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It is essential to explore the stability and activity of real-systemnanoparticles theoretically. While applications of theoretical methods for thispurpose can be found in literature, the expensive computational costs ofconventional theoretical methods hinder their massive applications to practicalmaterials design. With the recent development of neural network algorithmsalong with the advancement of computer systems, neural network potentials haveemerged as a promising candidate for the description of a wide range ofmaterials, including metals and molecules, with a reasonable computationaltime. In this study, we successfully validate a universal neural networkpotential, PFP, for the description of monometallic Ru nanoparticles, PdRuCuternary alloy nanoparticles, and the NO adsorption on Rh nanoparticles againstfirst-principles calculations. We further conduct molecular dynamicssimulations on the NO-Rh system and challenge the PFP to describe a large,supported Pt nanoparticle system.

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