Kamal Choudhary
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Explore the profile of Kamal Choudhary including associated specialties, affiliations and a list of published articles.
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Citations
337
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Recent Articles
1.
Wines D, Ibrahim A, Gudibandla N, Adel T, Abel F, Jois S, et al.
ACS Nano
. 2025 Mar;
PMID: 40053912
Two-dimensional (2D) 1T-VSe 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...
2.
Choudhary K
J Phys Chem Lett
. 2025 Feb;
16(8):2110-2119.
PMID: 39976483
Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This study introduces DiffractGPT, a generative pretrained transformer model...
3.
Choudhary K, Garrity K, Sharma V, Biacchi A, Walker A, Tavazza F
NPJ Comput Mater
. 2024 Nov;
6(1).
PMID: 39563780
Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric and dielectric properties of...
4.
Huang Y, Wang S, Achenie L, Choudhary K, Xin H
J Chem Phys
. 2024 Oct;
161(16).
PMID: 39435835
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...
5.
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Evans M, Bergsma J, Merkys A, Andersen C, Andersson O, Beltran D, et al.
Digit Discov
. 2024 Aug;
3(8):1509-1533.
PMID: 39118978
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...
6.
Choudhary K
J Phys Chem Lett
. 2024 Jun;
15(27):6909-6917.
PMID: 38935647
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 Letter, AtomGPT is...
7.
Wines D, Choudhary K
Mater Futur
. 2024 Jun;
3(2).
PMID: 38841205
The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, HS and LaH) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors....
8.
Li K, Persaud D, Choudhary K, DeCost B, Greenwood M, Hattrick-Simpers J
Nat Commun
. 2024 Jan;
15(1):284.
PMID: 38177139
No abstract available.
9.
Jablonka K, Ai Q, Al-Feghali A, Badhwar S, Bocarsly J, M Bran A, et al.
Digit Discov
. 2023 Nov;
2(5):1233-1250.
PMID: 38013906
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities,...
10.
Li K, Persaud D, Choudhary K, DeCost B, Greenwood M, Hattrick-Simpers J
Nat Commun
. 2023 Nov;
14(1):7283.
PMID: 37949845
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...