3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys

TL;DR

This paper introduces a 3D deep learning approach using voxelized atomic configurations to accurately model atomistic potentials in complex solid-solution alloys, aiming to improve material property predictions.

Contribution

It proposes a novel voxel-based 3D CNN method for developing atomistic potentials in complex alloys, enhancing robustness and efficiency over traditional approaches.

Findings

The 3D CNN effectively learns atomic interactions.

Voxel resolution impacts model performance.

Bounding box voxelization methods influence results.

Abstract

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.