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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. 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. Each set of spheres has a distribution of particle sizes in order to mimic realistic milling conditions of raw ingredients. Calculations are carried out for a combination of three distinguishable hard spheres representing different materials. We present work on the application of sequential supervised machine learning for a reduced-dimension, ballistic deposition, Monte Carlo particle packing.
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