{"id":"W6887537335","doi":"10.17169/refubium-44216","title":"Machine Learning Classification of Local Environments in Molecular Crystals","year":2024,"lang":"en","type":"article","venue":"Refubium (Universitätsbibliothek der Freien Universität Berlin)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Chemistry; Natural Sciences and Engineering Research Council of Canada; Division of Materials Research; Deutsche Forschungsgemeinschaft; National Science Foundation","keywords":"Graph; Molecular graph; Convolutional neural network; Representation (politics); Encoding (memory); Set (abstract data type); Molecular descriptor; Molecular dynamics","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":[0.0004541026,0.0003030017,0.0004636202,0.0007421263,0.0002978585,0.0006708486,0.0005773899,0.0005170486,0.0003951881],"category_scores_gemma":[0.001391568,0.0001889067,0.0003147247,0.0005397535,0.0007645236,0.001083458,0.0005248117,0.0006121537,0.0001132253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000531813,"about_ca_system_score_gemma":0.0003295106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00192494,"about_ca_topic_score_gemma":0.002934052,"domain_scores_codex":[0.9998475,0.00004559118,0.000007892162,0.00004797027,0.00002955757,0.00002151588],"domain_scores_gemma":[0.9994128,0.0002520274,0.0001285889,0.00008809899,0.00007237612,0.00004625547],"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":[0.0002003953,0.0001459544,0.01569843,0.00008227751,0.00004768601,0.0001218512,0.0000845085,0.8777931,0.02067247,0.008489144,0.001117873,0.07554621],"study_design_scores_gemma":[0.000003168841,0.00001214151,0.0006282122,0.000001898003,0.000001433934,0.000008556523,0.0000140505,0.9940687,0.002435701,0.002716023,0.0001068082,0.000003274623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.821356,0.0003458433,0.1760784,0.0002276397,0.00001554831,0.00003160572,0.0002649304,0.0007429115,0.0009371156],"genre_scores_gemma":[0.9692035,0.0001113378,0.02990949,0.00002325409,0.000007748987,0.00002039181,0.0003441499,0.00003158737,0.0003485824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00192494,"threshold_uncertainty_score":0.003858566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009784050481215126,"score_gpt":0.2301802588294918,"score_spread":0.2203962083482767,"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."}}