Machine learning potentials for accelerated nuclear fuel qualification

New nuclear fuels need to be qualified to ensure safe operation in a reactor under a range of conditions. Due to the historic need for many lengthy and expensive irradiation campaigns, qualification poses an immense time and cost burden that discourages new nuclear reactor technologies that utilize novel fuel types from being designed and/or brought to market. Accelerated fuel qualification (AFQ) is a concept combining advanced modeling and simulation with complementary experiments in order to reduce the qualification time and cost by targeting fewer integral tests. To this end, we derive new accurate machine learning (ML) potentials for actinides that provide high-fidelity reproduction of quantum mechanical (QM) forces at the same low cost of classical force fields. We employ an active learning approach that autonomously augments the QM training data set to iteratively refine the ML potential. We compare our ML potential against existing classical force fields as well as with experimental data (e.g., thermal expansion and elastic properties).