{"id":"W2038828876","doi":"10.1063/1.3609924","title":"Optimized energy landscape exploration using the <i>ab initio</i> based activation-relaxation technique","year":2011,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Regroupement Québécois sur les Matériaux de Pointe","funders":"","keywords":"Saddle point; Ab initio; Energy landscape; Relaxation (psychology); Saddle; Density functional theory; Computer science; Statistical physics; Algorithm; Ab initio quantum chemistry methods; Diffusion; Materials science; Computational chemistry; Physics; Chemistry; Mathematics; Molecule; Mathematical optimization; Thermodynamics; Geometry; Quantum mechanics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001858778,0.0000835683,0.0001163275,0.00001707307,0.00003455675,0.00002010488,0.0001501342,0.00004518772,0.00002971537],"category_scores_gemma":[0.00001720673,0.00004736341,0.00004815414,0.0001036875,0.00002646843,0.0003274614,0.00001066676,0.0001196328,7.837591e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002420478,"about_ca_system_score_gemma":0.00001935644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008327671,"about_ca_topic_score_gemma":6.322215e-8,"domain_scores_codex":[0.9994904,0.00004114735,0.0002259107,0.00003260125,0.0001329478,0.00007699978],"domain_scores_gemma":[0.9994975,0.00009541753,0.0001820229,0.0001115766,0.00009170436,0.00002179881],"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":[0.00004948277,0.00001591665,0.000001985629,0.000008348597,0.00001895571,1.84223e-7,0.000246262,0.01037463,0.9887345,0.0001502152,0.0002504119,0.000149094],"study_design_scores_gemma":[0.0002144028,0.00001169962,0.000002608836,0.00003873464,0.00003617201,0.000005376604,0.00007351756,0.01597387,0.9784578,0.005066911,0.00005625117,0.00006264469],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6654348,0.00006688611,0.3335028,0.0000529701,0.0001877322,0.00007282822,0.000001988023,0.0000425942,0.0006374134],"genre_scores_gemma":[0.9964573,0.00002394119,0.003013459,0.0001228361,0.0003565023,0.000004130396,0.000003923555,0.00001721462,6.36906e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3310226,"threshold_uncertainty_score":0.1931424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04740142585098649,"score_gpt":0.2276561066538602,"score_spread":0.1802546808028737,"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."}}