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We have designed a new method to fit the energy and atomic forces using asingle artificial neural network (SANN) for any number of chemical speciespresent in a molecular system. The traditional approach for fitting thepotential energy surface (PES) for a multicomponent (MC) system usingartificial neural network (ANN) is to consider n number of networks for nnumber of chemical species in the system. This shoots the computational costand 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 achemical system. Since, atomic forces are significant for geometryoptimizations and molecular dynamics simulations (MDS) for any chemical system,their accurate prediction is of utmost importance. So, to predict the atomicforces, we have modified the traditional way of fitting forces from underlyingenergy expression. We have applied our strategy to study geometry optimizationsand dynamics in gold-silver nanoalloys and thiol protected gold nanoclusters.Also, force fitting has made it possible to train smaller size systems andextrapolate the parameters to make accurate predictions for larger systems.This proposed strategy has definitely made the mapping and fitting of atomicforces easier and can be applied to a wide variety of molecular systems.

A transferable artificial neural network model for atomic forces in nanoparticles