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.
|Jan 13, 2023||Masters student Anshuman Sinha joins the group!|
|Dec 1, 2022||Undergraduates Rithwik and Pranav join the group!|
|Oct 25, 2022||Our paper on inverse design of materials is now published in Machine Learning: Science and Technology! Congrats Shuyi!|
|Oct 21, 2022||Our paper on high entropy alloy nanocatalysts in collaboration with Skrabalak group is now published in ACS Nano!|
|Sep 2, 2022||Sidharth Baskaran, an undergraduate student, joins the group!|