Npj Comput. Mater.:材料生长缺失数据—贝叶斯优化
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来自日本NTT公司基础研究所的Yuki K. Wakabayashi等,提出了一种处理材料生长中缺失数据的贝叶斯优化方法。作者通过将虚拟数据模拟和真实材料合成相结合,特别是通过机器学习辅助分子束外延生长铁磁流体SrRuO3薄膜,以此证明了该贝叶斯优化方法的有效性。作者通过在广泛三维参数空间中的开发和探索,仅通过运行35次MBE生长,便获得了具有 80.1 的高残余电阻率的拉伸应变 SrRuO3 薄膜,这是迄今为止报道的拉伸应变 SrRuO3 薄膜中最高的,同时也补充了缺失数据。该工作提出的方法为假设实验失败的多维参数范围内,提供了一种灵活的优化算法,这将提高高通量材料生长和自主材料生长的效率,同时在各种材料的生长中发挥重要作用。
该文近期发表于npj Computational Materials 8:180(2022),英文标题与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。
Bayesian optimization with experimental failure for high-throughput materials growth
Yuki K. Wakabayashi, Takuma Otsuka, Yoshiharu Krockenberger, Hiroshi Sawada, Yoshitaka Taniyasu & Hideki Yamamoto
A crucial problem in achieving innovative high-throughput materials growth with machine learning, such as Bayesian optimization (BO), and automation techniques has been a lack of an appropriate way to handle missing data due to experimental failures. Here, we propose a BO algorithm that complements the missing data in optimizing materials growth parameters. The proposed method provides a flexible optimization algorithm that searches a wide multi-dimensional parameter space. We demonstrate the effectiveness of the method with simulated data as well as in its implementation for actual materials growth, namely machine-learning-assisted molecular beam epitaxy (ML-MBE) of SrRuO3, which is widely used as a metallic electrode in oxide electronics. Through the exploitation and exploration in a wide three-dimensional parameter space, while complementing the missing data, we attained tensile-strained SrRuO3 film with a high residual resistivity ratio of 80.1, the highest among tensile-strained SrRuO3 films ever reported, in only 35 MBE growth runs.
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