4 papers
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…
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…