{"id":"W4414684245","doi":"10.17615/3d4v-kd69","title":"Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates","year":2024,"lang":"en","type":"article","venue":"Carolina Digital Repository (University of North Carolina at Chapel Hill)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Nanjing University; National Natural Science Foundation of China; Yunnan University; Hunan Normal University; Canada Research Chairs","keywords":"Polarizability; Macromolecule; Molecular dynamics; Electromagnetic shielding; Nuclear magnetic resonance spectroscopy; Wave function; Molecule; Speedup; Density functional theory","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002776051,0.000216243,0.0004159708,0.0001448944,0.0002750455,0.00006042778,0.0002482314,0.0001163094,0.000009728066],"category_scores_gemma":[0.0002917726,0.000217335,0.0001313835,0.0001518795,0.0009015118,0.0004143399,0.0002408635,0.0001248248,9.89824e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000677606,"about_ca_system_score_gemma":0.00006551686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000152344,"about_ca_topic_score_gemma":0.00002868261,"domain_scores_codex":[0.9985862,0.00009031495,0.0003251207,0.0004939609,0.0002580299,0.0002464035],"domain_scores_gemma":[0.9983661,0.0007674625,0.0003212192,0.0002106772,0.0002196851,0.0001148366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004914345,0.00007543896,0.2355503,0.002890815,0.00006107512,0.00005960597,0.005605322,0.01509999,0.7340144,0.0002011491,0.000005978416,0.005944433],"study_design_scores_gemma":[0.001818797,0.003032134,0.09137015,0.001186564,0.0003219802,0.0003661587,0.003803932,0.6220317,0.27309,0.0007037849,0.00134008,0.0009346968],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953521,0.001155341,0.002374557,0.00003586634,0.0002167512,0.0002702706,0.0003406339,0.0000929824,0.0001614673],"genre_scores_gemma":[0.998977,0.00005191934,0.0006754925,0.000005643448,0.00003623733,0.00000117167,0.00003344758,0.00002027455,0.0001987835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6069317,"threshold_uncertainty_score":0.8862666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004637784550719764,"score_gpt":0.1752562155673106,"score_spread":0.1706184310165909,"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."}}