{"id":"W4313366884","doi":"10.1038/s43588-022-00382-2","title":"A machine learning route between band mapping and band structure","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Max-Planck-Gesellschaft; Deutsche Forschungsgemeinschaft; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Computer science; Artificial intelligence; Pipeline (software); Machine learning; Scalability; Electronic band structure; Physics; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001925534,0.0009006711,0.000903851,0.002185142,0.0009575343,0.004102777,0.002699583,0.001641552,0.004546893],"category_scores_gemma":[0.009635864,0.000869459,0.001179994,0.001967996,0.001821129,0.004850687,0.002377014,0.003548418,0.001850574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190141,"about_ca_system_score_gemma":0.001128431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0025835,"about_ca_topic_score_gemma":0.002481022,"domain_scores_codex":[0.9991904,0.0002163549,0.00004363437,0.0003156167,0.0001930097,0.00004102166],"domain_scores_gemma":[0.9975948,0.001374667,0.0001671874,0.0005339003,0.0002786705,0.00005079694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009923712,0.0002771398,0.0037668,0.0004769043,0.0001741315,0.0001611255,0.0003464125,0.1912076,0.007227919,0.3111186,0.01409566,0.4710486],"study_design_scores_gemma":[0.000009115283,0.00002900574,0.0005090319,0.00004921908,0.00001160712,0.00005454543,0.00005662575,0.5478463,0.002053018,0.4431843,0.006174922,0.00002230689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005228161,0.0004173508,0.9895667,0.001289094,0.00006052572,0.00004974638,0.0003571306,0.0008013789,0.002229847],"genre_scores_gemma":[0.1536479,0.0009179859,0.8405428,0.0004461673,0.0001503008,0.000203951,0.001145285,0.0002763663,0.002669253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004546893,"threshold_uncertainty_score":0.01521081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007556286675695394,"score_gpt":0.2575494992656701,"score_spread":0.2499932125899747,"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."}}