{"id":"W4387666276","doi":"10.1016/j.mattod.2023.09.007","title":"Tailoring the mechanical properties of 3D microstructures: A deep learning and genetic algorithm inverse optimization framework","year":2023,"lang":"en","type":"article","venue":"Materials Today","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inverse; Microstructure; Genetic algorithm; Computer science; Materials science; Process (computing); Transferability; Range (aeronautics); Limiting; Algorithm; Mathematical optimization; Mechanical engineering; Machine learning; Mathematics; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005520781,0.0005016761,0.0005002336,0.000399983,0.0002489724,0.0007008053,0.0008191933,0.001349206,0.0008188663],"category_scores_gemma":[0.001145387,0.000433776,0.0004248392,0.0003393356,0.0008636424,0.001037011,0.000619013,0.0009533673,0.0001547911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009026664,"about_ca_system_score_gemma":0.0008788267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002598871,"about_ca_topic_score_gemma":0.004295544,"domain_scores_codex":[0.999903,0.00002414907,0.000003548017,0.00001887706,0.00003717331,0.00001320721],"domain_scores_gemma":[0.9996897,0.000157937,0.00004971111,0.00003551988,0.0000478129,0.00001937706],"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":[0.0000201401,0.00003916948,0.0003775787,0.00003662907,0.00001532576,0.00002099811,0.00001525352,0.9682465,0.007948954,0.01039944,0.0001936575,0.0126863],"study_design_scores_gemma":[0.000001796023,0.000005142269,0.00003622812,0.000001487157,8.888888e-7,0.000001860734,0.000001264998,0.9973711,0.0004171169,0.002085253,0.00007625144,0.000001580325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08379116,0.0004231068,0.9102858,0.0005291956,0.0000399042,0.00004099146,0.00009411816,0.000370534,0.004425187],"genre_scores_gemma":[0.7877954,0.0004008308,0.2092354,0.0001813538,0.00003492095,0.00009227693,0.0001112445,0.0001351299,0.002013423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002598871,"threshold_uncertainty_score":0.006549358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01229509010684038,"score_gpt":0.2416701502508446,"score_spread":0.2293750601440042,"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."}}