Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning

arXiv:2404.12445v1 Announce Type: new
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 …

abstract arxiv bayesian catalyst challenges computational conversion cs.ce cs.lg discovery energy health however human optimization performance physics.chem-ph representation representation learning screening spaces study sustainable sustainable energy type vast