{"id":"W3033996315","doi":"10.2172/1561222","title":"Harnessing the power of ab initio calculations, distributed computing and machine learning to efficiently locate extreme molecules for use in carbon-based solar cells (Final Technical Report)","year":2020,"lang":"en","type":"report","venue":"","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Virtual screening; Computer science; Supercomputer; Kriging; Computational science; Photovoltaic system; Process (computing); Ab initio; Computational chemistry; Chemistry; Machine learning; Parallel computing; Molecular dynamics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006122105,0.0005451303,0.0004370921,0.0004714134,0.0003960427,0.0008641853,0.0005786292,0.0003982662,0.003057301],"category_scores_gemma":[0.000829439,0.0002251884,0.0002561722,0.0005404446,0.0005440849,0.0008639008,0.0005703116,0.001063294,0.0007011684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004308784,"about_ca_system_score_gemma":0.0004330462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001101272,"about_ca_topic_score_gemma":0.0008777576,"domain_scores_codex":[0.9997942,0.00003835277,0.000006430304,0.0000298358,0.0001148822,0.00001630336],"domain_scores_gemma":[0.9997703,0.0001302214,0.00001969941,0.00003512939,0.00003051515,0.00001412256],"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":[0.0001840202,0.000354965,0.001374183,0.0005256729,0.00008233768,0.0001721337,0.00007357299,0.5447896,0.0781597,0.03233887,0.008390777,0.3335542],"study_design_scores_gemma":[0.00004855459,0.00009962593,0.0004117899,0.0000159119,0.00001140271,0.00003586168,0.000009763692,0.9401808,0.04016459,0.01091944,0.008087606,0.00001472923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1487905,0.003964739,0.8212038,0.001335477,0.0003291331,0.0002004844,0.0006557095,0.003990352,0.01952976],"genre_scores_gemma":[0.5790261,0.002839138,0.4115902,0.0001714699,0.0001688999,0.0002749971,0.0009353093,0.0006516954,0.004342099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003057301,"threshold_uncertainty_score":0.01022768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06322282575725187,"score_gpt":0.3063591863481231,"score_spread":0.2431363605908712,"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."}}