# Boris Kozinsky

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

## Biography

Boris Kozinsky is the Thomas D. Cabot Associate Professor of Computational Materials Science at the Harvard School of Engineering and Applied Sciences and Principal Scientist at Bosch Research. He studied at MIT for his B.S. degrees in Physics, Mathematics, and Electrical Engineering and Computer Science, and received his PhD degree in Physics also from MIT. He then established and led the atomistic computational materials design team at Bosch Research in Cambridge MA. In 2018 he started the Materials Intelligence Research group at Harvard that works at the intersection of fundamental materials physics, computational chemistry, and data science. His group develops and combines atomistic and electronic structure computations with machine learning for understanding and predicting quantum-level microscopic effects, particularly ionic, electronic and thermal transport and transformations in materials for energy storage and conversion. His work on the development and application of computational methods led to computation-driven design of materials for thermoelectrics, batteries, catalysts, and functional polymers.

Publications, preprints and patents on Google Scholar