{"id":"W4402901058","doi":"10.1063/5.0205702","title":"Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures","year":2024,"lang":"en","type":"article","venue":"APL Machine Learning","topic":"Thermal properties of materials","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal conductivity; van der Waals force; Condensed matter physics; Heterojunction; Materials science; Conductivity; Thermal; Thermodynamics; Physics; Quantum mechanics; Composite material; Molecule","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":[0.0002276349,0.0003159639,0.0002288508,0.0004456152,0.0002528411,0.0003041487,0.0004456341,0.0004959584,0.0005285202],"category_scores_gemma":[0.0007094953,0.000177056,0.000234014,0.0003679527,0.0002904537,0.0004529493,0.0002150052,0.0003144284,0.0001000839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004392856,"about_ca_system_score_gemma":0.0004662435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002699398,"about_ca_topic_score_gemma":0.00284246,"domain_scores_codex":[0.9999405,0.00001247593,0.000004100091,0.00000871379,0.00002356421,0.0000106597],"domain_scores_gemma":[0.9998502,0.00008043081,0.00001671593,0.00001359367,0.00003019765,0.00000896354],"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":[0.00005534361,0.00003733072,0.003278744,0.0001206058,0.00003255655,0.0001438146,0.00003970168,0.9324023,0.04897957,0.008021417,0.0002959643,0.006592631],"study_design_scores_gemma":[0.000002465472,0.000006168576,0.0005428334,0.000002892971,0.000001747051,0.000008229047,0.000006182221,0.9939556,0.004694871,0.0007157734,0.00005927638,0.000004101542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565789,0.0003309912,0.03796812,0.0001689967,0.00001999575,0.00001685773,0.0003255928,0.000171327,0.004419363],"genre_scores_gemma":[0.995025,0.00009562036,0.004456104,0.00001527658,0.000003953595,0.00001973727,0.0001482963,0.00001136374,0.0002246758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002699398,"threshold_uncertainty_score":0.005367339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671517170345829,"score_gpt":0.2600759217301456,"score_spread":0.2433607500266873,"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."}}