{"id":"W4285593336","doi":"10.1103/physrevb.106.045204","title":"Benchmarking exchange-correlation potentials with the mstar60 dataset: Importance of the nonlocal exchange potential for effective mass calculations in semiconductors","year":2022,"lang":"en","type":"article","venue":"Physical review. B./Physical review. B","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Compute Canada","keywords":"Hybrid functional; Density functional theory; Physics; Semiconductor; Benchmark (surveying); Condensed matter physics; Statistical physics; Materials science; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001820785,0.001761266,0.00137366,0.001718742,0.001050962,0.001520378,0.003197659,0.002208955,0.003081277],"category_scores_gemma":[0.003427849,0.0003707043,0.001363753,0.002575756,0.0006010818,0.001358997,0.001253311,0.001338621,0.002446381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009530793,"about_ca_system_score_gemma":0.0009267379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005284902,"about_ca_topic_score_gemma":0.007537322,"domain_scores_codex":[0.9989423,0.0002995228,0.00007398445,0.0001778122,0.0004289422,0.00007745568],"domain_scores_gemma":[0.9987268,0.0005735732,0.00008411837,0.0002485554,0.0002951801,0.00007173403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001630676,0.001361877,0.01741027,0.005491069,0.001682894,0.001285548,0.0003038771,0.4941769,0.01849909,0.02878656,0.3538795,0.07549161],"study_design_scores_gemma":[0.001385661,0.0007487207,0.01576132,0.0004137602,0.0002946581,0.0005337885,0.0002918263,0.7490462,0.03351279,0.02474771,0.1730674,0.0001960945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7492439,0.009059356,0.01261851,0.001971106,0.0005631592,0.0002028215,0.1852828,0.008662865,0.03239559],"genre_scores_gemma":[0.4534897,0.002033629,0.04191153,0.0008249812,0.0002390743,0.000478032,0.4937319,0.002247666,0.005043517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005284902,"threshold_uncertainty_score":0.0105083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075349114961647,"score_gpt":0.3291656320714087,"score_spread":0.3184121409217922,"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."}}