  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.
