{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001519386,0.0004419698,0.00120644,0.0002253611,0.000339991,0.0009886872,0.0007401523,0.0002618388,0.0002551947],"category_scores_gemma":[0.0009567289,0.0003563909,0.00007710026,0.000305815,0.0001518265,0.0003731525,0.000420979,0.0000868869,0.0001052786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008773567,"about_ca_system_score_gemma":0.0005767988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001414786,"about_ca_topic_score_gemma":0.000004169905,"domain_scores_codex":[0.9971439,0.0002548903,0.001082769,0.0008334821,0.0002457474,0.0004391788],"domain_scores_gemma":[0.9983258,0.0005398819,0.000572864,0.0004188988,0.00007893412,0.00006368025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003215127,0.00006187204,3.989192e-7,0.03474937,0.0000277738,0.000004126064,0.0001832653,0.0009553342,0.007860069,0.01092531,0.0002925088,0.9449078],"study_design_scores_gemma":[0.00005795246,0.0000835773,3.517048e-7,0.01301682,0.0003409843,0.00002630598,0.00001878342,0.0001311378,0.08753727,0.001395086,0.896257,0.001134663],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001616223,0.7290574,0.2655342,0.00003643177,0.00285449,0.001777909,0.0002861092,0.0001587763,0.0001330965],"genre_scores_gemma":[0.00001970745,0.8538257,0.1437018,0.00004549335,0.0002449775,0.0004973295,0.0002417679,0.00004937875,0.001373795],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9437732,"threshold_uncertainty_score":0.9998888,"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."}}