{"id":"W4252056807","doi":"10.26434/chemrxiv.13551629.v2","title":"Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potentials","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","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; Office of Nuclear Energy; Nuclear Energy University Program; U.S. Department of Energy","keywords":"Molecular dynamics; Melting point; Materials science; Lithium (medication); Chemical physics; Interatomic potential; Atom (system on chip); Work (physics); Absorption (acoustics); Molten salt; Thermodynamics; Chemistry; Computational chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000230972,0.00045968,0.0003886561,0.0002530111,0.0003076272,0.0005037354,0.0008130617,0.0009956132,0.001053173],"category_scores_gemma":[0.0007603728,0.0002665615,0.0003559096,0.0002373595,0.0004234849,0.0005983861,0.0004391793,0.000510406,0.0001431521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009061737,"about_ca_system_score_gemma":0.0007534152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199085,"about_ca_topic_score_gemma":0.008015594,"domain_scores_codex":[0.9999316,0.00001713208,0.000003323531,0.00001267778,0.00002102885,0.00001429586],"domain_scores_gemma":[0.9998509,0.000081465,0.00002152703,0.000008551079,0.00002459323,0.00001295448],"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":[0.00001533248,0.000008666793,0.0002605109,0.000008865917,0.000005735988,0.00001354508,0.000005710117,0.9964624,0.0009013785,0.001095029,0.00006878556,0.001154048],"study_design_scores_gemma":[9.128596e-7,0.000001688963,0.00002003782,4.851805e-7,3.894314e-7,5.744204e-7,8.782344e-7,0.9996809,0.000130904,0.0001297288,0.00003287994,5.66801e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.819515,0.0006377983,0.1649536,0.0006375174,0.00007891787,0.00007429374,0.0004605645,0.0004048685,0.01323737],"genre_scores_gemma":[0.9671817,0.0002126251,0.02898281,0.00006095448,0.00001687033,0.0001113071,0.0003170196,0.00006146976,0.00305525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01199085,"threshold_uncertainty_score":0.0238421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956359525752548,"score_gpt":0.2432499642665238,"score_spread":0.2236863690089983,"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."}}