  The need for advanced materials has led to the development of complex,
multi-component alloys or solid-solution alloys. These materials have shown
exceptional properties like strength, toughness, ductility, electrical and
electronic properties. Current development of such material systems are
hindered by expensive experiments and computationally demanding
first-principles simulations. Atomistic simulations can provide reasonable
insights on properties in such material systems. However, the issue of
designing robust potentials still exists. In this paper, we explore a deep
convolutional neural-network based approach to develop the atomistic potential
for such complex alloys to investigate materials for insights into controlling
properties. In the present work, we propose a voxel representation of the
atomic configuration of a cell and design a 3D convolutional neural network to
learn the interaction of the atoms. Our results highlight the performance of
the 3D convolutional neural network and its efficacy in machine-learning the
atomistic potential. We also explore the role of voxel resolution and provide
insights into the two bounding box methodologies implemented for voxelization.

Materials Science;Machine Learning;Computational Physics;Machine Learning