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Search Publications by: Kamal Choudhary (Fed)

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Displaying 26 - 47 of 47

Predicting anomalous quantum confinement effect in van der Waals materials

April 21, 2021
Author(s)
Francesca Tavazza, Kamal Choudhary
Materials with van der Waals bonding are known to exhibit a quantum confinement effect, in which the electronic band gap of the three-dimensional realization of a material is lower than that of its two-dimensional (2D) counterpart. However, the possibility

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

November 12, 2020
Author(s)
Kamal Choudhary, Kevin Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques

High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses

May 27, 2020
Author(s)
Kamal Choudhary, Kevin F. Garrity, Vinit Sharma, Adam J. Biacchi, Francesca M. Tavazza, Angela R. Hight Walker
In this work, combining high-throughput (HT) density functional perturbation theory and supervised machine learning approaches, we explored the territory of compounds with interesting infrared, piezoelectric and dielectric properties. We have computed Γ

Enhancing Materials Property Prediction by Leveraging Computational and Experimental Data using Deep Transfer Learning

November 22, 2019
Author(s)
Kamal Choudhary, Dipendra Jha, Ankit Agrawal, Alok Choudhary, Wei-keng Liao, Francesca M. Tavazza, Carelyn E. Campbell
The availability of huge collections of data from DFT-computations has spurred the interest of materials scientists in applying machine learning techniques to build models for fast prediction of materials properties. Such modeling practice has helped to

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

July 22, 2019
Author(s)
Kamal Choudhary, Aaron G. Kusne, Francesca M. Tavazza, Jason R. Hattrick-Simpers, Rama K. Vasudevan, Apurva Mehta, Ryan Smith, Lukas Vlcek, Sergei V. Kalinin, Maxim Ziatdinov
The use of advanced data analytics, statistical and machine learning approaches (‘AI’) to materials science has experienced a renaissance, driven by advances in computer sciences, availability and access of software and hardware, and a growing realization

An Inter-Laboratory Comparative High Throughput Experimental Materials Study of Zn-Sn-Ti-O Thin Films

March 19, 2019
Author(s)
Jason R. Hattrick-Simpers, Zachary T. Trautt, Kamal Choudhary, Aaron G. Kusne, Feng Yi, Martin L. Green, Sara Barron, Andriy Zakutayev, Nam Nguyen, Caleb Phillips, John Perkins, Ichiro Takeuchi, Apurva Mehta
High throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many possible applications. However, there are very few publications on the reproducibility of the HTE results

Thermodynamic analysis of the topologically close packed ? phase in the Co-Cr system

November 22, 2018
Author(s)
Peisheng Wang, Matthew C. Peters, Ursula R. Kattner, Kamal Choudhary, Gregory B. Olson
Density functional theory (DFT) calculations show that a thermodynamic description considering the magnetic contribution to the total energy for the end-members of the σ phase is necessary. A more straightforward method to use the DFT results in a CALPHAD

High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

September 7, 2018
Author(s)
Kamal Choudhary, Adam J. Biacchi, Supriyo Ghosh, Lucas M. Hale, Angela R. Hight Walker, Francesca M. Tavazza
In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are

Data-driven Discovery of 3D and 2D Thermoelectric Materials

June 26, 2018
Author(s)
Kamal Choudhary, Kevin Garrity, Francesca Tavazza
Abstract: In this work, we perform a systematic search for high-efficiency, three-dimensional (3D) and two-dimensional (2D) thermoelectric materials by combining semiclassical transport techniques with density functional theory (DFT) calculations. Out of

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

May 8, 2018
Author(s)
Kamal Choudhary, Qin Zhang, Sugata Chowdhury, Nhan V. Nguyen, Zachary T. Trautt, Marcus W. Newrock, Faical Y. Congo, Andrew C. Reid, Francesca M. Tavazza
We perform high-throughput density functional theory (DFT) calculations for optoelectronic properties (electronic bandgap and frequency dependent dielectric function) using the OptB88vdW functional (OPT) and the Tran-Blaha modified Becke Johnson potential

Identification of the Crystal Symmetry in 1T' MoTe2 with Polarization-Resolved Second Harmonic Generation and Raman Scattering

October 13, 2016
Author(s)
Ryan Beams, Luiz Gustavo Cancado, Sergiy Krylyuk, Irina Kalish, Berc Kalanyan, Arunima Singh, Kamal Choudhary, Patrick Vora, Francesca M. Tavazza, Albert Davydov, Stephan J. Stranick
We study the crystal symmetry properties of few-layer 1T' MoTe2 using the polarization dependence of the second harmonic generation (SHG) and Raman scattering. Bulk 1T' MoTe2 is know to be inversion symmetric, however, we nd that the inversion symmetry is