The rapid advancements in rechargeable Li-ion battery (LIB) technology over the last decade has revolutionized several key industries such as transportation and consumer electronics. However, new battery chemistries are needed to meet the rapidly growing demand and to improve the power density, safety, reliability, and lifetime of LIBs. Molecular modeling has become an integral part of the design cycle of new battery chemistries. It enables rapid evaluation and screening of large chemical and material design space thereby, helping industries reduce the time required to bring the new technology to the market. In this talk, I will introduce my research at the intersection of physics-based modeling and machine learning for battery materials design. In particular, I will discuss two broad research topics: 1) Application of advanced machine learning techniques to accelerate the discovery of novel catholyte and anolyte molecules for redox-flow batteries. 2) Application of density functional theory and molecular dynamics simulations to model the complex reactions at electrode-electrolyte interfaces leading to the formation of solid electrolyte interphase in batteries. The second portion of the talk will be focused on introducing the capabilities of Schrodinger’s digital chemistry platform for materials design. I will briefly introduce Schrodinger’s latest advancements in the field machine learning force fields for modeling complex materials such as liquid electrolytes, inorganic cathode coatings and polymer electrolytes, paving the way for efficient design of novel materials for next generation batteries. Finally, I will conclude the talk by providing a brief overview of Schrodinger’s education and training program which can be leveraged by students and researchers to incorporate molecular modeling in their own research. Bio-data of the speaker: Garvit Agarwal is Senior Scientist and Scientific Lead for Energy Storage at Schrodinger, working to extend and apply molecular modeling tools for the accelerated discovery of next-generation clean energy technologies. Garvit obtained his Ph.D. in Materials Science and Engineering from the University of Connecticut. He worked as a post-doctoral researcher in the Materials Science Division at Argonne National Laboratory prior to joining the Materials Science team at Schrodinger in 2021.