Calculations of Real-System Nanoparticles Using Universal Neural Network Potential PFP

TL;DR

This paper validates a universal neural network potential, PFP, for accurately modeling the stability, activity, and dynamics of various real-system nanoparticles, offering a computationally efficient alternative to traditional methods.

Contribution

It introduces and validates a universal neural network potential, PFP, capable of describing diverse nanoparticles and their interactions with high accuracy and reduced computational cost.

Findings

PFP accurately predicts properties of monometallic Ru nanoparticles.

PFP successfully models ternary alloy nanoparticles and NO adsorption on Rh.

Molecular dynamics simulations with PFP effectively describe large supported Pt nanoparticles.

Abstract

It is essential to explore the stability and activity of real-system nanoparticles theoretically. While applications of theoretical methods for this purpose can be found in literature, the expensive computational costs of conventional theoretical methods hinder their massive applications to practical materials design. With the recent development of neural network algorithms along with the advancement of computer systems, neural network potentials have emerged as a promising candidate for the description of a wide range of materials, including metals and molecules, with a reasonable computational time. In this study, we successfully validate a universal neural network potential, PFP, for the description of monometallic Ru nanoparticles, PdRuCu ternary alloy nanoparticles, and the NO adsorption on Rh nanoparticles against first-principles calculations. We further conduct molecular dynamics simulations on the NO-Rh system and challenge the PFP to describe a large, supported Pt nanoparticle system.