{"id":"W4411004979","doi":"10.26434/chemrxiv-2025-vfsvt","title":"Thermodynamics-informed machine learning for predicting temperature-dependent chemical properties","year":2025,"lang":"en","type":"preprint","venue":"ChemRxiv","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; Canada Research Chairs","keywords":"Thermodynamics; Chemistry; Materials science; Physics","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":[0.001581227,0.001108452,0.0009827295,0.001110006,0.0003883226,0.001055925,0.0011849,0.001368212,0.001686166],"category_scores_gemma":[0.005924573,0.0004672133,0.0007510236,0.0009698291,0.0007354313,0.001476299,0.0007922865,0.001936466,0.0009135333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009128481,"about_ca_system_score_gemma":0.001057723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002044839,"about_ca_topic_score_gemma":0.002583081,"domain_scores_codex":[0.999451,0.0002116047,0.00002600945,0.0001274758,0.0001371306,0.00004677715],"domain_scores_gemma":[0.9983388,0.001082572,0.0001548277,0.000170241,0.0001967531,0.00005676981],"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":[0.00007341035,0.0000960573,0.001797863,0.0001290694,0.00006411415,0.00003741797,0.00002185551,0.9485514,0.003661784,0.006415208,0.001803181,0.03734867],"study_design_scores_gemma":[0.000003476231,0.000008065886,0.0001000474,0.00000373117,0.000003047709,0.000003678484,0.000001642085,0.9936215,0.0008857689,0.005110648,0.0002550433,0.000003312602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1106131,0.003277847,0.8743111,0.001603844,0.0001948296,0.0001172473,0.001193648,0.003549031,0.005139388],"genre_scores_gemma":[0.8552993,0.00125394,0.1368765,0.0005179776,0.0002599494,0.000229941,0.001937504,0.0002481389,0.003376594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002044839,"threshold_uncertainty_score":0.008362412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719994124003566,"score_gpt":0.2647991897126163,"score_spread":0.2475992484725807,"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."}}