{"id":"W4412654790","doi":"10.1021/acsnano.5c04200","title":"Artificial Intelligence for Materials Discovery, Development, and Optimization","year":2025,"lang":"en","type":"review","venue":"ACS Nano","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Artificial intelligence; Computer science; Interpretability; Machine learning; Deep learning; Benchmarking; Data science; Reinforcement learning; Robustness (evolution)","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":[0.002235965,0.00121927,0.001456444,0.001407375,0.0007069504,0.003569342,0.001272124,0.002398267,0.005889031],"category_scores_gemma":[0.003871557,0.0004409449,0.001007675,0.00133359,0.003452044,0.003452286,0.002181323,0.004534026,0.002106046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001889774,"about_ca_system_score_gemma":0.002377444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583432,"about_ca_topic_score_gemma":0.001333277,"domain_scores_codex":[0.9984964,0.0005276054,0.00009556583,0.0002563435,0.0005377776,0.00008628484],"domain_scores_gemma":[0.9980074,0.001361186,0.0001024714,0.0002483389,0.0002208153,0.00005989034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002941188,0.00004444149,0.00040325,0.001998435,0.0001154492,0.0001232824,0.000125684,0.02489534,0.001633942,0.786426,0.02223124,0.1619735],"study_design_scores_gemma":[0.00001834342,0.00004941042,0.0002417258,0.0007383778,0.00003230156,0.0001003102,0.00007278803,0.03176682,0.0009018839,0.7645645,0.2014684,0.00004515755],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006793836,0.3756236,0.4504278,0.03376031,0.004011436,0.000187519,0.0008557484,0.001397917,0.1269419],"genre_scores_gemma":[0.2378627,0.4418107,0.2730294,0.007283492,0.004652684,0.0008279206,0.001403927,0.0004480362,0.03268108],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005889031,"threshold_uncertainty_score":0.01970077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04916722732603536,"score_gpt":0.346708232677654,"score_spread":0.2975410053516187,"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."}}