{"id":"W4386714071","doi":"10.1038/s41524-023-01104-6","title":"Hyperactive learning for data-driven interatomic potentials","year":2023,"lang":"en","type":"article","venue":"npj Computational Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Research Councils UK","keywords":"Interatomic potential; Ab initio; Molecular dynamics; Polyethylene glycol; Cluster (spacecraft); Process (computing); Statistical physics; Materials science; Computer science; Chemical physics; Molecular physics; Chemistry; Physics; Computational chemistry; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003882444,0.0009497997,0.001192883,0.0009201001,0.0007134206,0.001628094,0.00373018,0.002225759,0.003602362],"category_scores_gemma":[0.009575652,0.0009085248,0.001064366,0.0007793884,0.002163161,0.001970135,0.003262325,0.003034305,0.0006095562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001571924,"about_ca_system_score_gemma":0.001312407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003350102,"about_ca_topic_score_gemma":0.002933106,"domain_scores_codex":[0.9988433,0.0005499094,0.00005234489,0.0001588924,0.0003115691,0.00008389688],"domain_scores_gemma":[0.995207,0.003639166,0.0001564065,0.0003112488,0.0005135871,0.0001726325],"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":[0.0000366129,0.0000414644,0.0002926094,0.00006175003,0.00003283497,0.000060875,0.00005983363,0.9473211,0.0008642771,0.03074422,0.0006370432,0.01984735],"study_design_scores_gemma":[0.00000253691,0.000003804708,0.000008149481,0.000002266463,7.67285e-7,0.000003069161,0.000002488311,0.9927704,0.00015694,0.006930669,0.0001174116,0.000001556448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00882466,0.0002237702,0.9880427,0.0002768846,0.00003345622,0.00005156767,0.00005486911,0.0002705431,0.00222158],"genre_scores_gemma":[0.5845227,0.0003704652,0.4065572,0.0006433258,0.00009245214,0.0007235006,0.0004404777,0.0003287211,0.006321134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003882444,"threshold_uncertainty_score":0.02053255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04394644643524804,"score_gpt":0.3389252481607739,"score_spread":0.2949788017255259,"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."}}