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Search Publications by: Kamal Choudhary (Assoc)

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Displaying 1 - 25 of 70

BenchQC: A Benchmarking Toolkit for Quantum Computation

August 4, 2025
Author(s)
Nia Rodney-Pollard, Kamal Choudhary
The Variational Quantum Eigensolver (VQE) is a widely studied hybrid classical-quantum algorithm for approximating ground-state energies in molecular and materials systems. This study benchmarks the performance of the VQE for calculating ground-state

The JARVIS Infrastructure is All You Need for Materials Design

July 23, 2025
Author(s)
Kamal Choudhary
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is a unified platform for multiscale, multimodal, forward, and inverse materials design. It integrates diverse theoretical and experimental approaches, including density functional

Examining Generalizability of AI Models for Catalysis

June 7, 2025
Author(s)
Kamal Choudhary, Shih Han Wang, Hongliang Xin, Luke Achenie
In this work, we investigate the generalizability of problem-specific machine-learning models for catalysis across different datasets and adsorbates, and examine the potential of unified models as pre-screening tools for density functional theory

Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe2 with charge density wave states

March 7, 2025
Author(s)
Daniel Wines, Akram Ibrahim, Nishwanth Gudibandla, Tehseen Adel, Frank Abel, Sharadh Jois, Kayahan Saritas, Jaron Krogel, Li Yin, Tom Berlijn, Aubrey Hanbicki, Gregory Stephen, Adam Friedman, Sergiy Krylyuk, Albert Davydov, Brian Donovan, Michelle Jamer, Angela Hight Walker, Kamal Choudhary, Francesca Tavazza, Can Ataca
Two-dimensional (2D) 1T-VSe2 has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion Monte

Hybrid-LLM-GNN: Integrating Large Language Models and Graph Neural Networks for Enhanced Materials Property Prediction

December 18, 2024
Author(s)
Youjia Li, Vishu Gupta, Talha Killic, Kamal Choudhary, Daniel Wines, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Graph-centric learning has attracted significant interest in material informatics. Accordingly, a family of graph-based machine learning models, primarily utilizing Graph Neural Networks (GNN), has been developed to provide accurate material properties

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

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

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

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
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