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Accurate machine learning force fields via experimental and simulation data fusion

Engineering and Technology

Accurate machine learning force fields via experimental and simulation data fusion

S. Röcken and J. Zavadlav

Explore groundbreaking research by Sebastien Röcken and Julija Zavadlav on leveraging Machine Learning to fuse Density Functional Theory and experimental data for enhanced accuracy in titanium force fields. This innovative approach promises to correct DFT inaccuracies while preserving essential material properties.

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Playback language: English
Abstract
Machine Learning (ML)-based force fields offer the potential to achieve quantum-level accuracy across spatiotemporal scales. This paper presents a method that fuses Density Functional Theory (DFT) calculations and experimental data to train an ML potential for titanium. By combining both data sources, the model simultaneously satisfies various target objectives, leading to higher accuracy compared to single-source models. This approach corrects DFT inaccuracies while minimally affecting other properties. The methodology is widely applicable for generating highly accurate ML potentials for various materials.
Publisher
npj Computational Materials
Published On
Apr 05, 2024
Authors
Sebastien Röcken, Julija Zavadlav
Tags
Machine Learning
Density Functional Theory
titanium
ML potentials
quantum accuracy
force fields
data fusion

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