Machine Learning Interatomic Potentials for Radiation Damage and Physical Properties in Model Fluorite Systems

This project will use machine learning interatomic potentials to study the influence of radiation damage on physical properties of calcium fluoride and uranium dioxide.
Electron irradiation experiments and thermal conductivity measurements will be performed to validate the effectiveness of the developed potentials. The high throughput capability of this method will become an important combinatorial materials
science tool for developing and qualifying new nuclear fuels.