2021/07/02 by Gerardo Valadez Huerta, Yusuke Nanba, Huerta, Gerardo Valadez +12
Chemistry · Environmental Science · Physics and Astronomy · #Advanced Physical and Chemical Molecular Interactions #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Spectroscopy and Chemometric Analyses #Water Quality Monitoring and Analysis #cond-mat.mtrl-sci
paper · pdf · doi:10.48550/arxiv.2107.00963
Main Manuscript: 14 pages, 6 Figures, Supporting Information: 3 pages, 4 figrues
arxiv created 2021/07/02 · openalex publication_date 2021/07/02 · arxiv updated 2021/07/05 · openalex created_date 2021/07/19 · openalex updated_date 2026/07/28
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.