125 citations · 145 across the 6 of their papers we have counts for
12 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…
A Deep Learning Model for Atomic Structures Prediction Using X-ray Absorption Spectroscopic Data
Liang Li, Mindren Lu, Maria K. Y. Chan
A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spect…
Atomistic manipulation of reversible oxidation and reduction in Ag by electron beam
Huaping Sheng, He Zheng, Lifen Wang +6
Employing electrons for direct control of nanoscale reaction is highly desirable since it provides fabrication of nanostructures with different properties at atomic resolution and…