68 citations · 379 across the 17 of their papers we have counts for
17 papers
Strain-Induced Interfacial Bond Evolution and Facet-dependent Strain Response of Electronic Properties in Oxidized Silicon Nanowires
Yasir Mahmood, Daniel Wines, Viviana Dovale-Farelo +2
Silicon nanowires (Si NWs) have emerged as promising candidates for next-generation nanoelectronic devices due to their tunable electronic properties, and compatibility with aggres…
Machine Learning for Predicting Magnetization from X-ray Diffraction of Iron Oxide Nanoparticles Using Simple Physics-Based Data Generation
Frank M. Abel, Paige Burke, Daniel Wines +3
Automation and high-throughput characterization and synthesis for material development are becoming increasingly common; these approaches require machine learning (ML) tools to ass…
Probing Magnetic Properties of RuO Heterostructures Through the Ferromagnetic Layer
Frank M. Abel, Subhash Bhatt, Shelby S. Fields +20
RuO has been proposed as the prototypical altermagnetic material. However, several reports have recently questioned its intrinsic magnetic ordering, leading to conflicting fi…
CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
Daniel Wines, Kamal Choudhary
In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for…
Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe with charge density wave states
Daniel Wines, Akram Ibrahim, Nishwanth Gudibandla +18
Two-dimensional (2D) 1T-VSe has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) stat…
Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods
Daniel Wines, Jeonghwan Ahn, Anouar Benali +10
The field of two-dimensional (2D) materials has grown dramatically in the last two decades. 2D materials can be utilized for a variety of next-generation optoelectronic, spintronic…
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…
High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors
Daniel Wines, Kamal Choudhary, Adam J. Biacchi +2
High-throughput density functional theory (DFT) calculations allow for a systematic search for conventional superconductors. With the recent interest in two-dimensional (2D) superc…
Systematic DFT+U and Quantum Monte Carlo benchmark of magnetic two-dimensional (2D) CrX (X = I, Br, Cl, F)
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 CrI, which has a measured Curie tempe…
The intrinsic ferromagnetism of two-dimensional (2D) MnO revisited: A many-body Quantum Monte Carlo and DFT+U study
Daniel Wines, Kayahan Saritas, Can Ataca
Monolayer MnO is one of the few predicted two-dimensional (2D) ferromagnets that has been experimentally synthesized and is commercially available. The Mermin-Wagner theorem st…
A pathway towards high throughput Quantum Monte Carlo simulations for alloys: A case study of two-dimensional (2D)
Daniel Wines, Kayahan Saritas, Can Ataca
The study of alloys using computational methods has been a difficult task due to the usually unknown stoichiometry and local atomic ordering of the different structures experimenta…
A first-principles Quantum Monte Carlo study of two-dimensional (2D) GaSe
Daniel Wines, Kayahan Saritas, Can Ataca
Two-dimensional (2D) post-transition metal chalcogenides (PTMC) have attracted attention due to their suitable band gaps and lower exciton binding energies, making them more approp…