{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.007349458,0.0004894824,0.0005891831,0.0009079875,0.0008292763,0.002175502,0.002059968,0.0004870205,0.001135599],"category_scores_gemma":[0.001165487,0.0004115567,0.0003570935,0.000497004,0.0005119185,0.00008677229,0.006891799,0.001976687,0.001934368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000636642,"about_ca_system_score_gemma":0.001475392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002045505,"about_ca_topic_score_gemma":0.00002785183,"domain_scores_codex":[0.9930607,0.0007070138,0.0008092077,0.001946343,0.001979141,0.001497612],"domain_scores_gemma":[0.9964662,0.000503962,0.0001830899,0.001544163,0.0009259891,0.0003766221],"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":[0.0001225912,0.0001744288,0.000006229947,0.002871406,0.00002695707,0.00003676923,0.001590912,0.5831257,0.3753942,0.02138524,0.01509347,0.0001721198],"study_design_scores_gemma":[0.0002035837,0.0001255022,0.00001968873,0.001599569,0.00003526093,0.00001258604,0.00007535458,0.9337488,0.02177219,0.04094407,0.001071685,0.0003916575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6207914,0.001362663,0.3454736,0.005586538,0.01050708,0.005753207,0.004315782,0.001595113,0.004614593],"genre_scores_gemma":[0.9741389,0.00003550385,0.01852654,0.0000431354,0.001176209,0.001591003,0.0001572134,0.000140312,0.004191228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.353622,"threshold_uncertainty_score":0.9998336,"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."}}