  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.
