2 papers
cond-mat.mtrl-sci2025
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…
cond-mat.mtrl-sci2024
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…