Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by substantial experimental costs, extensive resource utilization, and lengthy development periods. To address these challenges, modern approaches often employ machine learning (ML) techniques such as Bayesian Optimization (BO), which streamline the search for optimal materials by iteratively selecting experiments that are most likely to yield beneficial results. However, traditional BO methods, while beneficial, often struggle with balancing the trade-off between exploration and exploitation, leading to sub-optimal performance in material discovery processes. This paper introduces a novel Threshold-Driven UCB-EI Bayesian Optimization (TDUE-BO) method, which dynamically integrates the strengths of Upper Confidence Bound (UCB) and Expected Improvement (EI) acquisition functions to optimize the material discovery process. Unlike the classical BO, our method focuses on efficiently navigating the high-dimensional material design space (MDS). TDUE-BO begins with an exploration-focused UCB approach, ensuring a comprehensive initial sweep of the MDS. As the model gains confidence, indicated by reduced uncertainty, it transitions to the more exploitative EI method, focusing on promising areas identified earlier. The UCB-to-EI switching policy dictated guided through continuous monitoring of the model uncertainty during each step of sequential sampling results in navigating through the MDS more efficiently while ensuring rapid convergence. The effectiveness of TDUE-BO is demonstrated through its application on three different material datasets, showing significantly better approximation and optimization performance over the EI and UCB-based BO methods in terms of the RMSE scores and convergence efficiency, respectively.

翻译：材料进步在技术发展中扮演着关键角色。然而，具有所需性能材料的发现与开发过程常因高昂实验成本、大量资源消耗及漫长开发周期而受阻。为应对这些挑战，现代方法常采用机器学习（ML）技术，如贝叶斯优化（BO），通过迭代选择最可能产生有益结果的实验来简化最优材料的搜索过程。然而，传统贝叶斯优化方法虽具优势，却往往难以平衡探索与利用间的权衡，导致材料发现过程性能欠佳。本文提出一种新颖的阈值驱动UCB-EI贝叶斯优化（TDUE-BO）方法，该方法动态整合置信上界（UCB）与期望改进（EI）采集函数的优势，以优化材料发现过程。与传统贝叶斯优化不同，本方法聚焦于高效导航高维材料设计空间（MDS）。TDUE-BO以探索导向的UCB方法启动，确保对MDS进行全面初始扫描。当模型因不确定性降低而获得置信度时，其转为更具利用性的EI方法，聚焦于先前识别的有前景区域。这种通过连续采样各步骤中持续监测模型不确定性所引导的UCB至EI切换策略，使得在确保快速收敛的同时能更高效地导航MDS。通过在三类不同材料数据集上的应用，TDUE-BO的有效性得到验证：相较于基于EI和UCB的贝叶斯优化方法，其在均方根误差评分与收敛效率方面均展现出显著更优的逼近与优化性能。