Using machine learning to identify factors that govern amorphization of irradiated pyrochlores

TL;DR

This study employs machine learning to analyze structural and energetic factors influencing amorphization resistance in pyrochlore oxides, revealing that multiple features collectively predict radiation tolerance, with specific factors being more predictive within certain families.

Contribution

It introduces a machine learning framework to identify key structural and energetic factors governing amorphization in pyrochlores, advancing materials design for radiation environments.

Findings

Multiple factors influence amorphization resistance.

Radii and electronegativities predict well across pyrochlore families.

Energetics are more predictive within Ti-based pyrochlores.

Abstract

Structure-property relationships is a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural features of a material enables materials discovery. Here, we use a machine learning model to examine the factors that govern amorphization resistance in the complex oxide pyrochlore ($A_2B_2$O$_7$). We examine the fidelity of predictions based on cation radii and electronegativities, the oxygen positional parameter, and the energetics of disordering and amorphizing the material. No one factor alone adequately predicts amorphization resistance. We find that, when multiple families of pyrochlores (with different B cations) are considered, radii and electronegativities provide the best prediction but when the machine learning model is restricted to only the $B$=Ti pyrochlores, the energetics of disordering and amorphization are optimal. This work provides new insight into the factors that govern the amorphization susceptibility and highlights the ability of machine learning approaches to generate that insight.