{"id":"W4207006180","doi":"10.1039/d1cp05847a","title":"Machine learning-enabled band gap prediction of monolayer transition metal chalcogenide alloys","year":2022,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"2D Materials and Applications","field":"Materials Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; University of Toronto; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Monolayer; Band gap; Materials science; Chalcogenide; Direct and indirect band gaps; Ternary operation; Semimetal; Chalcogen; Condensed matter physics; Electronic band structure; Alloy; Bowing; Lattice constant; Quinary; Optoelectronics; Nanotechnology; Crystallography; Chemistry; Optics; Computer science; Metallurgy; Physics; Diffraction","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":[],"consensus_categories":[],"category_scores_codex":[0.0001322873,0.0002131977,0.0003594305,0.000007997534,0.0001888634,0.00002432181,0.0002855839,0.00005002219,0.0006494492],"category_scores_gemma":[0.00001918699,0.000216994,0.0001881747,0.0001997667,0.00009207019,0.0001265956,0.0001339185,0.0002970137,0.00002344812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007723803,"about_ca_system_score_gemma":0.00003663189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002645883,"about_ca_topic_score_gemma":3.844566e-8,"domain_scores_codex":[0.9984936,0.00004899909,0.0003102773,0.0004299038,0.0004341997,0.0002830921],"domain_scores_gemma":[0.9992908,0.00005868808,0.000185967,0.0002893077,0.00006886858,0.0001063706],"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":[0.00007543025,0.0006511175,0.000006499845,0.00009893686,0.00002018504,9.528786e-7,0.0002064429,0.002149587,0.9963149,0.0003003396,0.00009447696,0.00008112804],"study_design_scores_gemma":[0.0004755114,0.00004198973,0.000005576922,0.000006359766,0.0000788696,0.000005256712,0.00003973833,0.008361586,0.9839346,0.006200663,0.0006673126,0.0001824715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968958,0.00002757926,0.0005289234,0.0001157361,0.00004599194,0.0001571039,0.0005027025,0.0001351212,0.001590985],"genre_scores_gemma":[0.9985765,0.000004781308,0.0001251228,0.00003360382,0.0005150225,0.0001891128,0.0004370708,0.00003341132,0.00008541725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01238024,"threshold_uncertainty_score":0.8848757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01675746081520227,"score_gpt":0.2273137238479625,"score_spread":0.2105562630327603,"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."}}