{"id":"W4389912021","doi":"10.48550/arxiv.2312.09733","title":"Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Argonne National Laboratory; Office of Science; Natural Sciences and Engineering Research Council of Canada; European Commission; Oak Ridge National Laboratory; CERN; U.S. Department of Energy; Eusko Jaurlaritza; National Science Foundation","keywords":"Supercomputer; Computer science; Perspective (graphical); Quantum computer; Computational science; Data science; Identification (biology); Computational model; Quantum; Face (sociological concept); Parallel computing; Simulation; Artificial intelligence; Physics","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":[0.009954589,0.0009262842,0.001453467,0.001668241,0.003016278,0.006676862,0.002981658,0.008455439,0.01134897],"category_scores_gemma":[0.01018139,0.000557949,0.001048768,0.002985734,0.01011217,0.0198948,0.004869693,0.01031659,0.002381075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00334751,"about_ca_system_score_gemma":0.007784756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003202509,"about_ca_topic_score_gemma":0.003445128,"domain_scores_codex":[0.9969096,0.001313931,0.0001008654,0.0002847402,0.0009776903,0.0004131958],"domain_scores_gemma":[0.9877731,0.007681882,0.0002650809,0.0009590515,0.002037169,0.001283612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006933149,0.0001356849,0.0002486031,0.0007924223,0.00002903892,0.00008502443,0.0002381617,0.003954718,0.0004112007,0.8766546,0.05679841,0.06058286],"study_design_scores_gemma":[0.00003475352,0.0000636649,0.0001817228,0.0005668119,0.00001153281,0.00007379364,0.0005173681,0.008681502,0.0003103742,0.8482305,0.1412889,0.00003907458],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.005610876,0.3397072,0.05265544,0.5530947,0.007008247,0.00008577712,0.0003341833,0.0005429381,0.0409606],"genre_scores_gemma":[0.1874186,0.6231775,0.1095439,0.04274676,0.02119257,0.0005786418,0.0005826592,0.0004568955,0.0143026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01134897,"threshold_uncertainty_score":0.0526455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08160126984975699,"score_gpt":0.2469802737629348,"score_spread":0.1653790039131778,"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."}}