{"id":"W2783650155","doi":"10.1002/mats.201700088","title":"Heuristic Search Strategy for Transforming Microstructural Patterns to Optimal Copolymerization Recipes","year":2018,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Copolymer; Materials science; Heuristic; Computer science; Ethylene; Polymerization; Polymer science; Algorithm; Polymer; Chemistry; Organic chemistry; Catalysis; Artificial intelligence; Composite material","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":[0.0007397808,0.0001472159,0.0001505083,0.0000845812,0.0006419377,0.000256498,0.0002043516,0.00005302699,0.0007467422],"category_scores_gemma":[0.0002165204,0.0001363469,0.00003360839,0.0001467704,0.0002056628,0.0001601347,0.00005624764,0.0000554793,0.00002852633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001645337,"about_ca_system_score_gemma":0.00003312916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001846389,"about_ca_topic_score_gemma":0.00001167454,"domain_scores_codex":[0.998624,0.0002709015,0.0002373313,0.0003743622,0.0001596516,0.0003337434],"domain_scores_gemma":[0.9992862,0.0002365607,0.00004636115,0.0002008793,0.0001162103,0.0001137495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001444616,0.0000100134,0.0001654218,0.00002684477,0.000003845629,0.000002042472,0.001595619,0.03458424,0.9523421,0.009679082,0.00000283382,0.001443529],"study_design_scores_gemma":[0.000459768,0.0004428214,0.001263454,0.00004192066,0.0000290593,0.00002569282,0.000375642,0.05324369,0.9389331,0.004621154,0.0002412462,0.0003224846],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6097469,0.00001318589,0.3895088,0.00005481248,0.000148515,0.0002838185,0.000140249,0.00004092133,0.00006276649],"genre_scores_gemma":[0.9909344,8.481198e-7,0.008539043,0.0001796908,0.0001316307,0.00001828451,0.00004796165,0.00002122251,0.0001269577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3811875,"threshold_uncertainty_score":0.8176304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479763414360865,"score_gpt":0.3109355375030264,"score_spread":0.2961379033594178,"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."}}