{"id":"W2071267590","doi":"10.1002/qua.24795","title":"Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces","year":2014,"lang":"en","type":"article","venue":"International Journal of Quantum Chemistry","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":220,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bottleneck; Quantum; Computer science; Ab initio; Artificial neural network; Potential energy; Statistical physics; Function (biology); Multi-mode optical fiber; Reduction (mathematics); Biological system; Physics; Artificial intelligence; Quantum mechanics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309424,0.0002138412,0.0003170765,0.00005931269,0.0001579773,0.0002889631,0.0007783945,0.00009747016,0.0001396922],"category_scores_gemma":[0.0004068898,0.0001844496,0.0001168891,0.00006107493,0.0002231368,0.0002998314,0.00015912,0.0001675805,0.000001308012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007848101,"about_ca_system_score_gemma":0.00009687793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005385957,"about_ca_topic_score_gemma":0.000001970345,"domain_scores_codex":[0.9978645,0.0001050544,0.0006202261,0.0003308224,0.0007458559,0.0003335354],"domain_scores_gemma":[0.9982753,0.0002768278,0.0007812388,0.0001528278,0.0003688148,0.0001450031],"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":[0.0002974364,0.00005477912,0.0003058875,0.00003509855,0.00002065589,0.00001516309,0.00001044223,0.4294018,0.5526665,0.01635566,0.0003563568,0.000480191],"study_design_scores_gemma":[0.0007515902,0.0001159151,0.0005688904,0.00007627449,0.00002717736,0.0002461419,0.00001473281,0.9344825,0.05386826,0.00914442,0.0005045512,0.0001996175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7853423,0.00009320864,0.2113341,0.001081175,0.002009969,0.00003434717,0.00003448451,0.00002402895,0.00004642548],"genre_scores_gemma":[0.9847605,0.000002875998,0.01377833,0.0002210095,0.001140023,0.000004762224,0.00002585705,0.000023487,0.00004314065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5050806,"threshold_uncertainty_score":0.7521638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168171702476078,"score_gpt":0.2484356903704982,"score_spread":0.2367539733457374,"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."}}