Machine learning method to determine concentrations of structural defects in irradiated materials

TL;DR

This paper introduces a machine learning approach to efficiently predict defect concentrations in irradiated materials, reducing computational complexity while maintaining high accuracy, demonstrated on uranium nitride.

Contribution

The paper presents a novel machine learning method that replaces traditional cluster dynamics calculations for defect growth prediction in irradiated materials.

Findings

Accurately predicts defect concentrations across various conditions.

Reduces computational time compared to traditional methods.

Applicable to multiple materials beyond uranium nitride.

Abstract

The formation and subsequent growth of structural defects in an irradiated material can strongly influence the material's performance in technological and industrial applications. Predicting how the growth of defects affects material performance is therefore a pressing problem in materials science. One common computational approach that is used to examine defect growth is cluster dynamics, a method which employs a system of mean-field rate equations to track the time evolution of concentrations of individual defect types. However, the computational complexity of performing cluster dynamics can limit its practical implementation, specifically in the context of exploring a broad set of physical conditions corresponding to, for example, different temperatures and pressures. Here, we present a machine learning approach to circumvent the computational challenges of performing cluster dynamics while maintaining high accuracy in the prediction of defect concentrations. The method is illustrated on the nuclear material uranium nitride but is broadly applicable to other materials. The developed data-driven method is shown to accurately capture complex correlations between material properties, temperature, irradiation conditions, and the concentration of defects.