A data-driven approach for the modeling of a ball-milled dispersion of BaTiO 3 nanoparticles
Abstract
Wet-state ball milling of ceramic nanoparticles is analyzed by machine learning and machine-learning-assisted model formulation. A linear model formula is constructed from the high-impact input features revealed in the machine learning. The formula explains the relation between the ball-milling conditions and hydrodynamic size with less precision but better analytical processability compared to the original machine learning.
Fields of Study & Subjects
Machine Learning in Materials Science
Mineral Processing and Grinding
Ferroelectric and Piezoelectric Materials
Publication details
- Journal / source
- Materials Horizons
- Volume
- 13
- Issue
- 9
- Pages
- 4519–4525
- Publication date
- 1 January 2026
- Language
- EN