A transferable artificial neural network model for atomic forces in nanoparticles

TL;DR

This paper introduces a single neural network approach to accurately predict atomic forces and energies in complex molecular systems, reducing computational costs and enabling scalable simulations of nanoparticles.

Contribution

The authors propose a novel single neural network method for fitting energies and forces in multicomponent systems, improving efficiency and scalability over traditional multi-network approaches.

Findings

Accurately predicts atomic forces in nanoparticles.

Enables geometry optimization and molecular dynamics simulations.

Allows extrapolation from small to large systems.

Abstract

We have designed a new method to fit the energy and atomic forces using a single artificial neural network (SANN) for any number of chemical species present in a molecular system. The traditional approach for fitting the potential energy surface (PES) for a multicomponent (MC) system using artificial neural network (ANN) is to consider n number of networks for n number of chemical species in the system. This shoots the computational cost and makes it difficult to apply to a system containing more number of species. We present a new strategy of using a SANN to compute energy and forces of a chemical system. Since, atomic forces are significant for geometry optimizations and molecular dynamics simulations (MDS) for any chemical system, their accurate prediction is of utmost importance. So, to predict the atomic forces, we have modified the traditional way of fitting forces from underlying energy expression. We have applied our strategy to study geometry optimizations and dynamics in gold-silver nanoalloys and thiol protected gold nanoclusters. Also, force fitting has made it possible to train smaller size systems and extrapolate the parameters to make accurate predictions for larger systems. This proposed strategy has definitely made the mapping and fitting of atomic forces easier and can be applied to a wide variety of molecular systems.