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Search Publications by: Kamal Choudhary (Fed)

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Displaying 1 - 25 of 45

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

October 18, 2023
Daniel Wines, Ramya Gurunathan, Kevin Garrity, Brian DeCost, Adam Biacchi, Francesca Tavazza, Kamal Choudhary
The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at NIST is a large-scale collection of curated datasets and tools with more than 80000 materials and millions of properties. JARVIS uses a combination of electronic

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

August 8, 2023
Kevin Maik Jablonka, Alexander Al-Feghali, Shruti Badhwar, Joshua Bocarsly, Stefan Bringuier, Kamal Choudhary, Defne Circi, Samantha Cox, Matthew Evans, Nicolas Gastellu, Jerome Genzling, Maria Victoria Gil, Ankur Gupta, Wibe de Jong, Tao Liu, Sauradeep Majumdar, Garrett Merz, Nicolas Moitessier, Lynda Brinson, Beatriz Mourino, Brenden Pelkie, Mayk Caldas Ramos, Bojana Rankovic, Jacob Sanders, Ben Blaiszik, Andrew White, Ian Foster, Ghezal Ahmad Jan Zia
Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an

AI for Materials

April 25, 2023
Debra Audus, Kamal Choudhary, Brian DeCost, A. Gilad Kusne, Francesca Tavazza, James A. Warren
The application of artificial intelligence (AI) methods to materials re- search and development (MR&D) is poised to radically reshape how materials are discovered, designed, and deployed into manufactured products. Materials underpin modern life, and

Forward and Inverse design of high $T_C$ superconductors with DFT and deep learning

April 21, 2023
Daniel Wines, Kevin Garrity, Tian Xie, Kamal Choudhary
We developed a multi-step workflow for the discovery of next-generation conventional superconductors. 1) We started with a Bardeen–Cooper–Schrieffer (BCS) inspired pre-screening of 55000 materials in the JARVIS-DFT database resulting in 1736 materials with

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction

March 27, 2023
Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Vishu Gupta, Yuwei Mao, Kewei Wang, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available

AtomVision: A machine vision library for atomistic images

March 1, 2023
Brian DeCost, Ramya Gurunathan, Adam Biacchi, Kamal Choudhary
Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate and curate microscopy image (such as scanning tunneling

Unified graph neural network force-field for the periodic table: solid state applications

February 23, 2023
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, Francesca Tavazza
Classical force fields (FFs) based on machine learning (ML) methods show great potential for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

September 22, 2022
Austin McDannald, Howie Joress, Brian DeCost, Avery Baumann, A. Gilad Kusne, Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, Winnie Wong-Ng, Andrew J. Allen, Christopher Stafford, Diana Ortiz-Montalvo
We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide ( CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously

Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties

July 1, 2022
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
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
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
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
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
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