Atomic cluster expansion for Pt-Rh catalysts: From ab initio to the simulation of nanoclusters in few steps

TL;DR

This paper introduces a semi-automated workflow to develop an atomic cluster expansion model for Pt-Rh nanoparticles, enabling accurate and efficient simulations of their properties for catalytic applications.

Contribution

The paper presents a novel workflow for parameterizing atomic cluster expansion models from ab initio data, specifically applied to Pt-Rh nanoparticles for catalysis.

Findings

The ACE model accurately reproduces properties of Pt and Rh metals and their compounds.

The workflow enhances simulation efficiency, enabling direct comparison with experiments.

The method supports the design of better catalysts through reliable nanoparticle modeling.

Abstract

Insight into structural and thermodynamic properties of nanoparticles is crucial for designing optimal catalysts with enhanced activity and stability. We present a semi-automated workflow for parameterizing the atomic cluster expansion (ACE) from ab initio data. The main steps of the workflow are the generation of training data from accurate electronic structure calculations, an efficient fitting procedure supported by active learning and uncertainty indication, and a thorough validation. We apply the workflow to the simulation of binary Pt-Rh nanoparticles that are important for catalytic applications. We demonstrate that the Pt-Rh ACE is able to reproduce accurately a broad range of fundamental properties of the elemental metals as well as their compounds while retaining an outstanding computational efficiency. This enables a direct comparison of simulations to high resolution experiments.