{"paper":{"title":"Quantitative trends in 8 physical properties of 115000 inorganic compounds gained by machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Evgeny Blokhin, Pierre Villars","submitted_at":"2018-06-09T22:31:52Z","abstract_excerpt":"We applied the decision trees (random forest) machine-learning technique for the large experimental materials dataset PAULING FILE, compiled from the world's peer-reviewed literature. The training and validation data were extracted from the hundreds of thousands of publications in materials science (1891-2017). Then, for the nearly 115'000 distinct inorganic compounds we predicted 8 thermodynamic, mechanical, and electronic properties, using the only crystalline structures as an input. For the predicted physical properties we observed certain periodical patterns in all unary, binary, ternary, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03553","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"}