{"id":"W4399500528","doi":"10.1016/j.actamat.2024.120074","title":"Towards accurate thermodynamics from random energy sampling","year":2024,"lang":"en","type":"article","venue":"Acta Materialia","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Materials science; Thermodynamics; Statistical physics; Sampling (signal processing); Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003289216,0.00100539,0.00159893,0.001878731,0.0007767425,0.002296483,0.001811775,0.00143557,0.002271764],"category_scores_gemma":[0.01497721,0.001140842,0.001216011,0.0009585964,0.002609986,0.003157371,0.002149228,0.002384746,0.0008892819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001429608,"about_ca_system_score_gemma":0.001425124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00246498,"about_ca_topic_score_gemma":0.00237817,"domain_scores_codex":[0.9983222,0.0007901163,0.00005947283,0.0001340774,0.0005679202,0.0001262414],"domain_scores_gemma":[0.9952508,0.002889672,0.0003149139,0.0008065049,0.0005793843,0.000158788],"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":[0.00004672035,0.00004092598,0.0006244095,0.0001323066,0.00003777923,0.0001040792,0.0000795326,0.4940299,0.001828352,0.4896207,0.001022686,0.01243268],"study_design_scores_gemma":[0.000004984927,0.00000547402,0.00005494122,0.000009085212,0.000002246471,0.000008271722,0.000004483827,0.8664746,0.0003180497,0.1326419,0.0004702059,0.000005749042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02255106,0.0005697815,0.9702424,0.0004896553,0.0001174938,0.00004902419,0.0001147259,0.0004178628,0.005448047],"genre_scores_gemma":[0.6556084,0.001646894,0.3334366,0.0005384407,0.0004040917,0.0005107219,0.0005195453,0.0009384265,0.006396893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003289216,"threshold_uncertainty_score":0.01739526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0202894032037013,"score_gpt":0.2877399979987461,"score_spread":0.2674505947950448,"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."}}