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Structure-property relationships is a key materials science concept thatenables the design of new materials. In the case of materials for applicationin radiation environments, correlating radiation tolerance with fundamentalstructural features of a material enables materials discovery. Here, we use amachine learning model to examine the factors that govern amorphizationresistance in the complex oxide pyrochlore ($A_2B_2$O$_7$). We examine thefidelity of predictions based on cation radii and electronegativities, theoxygen positional parameter, and the energetics of disordering and amorphizingthe 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 butwhen the machine learning model is restricted to only the $B$=Ti pyrochlores,the energetics of disordering and amorphization are optimal. This work providesnew insight into the factors that govern the amorphization susceptibility andhighlights the ability of machine learning approaches to generate that insight.

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