CMI researcher Fei Zhou joined Lawrence Livermore National Laboratory (LLNL) in 2013 as a staff scientist after a postdoc at UCLA and earning a doctorate in Physics from MIT. His research interests are computational materials modeling, including rare earth compounds for alloys, permanent magnet and solid-state lighting, Li-ion battery materials for energy storage, and strongly correlated materials. He is developing new computational methodologies, including static correlation effects in first-principles electronic structure calculations, as well as machine-learning methods (compressive sensing, neural networks).
As a member of CMI focus area on Crosscutting Research, he focuses on modeling advanced aluminum cerium alloys in collaboration with experimental collaborators in CMI. By leveraging the rapid advancement of machine-learning techniques that are revolutionizing many industries, CMI is developing a quantitative understanding of the solidification kinetics of Al-Ce alloys under various conditions. The solidification process is of the utmost importance for the quality of the produced alloys, since it has direct and decisive effects on the resulting mechanical properties.
Also, he is involved in atomistic modeling of rare earth magnets using advanced electronic structure tools and machine-learning algorithms. The overarching goal of these research activities is to accelerate the design and development of new critical materials with computational predictions and to reduce the time- and resource-consumption of expensive optimization procedures.