{"id":"W4281620629","doi":"10.1103/physrevlett.129.185701","title":"Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics","year":2022,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Division of Materials Research; Office of Science; Basic Energy Sciences; National Energy Research Scientific Computing Center; Compute Canada; U.S. Department of Energy","keywords":"Nucleation; Metadynamics; Phase transition; Materials science; Statistical physics; Chemical physics; Crystal (programming language); Phase (matter); Interpolation (computer graphics); Condensed matter physics; Computer science; Physics; Molecular dynamics; Artificial intelligence; Thermodynamics; Chemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001175479,0.0002024616,0.0004467107,0.00005095615,0.0002555175,0.00008158782,0.0004344972,0.000008241755,0.003019989],"category_scores_gemma":[0.0002421626,0.0001946024,0.000114768,0.0003151431,0.00006951164,0.0003054319,0.0001667012,0.0004394584,0.0001415269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000192416,"about_ca_system_score_gemma":0.00002190561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000468064,"about_ca_topic_score_gemma":0.00001289624,"domain_scores_codex":[0.9969066,0.001110168,0.0004083399,0.0005283548,0.0007207067,0.000325829],"domain_scores_gemma":[0.9991304,0.0002830344,0.000229151,0.0002676806,0.00001739229,0.00007237389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004077845,0.0002001008,0.00005795914,0.0001196262,0.000003344706,0.00002151022,0.0004880717,0.06534095,0.9329633,0.00006239537,0.0000643945,0.0006376019],"study_design_scores_gemma":[0.003312228,0.0005855035,0.001531266,0.0007146655,0.0002277324,0.00003307906,0.0001446655,0.967593,0.01379614,0.001068258,0.009760291,0.001233202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903375,0.0007212011,0.003218314,0.004791491,0.0002803875,0.0003813691,0.00009908184,0.0001189287,0.00005171295],"genre_scores_gemma":[0.9915616,0.0001949315,0.001122871,0.006749319,0.0001117564,0.0001151009,0.0001047055,0.00002872493,0.00001100993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9191671,"threshold_uncertainty_score":0.9978914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049404225656062,"score_gpt":0.2860506230121828,"score_spread":0.2755565807556222,"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."}}