{"id":"W4313529600","doi":"10.1016/j.patter.2022.100663","title":"Strain data augmentation enables machine learning of inorganic crystal geometry optimization","year":2023,"lang":"en","type":"article","venue":"Patterns","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; University of Toronto; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Energy minimization; Computer science; Construct (python library); Work (physics); Strain energy; Algorithm; Global optimization; Mathematical optimization; Machine learning; Biological system; Artificial intelligence; Mechanical engineering; Mathematics; Chemistry; Finite element method; Structural engineering; Engineering; Computational chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006913326,0.0008057876,0.0006911884,0.0005730769,0.0004038084,0.0009208359,0.001365827,0.001315803,0.002476237],"category_scores_gemma":[0.003252787,0.0005194368,0.0008075717,0.0005385615,0.0008174415,0.001507024,0.0008890646,0.001524549,0.0007833921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008554098,"about_ca_system_score_gemma":0.0009083672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003222259,"about_ca_topic_score_gemma":0.005692904,"domain_scores_codex":[0.9997479,0.00006237144,0.00001295915,0.00007412162,0.00008157151,0.0000210407],"domain_scores_gemma":[0.9990892,0.0004234228,0.00008548686,0.0002159935,0.0001411709,0.00004468727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004932098,0.00005210199,0.001113952,0.00004800313,0.00001639764,0.00003634049,0.00001550594,0.9562164,0.003603077,0.006571639,0.001366776,0.03091048],"study_design_scores_gemma":[0.000001534931,0.000004635585,0.00003789338,0.000001369644,7.045369e-7,0.000002656899,0.000001073704,0.9975517,0.0006808643,0.001535469,0.0001804468,0.000001678022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1729938,0.0005758333,0.810271,0.001136281,0.0001510693,0.00009190201,0.001254298,0.005155565,0.00837018],"genre_scores_gemma":[0.8157754,0.0002605735,0.1783587,0.0002845804,0.00007274681,0.0001583861,0.001702978,0.0003757547,0.003010879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003222259,"threshold_uncertainty_score":0.008283854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03268393480916215,"score_gpt":0.2915745195126704,"score_spread":0.2588905847035083,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}