Welcome to the Mannodi-Kanakkithodi Research Group at Purdue University
Research in our group is focused on accelerating the design of novel materials for energy applications via first principles materials modeling, data science, and machine learning. The primary methods we use are quantum mechanics-based density functional theory (DFT) and regression/optimization techniques based on neural networks, Gaussian processes, and random forests. Our research results in the generation of large materials datasets and frameworks for the on-demand prediction of materials properties as well as inverse design of materials with targeted properties. Currently, the group is working on compositional-, structural-, and defect-engineering of semiconductors (including halide perovskites and canonical group IV, III-V and II-VI compounds) for enhanced optoelectronic performance.
About Dr. Mannodi-Kanakkithodi
Arun Mannodi Kanakkithodi is an assistant professor in Materials Engineering at Purdue university. He received his PhD in Materials Science and Engineering from the University of Connecticut in 2017 and worked as a postdoctoral researcher at the Center for Nanoscale Materials in Argonne National Laboratory from 2017 to 2020. His research involves using first principles computational modeling, machine learning, and materials informatics to drive the design of new materials for energy-relevant applications. He is a resident associate in the Nanoscience and Technology Division at Argonne, a regular attendee, presenter, and organizer at the Materials Research Society (MRS) spring and fall meetings, and a co-organizer of the data science and machine learning workshop series as part of the NSF-funded nanoHUB.org.