Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning

IF 11.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Yuehui Xian, Xiangdong Ding, Xue Jiang, Yumei Zhou, Jun Sun, Dezhen Xue, Turab Lookman
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引用次数: 0

Abstract

Materials design often becomes an expensive black-box optimization problem due to limitations in balancing exploration-exploitation trade-offs in high-dimensional spaces. We propose a reinforcement learning (RL) framework that effectively navigates the complex design spaces through two complementary approaches: a model-based strategy utilizing surrogate models for sample-efficient exploration, and an on-the-fly strategy when direct experimental feedback is available. This approach demonstrates better performance in high-dimensional spaces (D ≥ 6) compared to Bayesian optimization (BO) with the Expected Improvement (EI) acquisition function through more dispersed sampling patterns and better landscape learning capabilities. Furthermore, we observe a synergistic effect when combining BO’s early-stage exploration with RL’s adaptive learning. Evaluations on both standard benchmark functions (Ackley, Rastrigin) and real-world high-entropy alloy data, demonstrate statistically significant improvements (p < 0.01) over traditional BO with EI, particularly in complex, high-dimensional scenarios. This work addresses limitations of existing methods while providing practical tools for guiding experiments.

Abstract Image

使用强化学习解锁材料设计贝叶斯全局优化之外的黑箱
材料设计往往成为一个昂贵的黑盒优化问题,因为在高维空间中难以平衡探索与开发之间的权衡。我们提出了一个强化学习(RL)框架,该框架通过两种互补的方法有效地导航复杂的设计空间:利用代理模型进行样本高效探索的基于模型的策略,以及当直接实验反馈可用时的动态策略。与具有期望改进(EI)获取函数的贝叶斯优化(BO)相比,该方法通过更分散的采样模式和更好的景观学习能力,在高维空间(D≥6)上表现出更好的性能。此外,我们观察到BO的早期探索与RL的适应性学习相结合会产生协同效应。对标准基准函数(Ackley, Rastrigin)和现实世界高熵合金数据的评估表明,与传统的EI BO相比,在统计上有显著改善(p < 0.01),特别是在复杂的高维场景中。这项工作解决了现有方法的局限性,同时为指导实验提供了实用的工具。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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