  Simulations at the atomic scale provide a direct and effective way to
understand the mechanical properties of materials. In the regime of classical
mechanics, simulations for the thermodynamic properties of metals and alloys
can be done by either solving the equations of motion or performing Monte Carlo
sampling. The key component for an accurate simulation of such physical systems
to produce faithful physical quantities is the use of an appropriate potential
or a force field. In this paper, we explore the use of methods from the realm
of machine learning to overcome and bypass difficulties encountered when
fitting potentials for atomic systems. Particularly, we will show that
classical potentials can be represented by a dense neural network with good
accuracy.
