Danny Perez, Ph.D.

After obtaining a Ph.D. in Physics from the Université de Montréal in 2006, Danny Perez joined the Theoretical Division of Los Alamos National Laboratory, becoming staff scientist in 2009. His research focuses on the development of novel atomistic, multi-scale, and machine-learning methods, on their implementation in simulation codes, and on their application to a range of energy-related problems. For example, his group investigates radiation damage in materials for fusion and fission nuclear energy, the formation of breakdown precursors in particle accelerators, and the design of new separation chemistries for critical elements. From 2017 to 2024, he led a national effort aiming at combining machine learning with traditional high-performance computing to demonstrate the power of exascale computing for materials simulation, culminating in demonstrations at scale on the first two exascale computers in the US.