Ordered bimetallic alloys have long been a central focus of computational catalyst research, and in recent years, exciting progress has been made in both scientific understanding and practical catalyst design of these materials. However, some drawbacks of bimetallics, such as the inherently limited design space that they offer, are becoming apparent, and high entropy alloys, which contain quasi-random arrangements of five or more elements, may be useful in overcoming these limitations. In this talk, we present an overview of some of the modeling and design challenges associated with these inherently complex materials. After outlining several key design considerations relating to the activity and stability of high entropy alloys, we describe combined electronic structure, catalysis science, and data science-based strategies that have been developed to address some of these challenges. We then demonstrate how these approaches may be applied to model chemistries such as ammonia decomposition and electrocatalytic hydrogen consumption, and we close with a few perspectives on future directions for the field.

First principles and machine learning modeling of catalysis on high entropy alloys: applications to thermal and electrocatalysis

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.