A data-driven approach for the modeling of a ball-milled dispersion of BaTiO3 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.