Publication Date

7-9-2021

Department

Ames Laboratory; Materials Science and Engineering; Mechanical Engineering

Campus Units

Ames Laboratory, Materials Science and Engineering, Mechanical Engineering

OSTI ID+

1809238

Report Number

IS-J 10545

DOI

10.1016/j.commatsci.2021.110693

Journal Title

Computational Materials Science

Volume Number

198

First Page

110693

Abstract

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 (DFT) approach, for calculating potentials will provide a promising schema for accurate atomistic simulations of structure and dynamics of general multi-principle element alloys.

DOE Contract Number(s)

AC02-07CH11358

Language

en

Publisher

Iowa State University Digital Repository, Ames IA (United States)

Available for download on Saturday, July 09, 2022

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