Arun Mannodi‐Kanakkithodi
Materials Science · Purdue University West Lafayette
Publications
109
Citations
4,529
Est. group size
~16
Recurring co-author estimate
Active years
13
Publishing since 2014
Arun Mannodi-Kanakkithodi's research focuses on computational materials science, using first-principles simulations and machine learning to study defects, dopability, and structure-property relationships in semiconductors and perovskites for applications like solar cells and catalysis. His group develops and applies tools such as graph neural networks, interatomic potentials, and generative models to accelerate the discovery and screening of new materials, including chalcogenide perovskites, halide perovskites, and quantum dots.
Publication output has grown substantially over the last decade, rising from single digits annually in 2017-2021 to a sustained higher rate of 12-19 papers per year from 2022-2025.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Unified Graph-Based Interatomic Potential for Perovskite Structure Optimization
Journal of Chemical Information and Modeling · 2026
- Electronic structure modulation in Ag nanoparticle-Ag <sub>2</sub> Cu <sub>2</sub> O <sub>3</sub> catalyst: a strategy for stable and enhanced oxygen reduction reaction
Nanoscale · 2026
- Defect modeling in semiconductors: the role of first principles simulations and machine learning
Journal of Physics Materials · 2025
- Exploring the Defect Landscape and Dopability of Chalcogenide Perovskite BaZrS<sub>3</sub>
The Journal of Physical Chemistry C · 2025
- High-throughput screening of ternary and quaternary chalcogenide semiconductors for photovoltaics
Computational Materials Science · 2025
- First principles investigation of dopants and defect complexes in CdSe <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si106.svg" display="inline" id="d1e1123"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> </mml:math> Te <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si107.svg" display="inline" id="d1e1131"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> </mml:math>
Solar Energy Materials and Solar Cells · 2025
- Discovering novel halide perovskites using generative adversarial networks and random forest regression
Computational Materials Science · 2025
- Using Machine Learning to Explore Defect Configurations in Cd/Zn-Se/Te Compounds
2025
- Unified Graph-based Interatomic Potential for Perovskite Structure Optimization
ChemRxiv · 2025
- Exploring the defect landscape and dopability of chalcogenide perovskite BaZrS3
arXiv (Cornell University) · 2025
- POLARIS: Perovskite Optimization using LLM-Assisted Refinement and Intelligent Screening
ChemRxiv · 2025
- POLARIS: Perovskite Optimization using LLM-Assisted Refinement and Intelligent Screening
ChemRxiv · 2025
- Bridging the Synthesizability Gap in Perovskites by Combining Computations, Literature Data, and PU Learning
arXiv (Cornell University) · 2025
- Interpretable Graph Neural Network for Predicting Transient Electronic Structures of Semiconductor Quantum Dots
ACS Materials Letters · 2025
- Unified Graph-based Interatomic Potential for Perovskite Structure Optimization
ChemRxiv · 2025
- arXiv (Cornell University)×11
- Computational Materials Science×5
- npj Computational Materials×4
- ChemRxiv×4
- Bulletin of the American Physical Society×4
- Mustafa Kurban
Materials Science · Indiana University
- Brian H. Lee
Materials Science · Purdue University West Lafayette
- Brett M. Savoie
Materials Science · Purdue University West Lafayette
- Ankur K. Gupta
Materials Science · Indiana University
- Farshud Sorourifar
Materials Science · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile