Jianguo Yu is a research staff scientist in the Fuels Modeling and Simulation Department at Idaho National Laboratory. In this capacity, he has been applying fundamental physics to understand and predict how important behaviors involving heat (phonon and electron), charge (electron and ion), and reaction pathway at the electronic, atomic and meso level contribute to material functionalities under extreme conditions. In particular, he has been developing atomistic dynamic charge transfer algorithm, charge optimized many-body (COMB) atomistic empirical potentials, finding functionals with machine learning, and bottom-up multiscale framework to bridge up atomistic methods and meso-scale approach models in a self-consistent manner. He has also been carrying out a variety of modeling and simulations methods spanning from first-principles electronic structure calculations, molecular dynamics (MD) and kinetic Monte Carlo (KMC) simulations to tackle with interfacial phenomena, microstructural and microchemical evolution of nanostructured materials, electron and ion transport phenomena, thin film growth, reactor materials, relative biological effectiveness, catalysis, dynamics of surface reactions, fuel cells, energy storage and conversion (i.e. rechargeable Li/Na-ion batteries).