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Search Publications by: Kamal Choudhary (Fed)

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Displaying 51 - 75 of 107

Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties

July 1, 2022
Author(s)
Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, A. Gilad Kusne, Austin McDannald, Diana Ortiz-Montalvo
The increasing CO$_2$ level is a critical concern and suitable materials are needed to directly capture such gases from the environment. While experimental and conventional computational methods are useful in finding such materials, they are usually slow

Recent Advances and Applications of Deep Learning Methods in Materials Science

February 24, 2022
Author(s)
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon Billinge, Elizabeth Holm, ShyuePing Ong, Chris Wolverton
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. Deep learning allows analysis of unstructured data and automated

Computational scanning tunneling microscope image database

December 5, 2021
Author(s)
Kamal Choudhary, Kevin Garrity, Charles Camp, Sergei Kalinin, Rama Vasudevan, Maxim Ziatdinov, Francesca Tavazza
We introduce the systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains data for 716 exfoliable

Uncertainty Prediction for Machine Learning Models of Material Properties

November 23, 2021
Author(s)
Francesca Tavazza, Brian DeCost, Kamal Choudhary
Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

November 15, 2021
Author(s)
Vishu Gupta, Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Wei-Keng Liao, Alok Choudhary, Ankit Agrawal
Artificial Intelligence (AI) and Machine Learning (ML) has been increasingly used in materials science to build property prediction models and accelerate materials discovery. The availability of large materials databases for some properties like formation

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