Daniel Wines

National Institute of Standards and Technology — United States

Materials Science · Physical Sciences

14h-index525citations51works6.22yr avg
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Research Topics

Machine Learning in Materials Science(25), 2D Materials and Applications(15), Heusler alloys: electronic and magnetic properties(7), Chalcogenide Semiconductor Thin Films(5), Inorganic Chemistry and Materials(5)

Publications51 total

Evaluating large language models for inverse semiconductor design
Digital Discovery·2026Open Access
Predicting Lattice Parameters from Atomic-Scale Images of Two Dimensional Materials Using Deep Learning
The Journal of Physical Chemistry C·2025
Machine Learning for Predicting Magnetization from X-ray Diffraction of Iron Oxide Nanoparticles Using Simple Physics-Based Data Generation
arXiv (Cornell University)·2025Open Access
Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods
Applied Physics Reviews·2025· 5 citedOpen Access
Probing Magnetic Properties of RuO$_{2}$ Heterostructures Through the Ferromagnetic Layer
arXiv (Cornell University)·2025Open Access
CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
ACS Materials Letters·2025· 15 citedOpen Access
Quantum Monte Carlo and Density Functional Theory Study of Strain and Magnetism in 2D 1T-VSe<sub>2</sub> with Charge Density Wave States
ACS Nano·2025· 2 cited
Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction
Digital Discovery·2024· 11 citedOpen Access
CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
arXiv (Cornell University)·2024· 1 citedOpen Access
Author response for "Hybrid-LLM-GNN: Integrating Large Language Models and Graph Neural Networks for Enhanced Materials Property Prediction"
2024· 1 cited
Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe$_2$ with charge density wave states
arXiv (Cornell University)·2024· 2 citedOpen Access
Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods
arXiv (Cornell University)·2024· 1 citedOpen Access
Data-driven design of high pressure hydride superconductors using DFT and deep learning
Materials Futures·2024· 12 citedOpen Access
JARVIS-Leaderboard: a large scale benchmark of materials design methods
npj Computational Materials·2024· 38 citedOpen Access
Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
The Journal of Physical Chemistry C·2024· 5 cited
Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
arXiv (Cornell University)·2023Open Access
Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
Applied Physics Reviews·2023· 32 citedOpen Access
Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
arXiv (Cornell University)·2023Open Access
Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models
The Journal of Physical Chemistry Letters·2023· 43 cited
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
arXiv (Cornell University)·2023· 4 citedOpen Access
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Frequent Co-authors

Can Ataca(28), Kamal Choudhary(22), Kayahan Saritas(13), Fatih Ersan(11), Francesca Tavazza(10), Jaron T. Krogel(8), Adam J. Biacchi(6), Kevin F. Garrity(6), Vishu Gupta(5), Ankit Agrawal(5), Rabin Mahat(5), Shambhu KC(5), Upama Karki(5), P. LeClair(5), Akram Ibrahim(5), Paul R. C. Kent(4), Sudhir Regmi(4), Muhammed Nur Talha Kilic(4), Frank M. Abel(4), Jaron A. Kropp(3)