18 citations · 18 across the 2 of their papers we have counts for
5 papers
Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets
Maciej P. Polak, Ryan Jacobs, Arun Mannodi-Kanakkithodi +2
Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications rang…
Data-Driven Design of Novel Halide Perovskite Alloys
Arun Mannodi-Kanakkithodi, Maria K. Y. Chan
The great tunability of the properties of halide perovskites presents new opportunities for optoelectronic applications as well as significant challenges associated with exploring…
Defect Physics of Pseudo-cubic Mixed Halide Lead Perovskites from First Principles
Arun Mannodi-Kanakkithodi, Ji-Sang Park, Alex B. F. Martinson +1
Owing to the increasing popularity of lead-based hybrid perovskites for photovoltaic (PV) applications, it is crucial to understand their defect physics and its influence on their…
Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides
Arun Mannodi-Kanakkithodi, Michael Y. Toriyama, Fatih G. Sen +3
The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semic…
Phonon-pump electronic-probe study of methylammonium lead iodide reveals electronically decoupled organic and inorganic sublattices
Peijun Guo, Arun Mannodi-Kanakkithodi, Jue Gong +10
Organic-inorganic hybrid perovskites such as methylammonium lead iodide (CH3NH3PbI3) are game-changing semiconductors for solar cells and light-emitting devices owing to their exce…