Finding new materials to serve as the next generation catalysts, batteries, solar cells, superconductors or electronic devices can have a potentially transformative impact on our lives and society. Our group at Georgia Tech seeks to harness the power of computing and machine learning to accelerate this discovery process, with the eventual goal of fully realizing materials by inverse design. We accomplish this by developing novel methods and tools which incorporate chemical information to model phenomena at the atomic scale, as well as design new materials from the ground up, atom-by-atom. We also work to establish automated, data-driven and domain-informed ecosystems for materials and chemical discovery which can be deployed on the latest supercomputers. Our group is highly multi-disciplinary and collaborative, welcoming people from different backgrounds with a shared desire to learn and make a positive impact on the world through our research.
|Sep 2, 2022||Sidharth Baskaran joins the group as an undergraduate student!|
|Aug 31, 2022||Ian Slagle joins the group as a joint PhD student w/ Faisal Alamgir!|
|Aug 15, 2022||Shuyi Jia and Sarah Eisenach join the group as PhD students!|
|Aug 1, 2022||Dr. Fung joins the School of CSE at Georgia Tech!|