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Search Publications by: Kamal Choudhary (Assoc)

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Displaying 1 - 25 of 75

Dynamic Embedding Representation for Graph Neural Networks to Enhance Materials Property Prediction with Limited Datasets

March 24, 2026
Author(s)
Vishu Gupta, Kamal Choudhary, Youjia Li, Muhammed Nur Talha Kili, Daniel Wines, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Graph neural networks (GNNs) have proven effective in understanding and predicting diverse material properties, even when working with limited datasets. An important step in training GNN is to use an appropriate and informative graph embedding that can

Evaluating Large Language Models for Inverse Semiconductor Design

January 16, 2026
Author(s)
MUHAMMED NUR TALHA KILIC, Daniel Wines, Kamal Choudhary, Vishu Gupta, Youjia Li, Sayak Chakrabarty, WEI-KENG LIAO, Alok Choudhary, Ankit Agrawal
Large Language Models (LLMs) with generative capabilities have garnered significant attention in various domains, including materials science. However, systematically evaluating their performance for structure generation tasks remains a major challenge. In

Predicting Lattice Parameters from Atomic-Scale Images of Two Dimensional Materials Using Deep Learning

December 15, 2025
Author(s)
Sayak Chakrabarty, Kamal Choudhary, Youjia Li, Daniel Wines, Vishu Gupta, Muhammed Nur Talha Kilic, Alok Choudhary, Ankit Agrawal
Determining lattice parameters in two-dimensional (2D) materials is essential for materials characterization and discovery. In this work, we propose a deep-learning-driven pipeline that addresses the regression task of estimating the lattice constants (a)

Intrinsic Direct Air Capture

September 10, 2025
Author(s)
Austin McDannald, Daniel Siderius, Brian DeCost, Kamal Choudhary, Diana Ortiz-Montalvo
How can you tell if a sorbent material will be good for any gas separation process – without having to do detailed simulations of the full process? We present new metrics to evaluate solid sorbent materials for Direct Air Capture (DAC), a particularly

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
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