{"id":"W4392366251","doi":"10.21203/rs.3.rs-3829677/v1","title":"Lightweight equivariant model for efficient interatomic potentialpredictions","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Education, India; Guangzhou Municipal Science and Technology Bureau; National Natural Science Foundation of China","keywords":"Equivariant map; Physics; Statistical physics; Computer science; Mathematics; Pure mathematics","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":[0.0006905727,0.001055983,0.001401909,0.0007667164,0.0005290388,0.00147955,0.003081602,0.001456436,0.01171631],"category_scores_gemma":[0.003264145,0.0006005722,0.001112516,0.0009455587,0.0006827657,0.003718994,0.002847103,0.002353053,0.004471767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007784694,"about_ca_system_score_gemma":0.001145475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00261332,"about_ca_topic_score_gemma":0.005044098,"domain_scores_codex":[0.9992118,0.0001790382,0.00004247767,0.0001642947,0.0003088568,0.00009342161],"domain_scores_gemma":[0.9989569,0.0002772269,0.00005932405,0.0004642557,0.0001768711,0.00006539433],"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":[0.000420533,0.0002700071,0.0009593086,0.0001536762,0.00009682434,0.0002275397,0.00006767414,0.6797544,0.006381603,0.1050519,0.0181479,0.1884686],"study_design_scores_gemma":[0.000005198241,0.000007498706,0.00002844702,0.000002082252,0.000003453452,0.00001059022,0.000003764019,0.9695137,0.0008338176,0.02887003,0.0007173456,0.000004049109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01840428,0.0001895473,0.9710617,0.0002888886,0.0001311924,0.00004613155,0.0007293645,0.00598606,0.003162755],"genre_scores_gemma":[0.6888101,0.0003002499,0.2874654,0.0004411742,0.0002221565,0.000236015,0.003621289,0.002628518,0.01627497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01171631,"threshold_uncertainty_score":0.03919494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06024192323136295,"score_gpt":0.4065112961207039,"score_spread":0.346269372889341,"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."}}