Adaptive sampling methods via machine learning for materials screening
Adaptive sampling methods via machine learning for materials screening
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DOI：
10.1080/27660400.2022.2039573
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Akira Takahashi;Y. Kumagai;Hirotaka Aoki;Ryo Tamura;F. Oba
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文献类型：
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作者：
Akira Takahashi;Y. Kumagai;Hirotaka Aoki;Ryo Tamura;F. Oba
结合第一性原理计算和贝叶斯优化（BO）的高通量虚拟筛选作为一种高效的材料探索方法备受关注。虚拟筛选的目的往往是寻找性能满足某一目标准则的材料，而传统的BO则是寻找全局极值。最近的一些工作通过转换目标属性来使用传统的BO。另一方面，先前提出了一种自适应采样方法，其中采集函数基于数据点在特定范围内达到目标属性的概率[Kishio et al., Chemom]。智能。实验室。系统学报，127,70(2013)]。在本文中，我们证明了这种自适应采样对于探索性能满足目标标准的材料是有效的。我们使用由第一性原理计算构建的内部数据库进行了材料勘探模拟，并比较了自适应采样和传统BO方法的性能。此外，我们评估和讨论了扩展到多目标问题的获取函数的性能，同时考虑了多目标特性。图形抽象
ABSTRACT High-throughput virtual screening by using a combination of first-principles calculations and Bayesian optimization (BO) has attracted much attention as a method for efficient material exploration. The purpose of the virtual screening is often to search for the materials whose properties meet a certain target criterion, while the conventional BO aims to find the global extremum. Some recent works use the conventional BO by converting target properties for such motivation. On the other hand, an adaptive sampling method, where the acquisition function is based on the probability that a data point achieves a target property within a specific range, is suggested previously [Kishio et al., Chemom. Intell. Lab. Syst. 127, 70 (2013)]. In this paper, we demonstrate that such adaptive sampling is effective for the exploration of the materials whose properties meet target criteria. We conducted simulations of material exploration using an in-house database constructed by first-principles calculations and compared the performance of the adaptive sampling and conventional BO approaches. Furthermore, we evaluate and discuss the performance of acquisition functions extended to multi-objective problems for material exploration, considering multiple-target properties simultaneously. Graphical abstract