{"id":"W2804191529","doi":"10.1002/adts.201800069","title":"A Bayesian Approach to Predict Solubility Parameters","year":2018,"lang":"en","type":"article","venue":"Advanced Theory and Simulations","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Canadian Institute for Advanced Research","funders":"Solar Technologies go Hybrid; Deutsche Forschungsgemeinschaft; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); U.S. Department of Energy","keywords":"Solubility; Bayesian probability; Miscibility; Computer science; Flexibility (engineering); Probabilistic logic; Set (abstract data type); Toolbox; Consistency (knowledge bases); Biological system; Algorithm; Chemistry; Artificial intelligence; Polymer; Statistical physics; Mathematics; Physics; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001152411,0.0001248855,0.0001488141,0.00005910561,0.0004701946,0.00009432302,0.0002248329,0.00004038611,0.0003393216],"category_scores_gemma":[0.0009834375,0.0001072631,0.00002193596,0.0002018272,0.0003913761,0.0003015247,0.0001137499,0.00006372958,0.00008570498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001832244,"about_ca_system_score_gemma":0.00002048491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000748389,"about_ca_topic_score_gemma":0.000005538031,"domain_scores_codex":[0.9985637,0.0003486647,0.0002079846,0.0004439715,0.0001586732,0.0002770096],"domain_scores_gemma":[0.9988863,0.0004480464,0.0000621424,0.0003902215,0.00006828678,0.0001449372],"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":[0.0003768567,0.0001331745,0.002837271,0.00003271008,0.000006869341,8.205935e-7,0.003198931,0.4377194,0.4553052,0.09650259,0.00003521872,0.00385094],"study_design_scores_gemma":[0.001183969,0.000788892,0.01743008,0.00007209955,0.0000516622,0.00002208694,0.0005744068,0.4561987,0.1132509,0.4060814,0.003359921,0.0009858508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6649441,0.000007442867,0.331684,0.00005109404,0.0002122348,0.0002191338,0.00002238214,0.0001084773,0.002751203],"genre_scores_gemma":[0.9092359,4.756865e-7,0.09016788,0.000293041,0.00006257923,0.00002044996,0.000004786356,0.00001021451,0.0002046662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3420543,"threshold_uncertainty_score":0.4374063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138128140926511,"score_gpt":0.2823534430196564,"score_spread":0.2709721616103913,"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."}}