Insight into structural and thermodynamic properties of nanoparticles is crucial for designing optimal catalysts with enhanced activity and stability. In this work, 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 atomistic simulations to high-resolution experiments.
Liang et al. (Tue,) studied this question.