High-performance catalysts are crucial for sustainable energy conversion and human health. However, the discovery of catalysts faces challenges due to the absence of efficient approaches to navigating vast and high-dimensional structure and composition spaces. In this study, we propose a high-throughput computational catalyst screening approach integrating density functional theory (DFT) and Bayesian Optimization (BO). Within the BO framework, we propose an uncertainty-aware atomistic machine learning model, UPNet, which enables automated representation learning directly from high-dimensional catalyst structures and achieves principled uncertainty quantification. Utilizing a constrained expected improvement acquisition function, our BO framework simultaneously considers multiple evaluation criteria. Using the proposed methods, we explore catalyst discovery for the CO2 reduction reaction. The results demonstrate that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization, leading to significant reduction of computing power and time (10x reduction of required DFT calculations) in high-performance catalyst discovery.

翻译：高性能催化剂对于可持续能源转换和人类健康至关重要。然而，由于缺乏有效方法来探索庞大且高维的结构与组分空间，催化剂发现面临诸多挑战。本研究提出了一种集成密度泛函理论(DFT)与贝叶斯优化(BO)的高通量计算催化剂筛选方法。在BO框架中，我们提出了一种具有不确定性感知能力的原子级机器学习模型UPNet，该模型能够直接从高维催化剂结构中自动学习表征，并实现原则性的不确定性量化。通过采用约束期望改进采集函数，我们的BO框架可同时考虑多个评价标准。利用所提方法，我们探索了CO2还原反应的催化剂发现。结果表明，该方法在实现高预测精度的同时，兼具可解释的特征提取能力与多标准设计优化功能，从而将高性能催化剂发现所需的计算功耗与时间显著降低（使所需DFT计算量减少10倍）。