activity
20152022
most citedTheory of Thermal Relaxation of Electrons in Semiconductors

125 citations · 461 across the 17 of their papers we have counts for

collaborators

18 papers

cond-mat.mtrl-sci2022★ 18 cited

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…

cond-mat.mtrl-sci2021

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…

cond-mat.mtrl-sci2020★ 17 cited

Ultrafast formation of transient 2D diamond-like structure in twisted bilayer graphene

Duan Luo, Dandan Hui, Bin Wen +17

Due to the absence of matching carbon atoms at honeycomb centers with carbon atoms in adjacent graphene sheets, theorists predicted that a sliding process is needed to form AA, AB,…

physics.app-ph2019

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…

cond-mat.mtrl-sci2019

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…

physics.comp-ph2019★ 2 cited

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…

cond-mat.mtrl-sci2019

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…

cond-mat.mtrl-sci2019★ 68 cited

Computational prediction of lattice thermal conductivity -- a comparison of molecular dynamics and Boltzmann transport approaches

Marcello Puligheddu, Yi Xia, Maria K. Y. Chan +1

The predictive modeling of lattice thermal conductivity is of fundamental importance for the understanding and design of materials for a wide range of applications. Two major appro…

physics.comp-ph2018★ 2 cited

Comparing optimization strategies for force field parameterization

Fatih G. Sen, Badri Narayanan, Jeffrey Larson +7

Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability…

cond-mat.mtrl-sci2018★ 35 cited

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…

cond-mat.mtrl-sci2018★ 65 cited

Anharmonic stabilization and lattice heat transport in rocksalt -GeTe

Yi Xia, Maria K. Y. Chan

Peierls-Boltzmann transport equation, coupled with third-order anharmonic lattice dynamics calculations, has been widely used to model lattice thermal conductivity () in bul…

cond-mat.mtrl-sci2017★ 36 cited

Lattice thermal transport in group II-alloyed PbTe

Yi Xia, James M. Hodges, Mercouri G. Kanatzidis +1

PbTe, one of the most promising thermoelectric materials, has recently demonstrated thermoelectric figure of merit () of above 2.0 when alloyed with group II elements. The impr…

cond-mat.mtrl-sci2017

Renormalized Lattice Dynamics and Thermal Transport in VO

Yi Xia, Maria K. Y. Chan

Vanadium dioxide (VO) undergoes a first-order metal-insulator transition (MIT) upon cooling near room temperature, concomitant with structural change from rutile to monoclini…

cond-mat.mes-hall2017★ 125 cited

Theory of Thermal Relaxation of Electrons in Semiconductors

Sridhar Sadasivam, Maria K. Y. Chan, Pierre Darancet

We compute the transient dynamics of phonons in contact with high energy "hot" charge carriers in 12 polar and non-polar semiconductors, using a first-principles Boltzmann transpor…

cond-mat.mtrl-sci2016★ 30 cited

Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces

Kiran Sasikumar, Badri Narayanan, Mathew Cherukara +5

Tantalum, tantalum oxide and their hetero-interfaces are of tremendous technological interest in several applications spanning electronics, thermal management, catalysis and bioche…

physics.atm-clus2016

Methodology of Parameterization of Molecular Mechanics Force Field From Quantum Chemistry Calculations using Genetic Algorithm: A case study of methanol

Ying Li, Hui Li, Maria K. Y. Chan +2

In molecular dynamics (MD) simulation, force field determines the capability of an individual model in capturing physical and chemistry properties. The method for generating proper…

cond-mat.mtrl-sci2016★ 30 cited

Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search

Alper Kinaci, Badri Narayanan, Fatih G. Sen +4

Au nanoclusters are of technological relevance for catalysis, photonics, sensors, and of fundamental scientific interest owing to planar to globular structural transformation at an…

cond-mat.mtrl-sci2015★ 33 cited

Describing the diverse geometries of gold from nanoclusters to bulk-- a first-principles based hybrid bond order potential

Badri Narayanan, Alper Kinaci, Fatih G. Sen +4

Molecular dynamics simulations using empirical force fields (EFFs) are crucial for gaining fundamental insights into atomic structure and long timescale dynamics of Au nanoclusters…