  Atomistic simulations can provide useful insights into the physical
properties of multi-principal-element alloys. However, classical potentials
mostly fail to capture key quantum (electronic-structure) effects. We present a
deep 3D convolutional neural-network (3D CNN) based framework combined with a
voxelization technique to design interatomic potentials for chemically complex
alloys. We highlight the performance of the 3D CNN model and its efficacy in
computing potentials using the medium-entropy alloy TaNbMo. In order to provide
insights into the effect of voxel resolution, we implemented two approaches
based on the inner and outer bounding boxes. An efficient 3D CNN model, which
is as accurate as the density-functional theory approach, for calculating
potentials will provide a promising schema for accurate atomistic simulations
of structure and dynamics of general multi-principle element alloys.
