This analysis uncovers the relationship between milling conditions and nanoparticle size using machine learning, indicating improved modeling techniques.
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.
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Ono et al. (2026) studied this question.
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