Dr. Keerthan Rao is an AI-Augmented Sorting Postdoctoral Researcher at INL’s Analytical Chemistry department. He is a computational chemist with experience in density functional theory and machine learning of molecular and periodic level. His expertise and interests include working with materials like heterogeneous and homogeneous catalysts, perovskites, battery materials, ligand chemistry and critical materials recovery. At INL, he has worked on building computational pipelines to automate DFT calculations and used his skills in python to build a multiprocessing software for a high throughput data collection machine at the PDU. He has also developed a machine learning pipeline that can process hyperspectral images to classify different types of plastics with high accuracy. He is interested in collaborating with others to explore exciting science at the intersection of DFT, machine learning, and image processing. Keerthan obtained his Doctorate in Chemistry from the University of Kentucky under the advisement of Dr. Chad Risko and holds a master’s degree in chemical engineering from Purdue University where he was first introduced to computational chemistry working under Dr. Jeffrey Greeley.

Computational Chemistry, DFT, Machine Learning, Solid-state materials for adsorption, Catalysis