{"id":"W4414857924","doi":"10.1073/pnas.2415662122","title":"Generalized convolutional many-body distribution functional representations","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Canada First Research Excellence Fund; European Commission","keywords":"Kernel (algebra); Scaling; Invariant (physics); Weighting; Reduction (mathematics); Set (abstract data type); Fourier transform; Convolution (computer science)","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":[0.0003759929,0.000497723,0.0004800741,0.000420374,0.0001822137,0.0005861254,0.001175575,0.0007717141,0.003030681],"category_scores_gemma":[0.0009858542,0.0002028018,0.0005454742,0.0005405392,0.0003379296,0.0008686247,0.0005031276,0.0007473475,0.0008303287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007517625,"about_ca_system_score_gemma":0.0007521678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005826475,"about_ca_topic_score_gemma":0.005926419,"domain_scores_codex":[0.9998676,0.00003208527,0.000007060644,0.00002219967,0.00004616574,0.0000250323],"domain_scores_gemma":[0.9997315,0.00008546238,0.00002332547,0.00007022332,0.00007103504,0.00001843555],"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":[0.00006174469,0.00003717186,0.0005813915,0.00007026982,0.00003224088,0.00007004508,0.00003348061,0.8814694,0.004375351,0.05738189,0.00427676,0.05161016],"study_design_scores_gemma":[0.000001338857,0.000002577631,0.00004446411,0.000001591732,0.000001023173,0.000006293548,0.00000150328,0.9952534,0.0003606601,0.003942353,0.0003829834,0.00000180861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05757114,0.0004492743,0.9330384,0.0003948069,0.00006042479,0.00004404385,0.001582855,0.001739114,0.005119949],"genre_scores_gemma":[0.7708838,0.0007039297,0.2127957,0.0003251825,0.00005755658,0.0002627931,0.004479242,0.0003948571,0.01009693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005826475,"threshold_uncertainty_score":0.01158512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0315739614806647,"score_gpt":0.3295252732216478,"score_spread":0.297951311740983,"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."}}