Boris Kozinsky works at the intersection of fundamental physics of materials properties, efficient computational algorithms, and machine learning methods. The overall vision is to leverage the rapidly expanding power of computation and data science to accelerate discovery and design of new practical materials needed for breakthroughs in energy storage and conversion systems. Performance of these systems is controlled by atomic-scale transport and reaction mechanisms and their coupling at different length and time scales that are difficult to probe by experiment alone. Atomistic and electronic structure computations are emerging as a powerful tool for understanding and distilling the design rules governing quantum-level microscopic effects. Interests Physics of transport phenomena in complex functional materials and interfaces Atomistic and electronic structure computation methods accelerated by machine learning techniques Catalysts, thermoelectrics, batteries, alloys, polymers, 2D materials