{"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/bayesian-optimization-for-materials-design","title":"Bayesian optimization for materials design","arxiv_id":"1506.01349","date":"2015-06-03","proceeding":null,"authors":["Peter I. Frazier","Jialei Wang"],"abstract":"We introduce Bayesian optimization, a technique developed for optimizing\ntime-consuming engineering simulations and for fitting machine learning models\non large datasets. Bayesian optimization guides the choice of experiments\nduring materials design and discovery to find good material designs in as few\nexperiments as possible. We focus on the case when materials designs are\nparameterized by a low-dimensional vector. Bayesian optimization is built on a\nstatistical technique called Gaussian process regression, which allows\npredicting the performance of a new design based on previously tested designs.\nAfter providing a detailed introduction to Gaussian process regression, we\nintroduce two Bayesian optimization methods: expected improvement, for design\nproblems with noise-free evaluations; and the knowledge-gradient method, which\ngeneralizes expected improvement and may be used in design problems with noisy\nevaluations. Both methods are derived using a value-of-information analysis,\nand enjoy one-step Bayes-optimality.","url_abs":"http://arxiv.org/abs/1506.01349v1","url_pdf":"http://arxiv.org/pdf/1506.01349v1.pdf","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":"bayesian-optimization-for-materials-design","repo_url":"https://github.com/clancyLab/NCM2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.01349","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}