Chemistrynpj Computational Materials
Predicting glass structure by physics-informed machine learning
M. L. Bødker, M. Bauchy, et al.
This groundbreaking research by Mikkel L. Bødker, Mathieu Bauchy, Tao Du, John C. Mauro, and Morten M. Smedskjaer unveils a novel physics-informed machine learning model that predicts the complex relationships between composition and structure in oxide glasses with remarkable accuracy.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Physics
Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning
Z. Li, T. Flynn, et al.
Engineering and Technology
SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning
Y. Song, M. Zhang, et al.
Engineering and Technology
Uncovering drone intentions using control physics informed machine learning
A. Perrusquía, W. Guo, et al.
Materials Science
Structural changes during glass formation extracted by computational homology with machine learning
A. Hirata, T. Wada, et al.
