Welcome to the Mannodi 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 and chalcogenide perovskites, canonical group IV, III-V and II-VI compounds, and zincblende-derived ternary and quaternary chalcogenides) for enhanced optoelectronic performance.
PI: Arun Mannodi Kanakkithodi
Assistant Professor, Materials Engineering
Ph: 765-496-0337
Email: amannodi@purdue.edu
School of Materials Engineering
Neil Armstrong Hall of Engineering
701 West Stadium Avenue
West Lafayette, IN 47907-2045
Arun Mannodi Kanakkithodi: Selected Honors
DARPA Young Faculty Award (YFA) (2025).
ACS Materials Au Rising Star in Materials Science (2024).
DOE Solar Energy Technology Office (SETO) Small Innovation Projects in Solar (SIPS) award (2023).
Scialog Fellow, Automating Chemical Laboratories, RCSA (2023).
Functional Materials Division (FMD) Young Leaders Professional Development Award, TMS (2023).
Rising Star in Computational Materials Science, Elsevier (2023).
Emerging Leader, Modeling and Simulation in Materials Science and Engineering (2021).
Distinguished Young Investigator award, Argonne National Laboratory (2020).
Arun Mannodi Kanakkithodi: Selected Publications
M. Biswas, R.Desai, G. Bidna, and A. Mannodi-Kanakkithodi, "Unified Graph-based Interatomic Potential for Perovskite Structure Optimization", Journal of Chemical Information and Modeling. doi: 10.1021/acs.jcim.5c01611 (2026).
R. Desai, J. Ahn, A. Strachan, and A. Mannodi-Kanakkithodi, "Bridging the Synthesizability Gap in Perovskites by Combining Computations, Literature Data, and PU Learning", Machine Learning Science and Technology., 6, 045061 (2025).
M.H. Rahman, S. Rojsatien, D. Krasikov, M.K.Y. Chan, M. Bertoni, and A. Mannodi-Kanakkithodi*, "First Principles Investigation of Dopants and Defect Complexes in CdSe_(x)Te_(1−x)", Solar Energy Materials and Solar Cells. 293, 113857 (2025).
M.H. Rahman and A. Mannodi-Kanakkithodi*, "Defect Modeling in Semiconductors: The Role of First Principles Simulations and Machine Learning", Journal of Physics: Materials, 8, 022001 (2025).
M.H. Rahman, M. Biswas, and A. Mannodi-Kanakkithodi, "Understanding Defect-Mediated Ion Migration in Semiconductors using Atomistic Simulations", ACS Materials Au, 2024 Rising Stars in Materials Science collection, 4, 6, 557–573 (2024).
M.H. Rahman, P. Gollapalli, P. Manganaris, S.K. Yadav, G. Pilania, B. DeCost, K. Choudhary, and A. Mannodi-Kanakkithodi, "Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks", APL Machine Learning, 2, 016122 (2024).
J. Yang, P. Manganaris, A. Mannodi-Kanakkithodi, "A High-Throughput Computational Dataset of Halide Perovskite Alloys", Digital Discovery, 2, 856-870 (2023).