68 citations · 379 across the 17 of their papers we have counts for
6 papers · 1 filter
Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
Daniel Wines, Kamal Choudhary
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 approac…
Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
Akram Ibrahim, Daniel Wines, Can Ataca
The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference of these mat…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
Daniel Wines, Ramya Gurunathan, Kevin F. Garrity +4
The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at the National Institute of Standards and Technology (NIST) is a large-scale collection o…
Inverse Design of Next-generation Superconductors Using Data-driven Deep Generative Models
Daniel Wines, Tian Xie, Kamal Choudhary
Finding new superconductors with a high critical temperature () has been a challenging task due to computational and experimental costs. We present a diffusion model inspired…
A Quantum Monte Carlo study of the structural, energetic, and magnetic properties of two-dimensional (2D) H and T phase VSe
Daniel Wines, Juha Tiihonen, Kayahan Saritas +2
Previous works have controversially claimed near-room temperature ferromagnetism in two-dimensional (2D) VSe, with conflicting results throughout the literature. These discrepa…