Sequential Machine Learning Applications of Particle Packing with Large Size Variations

We present work on the application of sequential supervised machine learning for a reduced-dimension, ballistic deposition, Monte Carlo particle packing. Calculations are carried out for a combination of three distinguishable hard spheres representing different materials. Each set of spheres has a distribution of particle sizes in order to mimic realistic milling conditions of raw ingredients. Since infinite combinations of particle size, distribution, fraction, and density exist, we employ machine learning to aid in the design optimization of new high packing density mixtures. Previously calculated binary packs of particle radius ratios of 80:1 were analyzed, but this work highlights results of ternary packs with radius ratios greater than 300:1. We demonstrate a sequential learning approach where iterative experiments are performed based on minimizing the uncertainty in the target regime of high packing density. New candidate mixtures are identified via classification rather than regression which provides superior ability to extrapolate into high packing density mixtures.