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Search Publications by: Daniel Wines (Fed)

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Displaying 1 - 16 of 16

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)

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

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

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

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
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