Publications

Displaying 1 - 25 of 59

Prediction of Magnetic Properties in van der Waals Magnets using Graph Neural Networks

November 4, 2024

Author(s)

Kamal Choudhary, Peter Minch, Trevor David Rhone, Romakanta Bhattarai

We study two-dimensional (2D) magnetic materials using state-of-the-art machine learning models that use a graph-theory framework. We find that representing materials as graphs allows us to better learn structure-property relationships by leveraging both

Origin of unique electronic structures of single-atom alloys unravelled by interpretable deep learning

October 21, 2024

Author(s)

Yang Huang, Shih-Han Wang, Luke Achenie, Kamal Choudhary, Hongliang Xin

We uncover the origin of unique electronic structures of single-atom alloys (SAAs) by interpretable deep learning. The approach integrates tight-binding moment theory with graph neural networks to accurately describe the local electronic structure of

Setting standards for data driven materials science

October 1, 2024

Author(s)

Keith Butler, Kamal Choudhary, Gabor Csanyi, Alex Ganose, Sergei Kalinin, Dane Morgan

A young Steve Jobs once called computers 'bicycles for the mind' – he was referring to the dramatic decrease in the energetic cost of transportation that could be obtained with the bicycle, which breaks all scaling laws for how efficiently an animal can

AtomGPT: Atomistic Generative Pre-trained Transformer for Forward and Inverse Materials Design

June 27, 2024

Author(s)

Kamal Choudhary

Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials design remains underexplored. In this article, we introduce AtomGPT, a model

InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning

May 23, 2024

Author(s)

Kamal Choudhary, Kevin Garrity

We introduce a computational framework (InterMat) to predict band offsets of semiconductor interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first step, we benchmark OptB88vdW generalized gradient approximation (GGA)

Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning

May 13, 2024

Author(s)

Daniel Wines, Kamal Choudhary

The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H3S and LaH10) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

May 7, 2024

Author(s)

Kamal Choudhary, Daniel Wines, Kevin Garrity, aldo romero, Jaron Krogel, Kayahan Saritas, Panchapakesan Ganesh, Paul Kent, Pascal Friederich, Vishu Gupta, Ankit Agrawal, Pratyush Tiwary, ichiro takeuchi, Robert Wexler, Arun Kumar Mannodi-Kanakkithodi, Avanish Mishra, Kangming Li, Adam Biacchi, Francesca Tavazza, Ben Blaiszik, Jason Hattrick-Simpers, Maureen E. Williams

Reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have

A reproducibility study of graph neural networks for materials property prediction

April 30, 2024

Author(s)

Kangming Li, Brian DeCost, Kamal Choudhary, Jason Hattrick-Simpers

Use of machine learning has been increasingly popular in materials science as data-driven materials discovery is becoming the new paradigm. Reproducibility of findings is paramount for promoting transparency and accountability in research and building

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

April 18, 2024

Author(s)

Kamal Choudhary, Matthew Evans, Gian-Marco Rignanese

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data

Efficient first principles based modeling via machine learning: from simple representations to high entropy materials

April 17, 2024

Author(s)

Kangming Li, Kamal Choudhary, Brian DeCost, Michael Greenwood, Jason Hattrick-Simpers

High-entropy materials (HEMs) have recently emerged as a significant category of materials, of- fering highly tunable properties. However, the scarcity of HEM data in existing density functional theory (DFT) databases, primarily due to computational

Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks

March 27, 2024

Author(s)

Md. Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, Ghanshyam Pilania, Arun Kumar Mannodi-Kanakkithodi, Brian DeCost, Kamal Choudhary

First principles computations reliably predict the energetics of point defects in semiconductors, but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially

Structure-Aware GNN-Based Deep Transfer Learning Framework For Enhanced Predictive Analytics On Small Materials Data

January 2, 2024

Author(s)

Vishu Gupta, Kamal Choudhary, Brian DeCost, Francesca Tavazza, Carelyn E. Campbell, Wei-keng Liao, Alok Choudhary, Ankit Agrawal

Modern data mining methods have been demonstrated to be effective tools to comprehend and predict materials properties. An essential component in the process of materials discovery is to know which material(s) (represented by their composition and crystal

Exploiting redundancy in large materials datasets for efficient machine learning with less data

November 10, 2023

Author(s)

Kamal Choudhary, Brian DeCost, Kangming Li, Daniel "Persaud ", Jason Hattrick-Simpers, Michael Greenwood

Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to

Searching Thermodynamically Stable Materials with Ultralow Lattice Thermal Conductivity by Dual Machine Learning Models

October 31, 2023

Author(s)

Joshua Ojih, Chen Shen, Uche Onyekpe, Ming Hu, Kamal Choudhary, Hongbin Zhang

Materials with ultralow lattice thermal conductivity (LTC) continue to be of great interest for technologically important applications such as thermal insulators and thermoelectrics. We report an efficient workflow combining high-throughput density

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

October 18, 2023

Author(s)

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

Author(s)

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

Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models

July 18, 2023

Author(s)

Daniel Wines, Tian Xie, Kamal Choudhary

Finding new superconductors with a high critical temperature (Tc) has been a challenging task due to computational and experimental costs. We present a diffusion model inspired by the computer vision community to generate new superconductors with unique

AI for Materials

April 25, 2023

Author(s)

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

Author(s)

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

Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table

April 14, 2023

Author(s)

Kevin Garrity, Kamal Choudhary

Parametrized tight-binding models fit to first principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and solids. However, well-tested parameter sets are generally only available for a

A critical examination of robustness and generalizability of machine learning prediction of materials properties

April 7, 2023

Author(s)

Kangming LI, Brian DeCost, Kamal Choudhary, Michael Greenwood, Jason Hattrick-Simpers

Recent advances in machine learning (ML) have led to substantial performance improvement in material database benchmarks, but an excellent benchmark score may not imply good generalization performance. Here we show that ML models trained on Materials

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

March 27, 2023

Author(s)

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

Author(s)

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

Rapid Prediction of Phonon Structure and Properties using the Atomistic Line Graph Neural Network (ALIGNN)

February 24, 2023

Author(s)

Ramya Gurunathan, Kamal Choudhary, Francesca Tavazza

The phonon density-of-states (DOS) summarizes the lattice vibrational modes supported by a structure, and gives access to rich information about the material's stability, thermodynamic constants, and thermal transport coefficients. Here, we present an

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

February 23, 2023

Author(s)

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

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