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Bayesian optimization with adaptive surrogate models for automated experimental design

npj Computational Materials 2021年第1期7卷 1800-1811页

作者： Bowen Lei Tanner Quinn Kirk Anirban Bhattacharya Debdeep Pati Xiaoning Qian raymundo arroyave Bani K.Mallick Department of Statistics Texas A&M UniversityCollege StationTXUSA Department of Mechanical Engineering Texas A&M UniversityCollege StationTXUSA Department of Electrical&Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Computer Science&Engineering Texas A&M UniversityCollege StationTXUSA Department of Materials Science&Engineering Texas A&M UniversityCollege StationTXUSA

Bayesian optimization(BO)is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to ***,optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional *** 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 *** 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 *** 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 *** 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 *** simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.

关键词： functions properties optimization

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