Publications

Displaying 1 - 9 of 9

Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning

May 13, 2024

Author(s)

Daniel Wines, Kamal Choudhary

The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H3S and LaH10) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we

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

Neural network potentials for modeling nonstoichiometric materials: a case of Chromium Sulfides Cr1-xS

January 10, 2024

Author(s)

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 materials to engage in three-dimensional chemical bonding, resulting in

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

Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models

July 18, 2023

Author(s)

Daniel Wines, Tian Xie, Kamal Choudhary

Finding new superconductors with a high critical temperature (Tc) has been a challenging task due to computational and experimental costs. We present a diffusion model inspired by the computer vision community to generate new superconductors with unique

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

A Quantum Monte Carlo Study of the Structural, Energetic, and Magnetic Properties of Two-Dimensional H and T phase VSe2

April 5, 2023

Author(s)

Daniel Wines, Juha Tiihonen, Kayahan Saritas, Jaron Krogel, Can Ataca

One of the most promising 2D ferromagnetic materials is monolayer VSe2 (T and H phase). Several works have controversially claimed near-room temperature ferromagnetism in VSe2, with conflicting results throughout the theoretical and experimental literature

High-Throughput DFT-Based Discovery of Next Generation Two Dimensional (2D) Superconductors

January 30, 2023

Author(s)

Daniel Wines, Kamal Choudhary, Adam Biacchi, Kevin Garrity, Francesca Tavazza

High-throughput density functional theory (DFT) calculations allow for a systematic search for conventional Bardeen–Cooper–Schrieffer (BCS) superconductors. With the recent interest in two-dimensional (2D) superconductors, we extend our previously

Systematic DFT+U and Quantum Monte Carlo Benchmark of Magnetic Two-Dimensional (2D) CrX3 (X = I, Br, Cl, F)

January 9, 2023

Author(s)

Daniel Wines, Kamal Choudhary, Francesca Tavazza

The search for two-dimensional (2D) magnetic materials has attracted a great deal of attention because of the experimental synthesis of 2D CrI3, which has a measured Curie temperature of 45 K. Often times, these monolayers have a higher degree of electron

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