Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys

TL;DR

This paper introduces a CNN model that accurately predicts diffusion barrier spectra in multi-principal element alloys, significantly aiding rapid alloy discovery and understanding local configuration effects.

Contribution

A novel CNN approach for predicting path-dependent vacancy migration energy barriers in MPEAs across compositions and configurations.

Findings

CNN accurately predicts diffusion barriers in MPEAs

Uncovered relevant length scale of local configurations

Facilitates rapid alloy screening for desired properties

Abstract

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. However, how to efficiently identify optimal compositions from such a large design space for targeted properties is a grand challenge in material science. Here we developed a convolutional neural network (CNN) model that can accurately and efficiently predict path-dependent vacancy migration energy barriers, which are critical to diffusion behaviors and many high-temperature properties, of MPEAs at any compositions and with different chemical short-range orders within a given alloy system. The success of the CNN model makes it promising for developing a database of diffusion barriers for different MPEA systems, which would accelerate alloy screening for the discovery of new compositions with desirable properties. Besides, the length scale of local configurations relevant to migration energy barriers is uncovered, and the implications of this success to other aspects of materials science are discussed.