Bayesian optimization with adaptive surrogate models for automated experimental design

Abstract Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the...

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Autores principales:	Bowen Lei, Tanner Quinn Kirk, Anirban Bhattacharya, Debdeep Pati, Xiaoning Qian, Raymundo Arroyave, Bani K. Mallick
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/9f2f69e1532d4345b0eb8216ac8fc446
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spelling	oai:doaj.org-article:9f2f69e1532d4345b0eb8216ac8fc4462021-12-05T12:10:17ZBayesian optimization with adaptive surrogate models for automated experimental design10.1038/s41524-021-00662-x2057-3960https://doaj.org/article/9f2f69e1532d4345b0eb8216ac8fc4462021-12-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00662-xhttps://doaj.org/toc/2057-3960Abstract Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies. This can have a significant impact on modern scientific discovery, in particular autonomous materials discovery, which can be viewed as an optimization problem aimed at looking for the maximum (or minimum) point for the desired materials properties. The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions. In this paper, we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure, namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees. They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions. Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.Bowen LeiTanner Quinn KirkAnirban BhattacharyaDebdeep PatiXiaoning QianRaymundo ArroyaveBani K. MallickNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Bowen Lei Tanner Quinn Kirk Anirban Bhattacharya Debdeep Pati Xiaoning Qian Raymundo Arroyave Bani K. Mallick Bayesian optimization with adaptive surrogate models for automated experimental design
description	Abstract Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies. This can have a significant impact on modern scientific discovery, in particular autonomous materials discovery, which can be viewed as an optimization problem aimed at looking for the maximum (or minimum) point for the desired materials properties. The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions. In this paper, we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure, namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees. They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions. Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.
format	article
author	Bowen Lei Tanner Quinn Kirk Anirban Bhattacharya Debdeep Pati Xiaoning Qian Raymundo Arroyave Bani K. Mallick
author_facet	Bowen Lei Tanner Quinn Kirk Anirban Bhattacharya Debdeep Pati Xiaoning Qian Raymundo Arroyave Bani K. Mallick
author_sort	Bowen Lei
title	Bayesian optimization with adaptive surrogate models for automated experimental design
title_short	Bayesian optimization with adaptive surrogate models for automated experimental design
title_full	Bayesian optimization with adaptive surrogate models for automated experimental design
title_fullStr	Bayesian optimization with adaptive surrogate models for automated experimental design
title_full_unstemmed	Bayesian optimization with adaptive surrogate models for automated experimental design
title_sort	bayesian optimization with adaptive surrogate models for automated experimental design
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9f2f69e1532d4345b0eb8216ac8fc446
work_keys_str_mv	AT bowenlei bayesianoptimizationwithadaptivesurrogatemodelsforautomatedexperimentaldesign AT tannerquinnkirk bayesianoptimizationwithadaptivesurrogatemodelsforautomatedexperimentaldesign AT anirbanbhattacharya bayesianoptimizationwithadaptivesurrogatemodelsforautomatedexperimentaldesign AT debdeeppati bayesianoptimizationwithadaptivesurrogatemodelsforautomatedexperimentaldesign AT xiaoningqian bayesianoptimizationwithadaptivesurrogatemodelsforautomatedexperimentaldesign AT raymundoarroyave bayesianoptimizationwithadaptivesurrogatemodelsforautomatedexperimentaldesign AT banikmallick bayesianoptimizationwithadaptivesurrogatemodelsforautomatedexperimentaldesign
_version_	1718372208627154944

Bayesian optimization with adaptive surrogate models for automated experimental design

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