Theoretical and Computational Works on Oxide Nuclear Fuel Materials

Abstract

This chapter briefly reviews theoretical and computational works on nuclear fuel oxide materials. This review is not a complete one overviewing whole of the field but a biased one by the author’s limited experience and knowledge. However, we believe that it is interesting for students and researchers who want to immediately catch the trend of theoretical and computational works on nuclear fuel materials. Especially, we pile up our notions in density functional theory (DFT) calculations on oxide fuel compounds. The readers can easily learn why DFT approaches are important but difficult on the compounds. Furthermore, we show a brand new methodological technique called machine-learning molecular dynamics (MLMD) automatically bridging the time and space gap between DFT and molecular dynamics (MD) in comparison with conventional MD using empirical potentials. We believe that MLMD will be a central scheme in the future. In addition to our specialties, DFT and MD topics, we briefly give an introduction on physics, chemistry, and multiscale simulations with material behaviors on oxide fuel materials through our learning from several literature. We believe that the references in each section will also be useful for students and young researchers.