most citedCHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

1 citations · 1 across the 2 of their papers we have counts for

collaborators
Showing cond-mat.mtrl-sciShow all

2 papers · 1 filter

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★ 1 cited

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…