Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case
| Autor: | Ryo Nakayama, Ryota Shimizu, Taishi Haga, Takefumi Kimura, Yasunobu Ando, Shigeru Kobayashi, Nobuaki Yasuo, Masakazu Sekijima, Taro Hitosugi |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Science and Technology of Advanced Materials: Methods, Vol 2, Iss 1, Pp 119-128 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2766-0400 27660400 |
| DOI: | 10.1080/27660400.2022.2066489 |
| Popis: | Materials exploration requires the optimization of a multidimensional space including the chemical composition and synthesis parameters such as temperature and pressure. Bayesian optimization has attracted attention as a method for efficient multidimensional optimization. Appropriate choices of the acquisition function and initial values of the hyperparameters of the kernel functions are essential for the Bayesian optimization of synthesis conditions in a small number of experiments. However, to date, there has been little discussion on how to tune Bayesian optimization for materials exploration, and no guidelines have been provided for materials scientists. In this study, we investigated the optimum initial values of the hyperparameters in Bayesian optimization using one-dimensional model functions that mimic actual materials syntheses. The optimal lengthscale and variance for different process windows of materials synthesis were investigated. It was shown that the use of an appropriate acquisition function and suitable initial values of the hyperparameters of the kernel functions enable the optimization of synthesis conditions in a small number of trials. These results provide insight for enabling fully automated and autonomous materials synthesis using Bayesian optimization and robotics for materials exploration. |
| Databáze: | Directory of Open Access Journals |
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