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A data-driven approach for the modeling of a ball-milled dispersion of BaTiO3 nanoparticles
Takumi Ono1, Tarojiro Matsumura2, Kiwamu Sue1
1Research Institute for Chemical Process Technology, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 5, 1-1-1 Higashi, 3058565 Tsukuba, Japan. s.takeshita@aist.go.jp.
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.
