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.
@article{arxiv.2107.00963,
title = {Calculations of Real-System Nanoparticles Using Universal Neural Network Potential PFP},
author = {Gerardo Valadez Huerta and Yusuke Nanba and Iori Kurata and Kosuke Nakago and So Takamoto and Chikashi Shinagawa and Michihisa Koyama},
journal= {arXiv preprint arXiv:2107.00963},
year = {2021}
}