Semi-supervised generative approach to chemical disorder: application to point-defect formation in uranium-plutonium mixed oxides.

Chemical disorder has a major impact on the characterization of the atomic-scale properties of highly complex chemical compounds, such as the properties of point defects. Due to the vast amount of possible atomic configurations, the study of such properties becomes intractable if treated with direct sampling. In this work, we propose an alternative approach, in which samples are selected based on the local atomic composition around the defect, and the defect formation energy is obtained as a function of this local composition with a reduced computational cost. We apply this approach to (U, Pu)O nuclear fuels. The formation-energy distribution is computed using machine-learning generative methods, and used to investigate the impact of chemical disorder and the range of influence of local composition on the defect properties. The predicted distributions are then used to calculate the concentration of thermal defects. This approach allows for the first time for the computation of the latter property with a physically meaningful exploration of the configuration space, and opens the way to a more efficient determination of physico-chemical properties in other chemically-disordered compounds such as high-entropy alloys.