:13Z"},{"alias_kind":"pith_short_12","alias_value":"VTBJKTG3KYS2","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VTBJKTG3KYS24DCX","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VTBJKTG3","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:61e3ef07b67db33e8c3241d64a58cc330a69a9c1337bb7a532a0367255174e4c","target":"graph","created_at":"2026-05-18T00:04:13Z","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":"Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of ","authors_text":"Christian Els\\\"asser, Daniel F. Urban, Georg Krugel, Johannes J. M\\\"oller, Wolfgang K\\\"orner","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-03-08T12:58:14Z","title":"Compositional optimization of hard-magnetic phases with machine-learning models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03073","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":"sha256:355caaf3a398d9916c72902276db8ca063a26a5a11be48c45e0cd5c3605f8ca1","target":"record","created_at":"2026-05-18T00:04:13Z","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":"5c9468459ed1a7026e2e6d58a71e9b52be758ce6e91cd971adc70d9a1d61a293","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-03-08T12:58:14Z","title_canon_sha256":"00d3dce343b4e5be0916074aa321f9fec743c1d89c5b5a35c433149fde8f5ff5"},"schema_version":"1.0","source":{"id":"1803.03073","kind":"arxiv","version":2}},"canonical_sha256":"acc2954cdb5625ae0c571605f389eb109bfbba0281e4daeea002b0a41fcbbc09","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"acc2954cdb5625ae0c571605f389eb109bfbba0281e4daeea002b0a41fcbbc09","first_computed_at":"2026-05-18T00:04:13.190309Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:13.190309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xsRAQiUIWCJCLqOSzoGOXn8+VbG/ztZTToYPOlw+9FBqehQeyc1Ozy4dG2fo0aZX2tTbPKDHyy7lYkvflLQbDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:13.190886Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.03073","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:355caaf3a398d9916c72902276db8ca063a26a5a11be48c45e0cd5c3605f8ca1","sha256:61e3ef07b67db33e8c3241d64a58cc330a69a9c1337bb7a532a0367255174e4c"],"state_sha256":"4f1d116bc4dc5892ffd0fc0cb0d68aa7d8556115a0b9c388523e895e0a92cbca"}