Cost-effective materials discovery: Bayesian optimization across multiple information sources,Materials Horizons

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Cost-effective materials discovery: Bayesian optimization across multiple information sources
Materials Horizons ( IF 12.2 ) Pub Date : 2020-06-08 , DOI: 10.1039/d0mh00062k
Henry C. Herbol _{1,

2,

3,

4} , Matthias Poloczek _{4,

5,

6} , Paulette Clancy _{1,

2,

3,

4}

Affiliation

Applications of Bayesian optimization to problems in the materials sciences have primarily focused on consideration of a single source of data, such as DFT, MD, or experiments. This work shows how it is possible to incorporate cost-effective sources of information with more accurate, but expensive, sources as a means to significantly accelerate materials discovery in the computational sciences. Specifically, we compare the performance of three surrogate models for multi-information source optimization (MISO) in combination with a cost-sensitive knowledge gradient approach for the acquisition function: a multivariate Gaussian process regression, a cokriging method exemplified by the intrinsic coregionalization model, and a new surrogate model we created, the Pearson-r coregionalization model. To demonstrate the effectiveness of this MISO approach to the study of commonly encountered materials science problems, we show MISO results for three test cases that outperform a standard efficient global optimization (EGO) algorithm: a challenging benchmark function (Rosenbrock), a molecular geometry optimization, and a binding energy maximization. We outline factors that affect the performance of combining different information sources, including one in which a standard EGO approach is preferable to MISO.

中文翻译：

具有成本效益的材料发现：跨多个信息源的贝叶斯优化

贝叶斯优化在材料科学问题中的应用主要集中在考虑单一数据源，例如DFT，MD或实验。这项工作表明，如何将具有成本效益的信息源与更准确但昂贵的信息源相结合，以此作为显着加快计算科学中材料发现的一种手段。具体来说，我们比较了三种用于多信息源优化（MISO）的替代模型的性能与针对成本函数的知识敏感性梯度方法（用于获取功能）的比较：多元高斯过程回归，以固有共域化模型为例的协同克里格方法，以及我们创建的新代理模型Pearson- r共区域化模型。为了证明这种MISO方法在研究常见材料科学问题方面的有效性，我们展示了三个测试用例的MISO结果，它们优于标准高效的全局优化（EGO）算法：具有挑战性的基准函数（Rosenbrock），分子几何优化，并使结合能最大化。我们概述了影响组合不同信息源的性能的因素，其中包括一种标准的EGO方法比MISO更好的方法。

更新日期：2020-08-03

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