Bayesian optimization with adaptive surrogate models for automated experimental design

التفاصيل البيبلوغرافية
العنوان: Bayesian optimization with adaptive surrogate models for automated experimental design
المؤلفون: Bowen Lei, Tanner Quinn Kirk, Anirban Bhattacharya, Debdeep Pati, Xiaoning Qian, Raymundo Arroyave, Bani K. Mallick
المصدر: npj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)
بيانات النشر: Nature Portfolio, 2021.
سنة النشر: 2021
مصطلحات موضوعية: QA76.75-76.765, Mechanics of Materials, Modeling and Simulation, TA401-492, General Materials Science, Computer software, Materials of engineering and construction. Mechanics of materials, Computer Science Applications
الوصف: 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.
اللغة: English
تدمد: 2057-3960
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f0c9c8a02c72af0f278f7f12c72a81f
https://doaj.org/article/9f2f69e1532d4345b0eb8216ac8fc446
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....3f0c9c8a02c72af0f278f7f12c72a81f
قاعدة البيانات: OpenAIRE