{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/high-dimensional-discrete-bayesian","title":"High-Dimensional Discrete Bayesian Optimization with Self-Supervised Representation Learning for Data-Efficient Materials Exploration","arxiv_id":null,"date":"2021-09-24","proceeding":"NeurIPS Workshop AI4Scien 2021 12","authors":["Masaki Adachi"],"abstract":"A material exploration model based on high-dimensional discrete Bayesian optimization is introduced. Features were extracted from a large-scale database of ab-initio calculations by self-supervised representation learning. Material exploration was carried out based on 100 prior target values from 6,218 candidate materials. As a baseline, ten human experts of materials science were selected and evaluated their exploration efficiency. Under the same conditions, the proposed discrete algorithm was 1.93 times as efficient as human experts on average, while the conventional continuous algorithm could not outperform them.","url_abs":"https://openreview.net/forum?id=xJhjehqjQeB","url_pdf":"https://openreview.net/pdf?id=xJhjehqjQeB","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"high-dimensional-discrete-bayesian","repo_url":"https://github.com/ma921/banditmaterialsexplorer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}