{"id":"W7064459384","doi":"","title":"Characterizing the potential energy surface of two dimensional and bulk materials using high dimensional neural network potentials","year":2018,"lang":"en","type":"dissertation","venue":"e-scholar@UOIT (University of Ontario Institute of Technology)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Surface (topology); Artificial neural network; Energy (signal processing); Potential energy; Work (physics); Density functional theory; Function (biology); Field (mathematics); Potential field","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003780229,0.0003190136,0.0007072395,0.0002565513,0.0005966835,0.0000326086,0.0006609061,0.00029312,0.01299454],"category_scores_gemma":[0.000009409436,0.0003223318,0.0001653327,0.0003143094,0.0004202637,0.0001824221,0.0006187978,0.0005767352,0.000003051957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004826017,"about_ca_system_score_gemma":0.0006301565,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1093183,"about_ca_topic_score_gemma":0.01166642,"domain_scores_codex":[0.9982019,0.00002383834,0.0004552231,0.0004608192,0.0005070001,0.0003512347],"domain_scores_gemma":[0.998124,0.000004318113,0.0008679767,0.0004735728,0.0004607274,0.00006939331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007738773,0.0002746709,0.0004191835,0.000131136,0.0006882458,0.0000402442,0.0003536079,0.001268353,0.9232696,0.005371827,0.0008302103,0.06657905],"study_design_scores_gemma":[0.009290335,0.001219096,0.073768,0.002218749,0.002622435,0.00004702208,0.001882593,0.003169289,0.2314079,0.005313814,0.6666991,0.002361733],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975188,0.00009761385,0.00003084617,0.0002417878,0.0009695946,0.000322589,0.0001609336,0.00001915122,0.0006386659],"genre_scores_gemma":[0.9483531,0.00001210899,0.04556106,0.00001889064,0.0001420457,0.000001275419,0.001050307,0.00002656152,0.004834643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6918617,"threshold_uncertainty_score":0.9999229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009034761668350395,"score_gpt":0.2157629459054861,"score_spread":0.2067281842371357,"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."}}