Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning

TL;DR

This paper introduces a novel Bayesian optimization framework with a specialized machine learning model for efficient, multicriteria catalyst discovery, significantly reducing computational costs in high-dimensional search spaces.

Contribution

It presents UPNet, an uncertainty-aware atomistic ML model integrated into Bayesian optimization for accelerated catalyst discovery with interpretable features.

Findings

Achieves 10x reduction in DFT calculations

Enables multicriteria catalyst design optimization

Provides high prediction accuracy and interpretable features

Abstract

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.