Machine-learning Accelerated Descriptor Design for Catalyst Discovery: A CO2 to Methanol Conversion Case Study

作     者：Pisal, Prajwal Krejčí, Ondřej Rinke, Patrick 

作者机构：Department of Applied Physics Aalto University P.O. Box 11000 AALTO FI-00076 Finland Department of Physics Technical University of Munich James-Franck-Strasse 1 Garching85748 Germany Atomistic Modeling Center Munich Data Science Institute Technical University of Munich Walther-Von-Dyck Str. 10 Garching85748 Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Methanol 

摘      要：Transforming CO2 into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discovery of novel thermal heterogeneous catalysts, using machine-learned force fields. We propose a new catalytic descriptor, termed adsorption energy distribution, that aggregates the binding energies for different catalyst facets, binding sites, and adsorbates. The descriptor is versatile and can easily be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates. By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys, we offer a powerful tool for catalyst discovery. Finally, we propose new promising candidate materials such as ZnRh and ZnPt3, which to our knowledge, have not yet been tested, and discuss their possible advantage in terms of stability. © 2024, CC BY.