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Kamal Choudhary

national institute of standards and technology

H指数:37

被引数:6,531

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M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution

M-CODE：基于本体、维度和演化的材料分类

Computational Materials Science

IF3.3

2026-07-010

PRE

Evaluating large language models for inverse semiconductor design

评估用于逆向半导体设计的大型语言模型

Digital Discovery

IF5.6

2026-01-300

OAAI

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

JOURNAL OF PHYSICAL CHEMISTRY C

IF3.2

2025-12-010

PRE

Interpretable ensemble learning for materials property prediction with classical interatomic potentials

基于经典原子间势的用于材料属性预测的可解释集成学习

npj Computational Materials

IF11.9

2025-10-240

OA

SlaKoNet: A Unified Slater-Koster Tight-Binding Framework Using Neural Network Infrastructure for the Periodic Table

SlaKoNet：一种基于神经网络基础设施的、适用于周期表的统一Slater-Koster紧束缚框架

The Journal of Physical Chemistry Letters

IF0

2025-10-170

PRE

Intrinsic direct air capture

固有直接空气捕集

IF7.4

2025-09-110

OAAI

BenchQC: A Benchmarking Toolkit for Quantum Computation

BenchQC：量子计算基准测试工具包

Journal of Computational Chemistry

IF4.8

2025-08-040

OA

The JARVIS infrastructure is all you need for materials design

JARVIS基础设施是材料设计所需的一切

Computational Materials Science

IF3.3

2025-07-230

PRE

Examining generalizability of AI models for catalysis

考察用于催化领域的AI模型泛化能力

Journal of Catalysis

IF6.5

2025-06-070

PRE

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

CHIPS-FF：评估通用机器学习力场在材料性质方面的应用

ACS MATERIALS LETTERS

IF8.7

2025-05-050

OA

Quantum Monte Carlo and Density Functional Theory Study of Strain and Magnetism in 2D 1T-VSe2 with Charge Density Wave States

量子蒙特卡罗和密度泛函理论研究具有电荷密度波态的二维1T-VSe₂中的应变与磁性

IF16

2025-03-070

PRE

DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns Using a Generative Pretrained Transformer

DiffractGPT：利用生成式预训练变换器从X射线衍射图谱确定原子结构

JOURNAL OF PHYSICAL CHEMISTRY LETTERS

IF4.6

2025-02-200

PRE

Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction

DIGITAL DISCOVERY

IF5.6

2025-01-010

OA

Predicting magnetic properties of van der Waals magnets using graph neural networks

PHYSICAL REVIEW MATERIALS

IF3.4

2024-11-040

PRE

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

JOURNAL OF CHEMICAL PHYSICS

IF3.1

2024-10-222

PRE

Setting standards for data driven materials science

为数据驱动的材料科学制定标准

NPJ COMPUTATIONAL MATERIALS

IF11.9

2024-10-010

OA

Data-driven design of high pressure hydride superconductors using DFT and deep learning

MATERIALS FUTURES

IF10.8

2024-05-313

OA

JARVIS-Leaderboard: a large scale benchmark of materials design methods

NPJ COMPUTATIONAL MATERIALS

IF11.9

2024-05-076

OAAI

Choudhary, Kamal; Wines, Daniel; Li, Kangming; Garrity, Kevin F.; Gupta, Vishu; Romero, Aldo H.; Krogel, Jaron T.; Saritas, Kayahan; Fuhr, Addis; Ganesh, Panchapakesan; Kent, Paul R. C.; Yan, Keqiang; Lin, Yuchao; Ji, Shuiwang; Blaiszik, Ben; Reiser, Patrick; Friederich, Pascal; Agrawal, Ankit; Tiwary, Pratyush; Beyerle, Eric; Minch, Peter; Rhone, Trevor David; Takeuchi, Ichiro; Wexler, Robert B.; Mannodi-Kanakkithodi, Arun; Ertekin, Elif; Mishra, Avanish; Mathew, Nithin; Wood, Mitchell; Rohskopf, Andrew Dale; Hattrick-Simpers, Jason; Wang, Shih-Han; Achenie, Luke E. K.; Xin, Hongliang; Williams, Maureen; Biacchi, Adam J.; Tavazza, Francesca

Exploiting redundancy in large materials datasets for efficient machine learning with less data (vol 14, 7283, 2023)

NATURE COMMUNICATIONS

IF15.7

2024-01-040

OAAI

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

NPJ COMPUTATIONAL MATERIALS

IF11.9

2024-01-0211

OA