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在贝叶斯优化框架中，优化实验设计已被认为是一种规避传统探索设计空间局限性的重要方法。近期，在新材料设计和发现领域中，一种基于贝叶斯优化的设计范式吸引了研究者的广泛兴趣。这种设计既考虑了现有设计空间知识的需求，又具有加快迭代设计过程的目标。此外，它还可以识别材料中重要属性之间差异的关键因素，有助于我们更好地理解其背后物理/化学机制。然而，传统的基于高斯过程的贝叶斯优化，在面对高维设计空间，特别是初始信息很少的情况时，如何发现占主导地位的自由度、如何处理协变量之间的交互作用，无疑是一个极大的挑战。
来自德州农工大学的Bani K. Mallick 教授领导的团队，提出了一个基于自适应替代模型的贝叶斯优化、完全自动化的实验设计框架。作者通过在传统的贝叶斯优化过程中使用贝叶斯模型，替代基于高斯过程的机器学习模型，更具适应性和灵活性。该框架使用了贝叶斯多元自适应回归样条(BMARS)和贝叶斯加性回归树(BART)作为替代模型，其中BMARS能够捕捉很有挑战性的模式，而BART则通过集成多个单独的树产生了强大的回归算法，展现出了很好的各向异性和适应性。在模拟研究和现实世界的材料科学案例研究上，该方法都被证实具有更高的搜索效率和更强的稳定性，对自主材料研究的实验设计有重要意义。
该文近期发表于npj Computational Materials 7: 194 (2021)，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 adaptive surrogate models for automated experimental design
Bowen Lei, Tanner Quinn Kirk, Anirban Bhattacharya, Debdeep Pati, Xiaoning Qian, Raymundo Arroyave& Bani K. Mallick
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.
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