{"id":"W4388333184","doi":"10.1016/j.mattod.2023.10.002","title":"Engineering the fracture resistance of 2H-transition metal dichalcogenides using vacancies: An in-silico investigation based on HRTEM images","year":2023,"lang":"en","type":"article","venue":"Materials Today","topic":"2D Materials and Applications","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Argonne National Laboratory; Basic Energy Sciences; Quest High Performance Computing; Multidisciplinary University Research Initiative; Office of Naval Research; Northwestern University; University of Victoria; U.S. Department of Energy; Office of Science; Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"High-resolution transmission electron microscopy; Transition metal; Materials science; Fracture (geology); In silico; Nanotechnology; Chemistry; Composite material; Transmission electron microscopy; Catalysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114613,0.0001818459,0.0002837658,0.0001331395,0.0001311339,0.0001242887,0.0002453562,0.00008958262,0.0002764565],"category_scores_gemma":[0.00007734554,0.000138256,0.00003773233,0.0003546916,0.00008645008,0.0002493981,0.00002966223,0.00005183657,0.00004300393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004624251,"about_ca_system_score_gemma":0.00005213585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002075982,"about_ca_topic_score_gemma":0.00003486748,"domain_scores_codex":[0.9985148,0.0001795771,0.0004566395,0.000316094,0.0002644993,0.0002683506],"domain_scores_gemma":[0.9991754,0.0001193333,0.0001862825,0.0004111041,0.00005721151,0.0000506879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003828329,0.00002157193,0.00001389107,0.0001040752,0.000002568882,0.000002437979,0.0003420992,0.02365606,0.9754038,0.0002997522,0.0001087586,0.000006733397],"study_design_scores_gemma":[0.0001904974,0.00002472678,0.004093565,0.0001316726,0.00001829034,9.452625e-7,0.0001004808,0.00151296,0.9929382,0.0005834617,0.0002562277,0.0001489817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973863,0.00001668798,0.0006816855,0.0007218753,0.0004126331,0.0003553688,0.0002663052,0.0001372102,0.00002187031],"genre_scores_gemma":[0.9980902,0.00000748818,0.001295661,0.0002051855,0.0001565331,0.0001048067,0.00008791817,0.00003124928,0.00002096542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0221431,"threshold_uncertainty_score":0.5637917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124597303447335,"score_gpt":0.2537771971687206,"score_spread":0.2325312241342473,"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."}}