{"id":"W2979285519","doi":"10.1016/j.matt.2019.08.017","title":"Inverse Design of Solid-State Materials via a Continuous Representation","year":2019,"lang":"en","type":"article","venue":"Matter","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":370,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute; University of Toronto; Canadian Institute for Advanced Research","funders":"Korea Institute of Energy Technology Evaluation and Planning; Office of Science; Korea Institute of Science and Technology Information; Natural Resources Canada; National Research Foundation of Korea; U.S. Department of Energy","keywords":"Representation (politics); Generative grammar; Computer science; Generative Design; Space (punctuation); Chemical space; Inverse; Artificial intelligence; Materials science; Mathematics; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00106845,0.0001619587,0.0003666196,0.00009103196,0.00004634968,0.0001006135,0.0004018473,0.00005363868,0.02521502],"category_scores_gemma":[0.00006628064,0.0001398843,0.00003584183,0.0001288809,0.0001233153,0.0003173634,0.0001593441,0.00005474151,0.01336522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002449466,"about_ca_system_score_gemma":0.00002723707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002588156,"about_ca_topic_score_gemma":0.000002173586,"domain_scores_codex":[0.9980314,0.0004102394,0.0004820553,0.0004074255,0.0003397102,0.0003291194],"domain_scores_gemma":[0.9988816,0.0001095491,0.0003437751,0.0005242735,0.00008585118,0.00005495584],"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":[0.00007798494,0.00002264629,0.005831777,0.00006173536,0.000003372057,0.000004615605,0.0004314291,0.005218695,0.9855755,0.00001227887,0.002725182,0.00003480423],"study_design_scores_gemma":[0.0003336055,0.00008148135,0.009047067,0.00003720109,0.000008363491,0.00001667318,0.00002801334,0.001603633,0.9877452,0.0007243794,0.000194735,0.0001796025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748145,0.000003702858,0.02281108,0.0002393784,0.00108945,0.0004549147,0.00003196895,0.00007371953,0.0004812664],"genre_scores_gemma":[0.9840773,0.000001270026,0.01221743,0.000637532,0.00004387607,0.00002507739,0.000006712057,0.00002713524,0.002963649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0118498,"threshold_uncertainty_score":0.987403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388422208033181,"score_gpt":0.2728444738720601,"score_spread":0.2589602517917283,"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."}}