Sukrit Mukhopadhyay

Sukrit Mukhopadhyay started his career at Dow in May, 2013, after receiving his Ph.D. degree on “Theoretical Studies of Electronic Properties and Electronic Processes in Conjugated Molecules” from Indian Institute of Science (Bangalore) and post-doctoral research at Georgia Institute of Technology, under Jean-Luc Brédas supervision. During early part of his career at Dow, Sukrit primarily contributed to modeling of electronic, transport, emission and device properties of organic conjugated molecules and polymers, used in various stacks of an OLED device. His work has contributed to the design of new molecular architectures that can be used in displays. Post 2015, he shifted his focus towards developing new methodologies (in collaboration with Tom Miller at Caltech, which resulted in a software start-up company (ENTOS)) and modeling reaction rates, designing catalyst packages for olefin polymerization, ring-opening of epoxides to form polyols and various machine learning approaches, applied to the development of new catalyst architectures and polymeric blends. Currently, in collaboration with Regina Barzilay and Klavs Jensen at MIT, he is actively pursuing variational autoencoders and graph-based methods to generate molecular materials that are relevant to a wide range of applications. In parallel, Sukrit is also exploring opportunities to combine natural language processing to extract data from proprietary and non-proprietary literature and high throughput experimental research with of-the-shelf machine learning algorithms and computed electronic and steric descriptors of materials (molecules and polymers) to build a wide variety of predictive and explanatory empirical models.