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.

我们介绍了顺序监督机器学习在降维、弹道沉积、蒙特卡洛粒子堆积方面的应用。对代表不同材料的三个可区分的硬球的组合进行计算。每组球体都具有粒度分布，以模拟原料成分的真实研磨条件。由于存在粒径、分布、分数和密度的无限组合，我们采用机器学习来帮助设计优化新的高填充密度混合物。之前计算的粒子半径比为 80:1 的二元包被分析，但这项工作突出了半径比大于 300:1 的三元包的结果。我们展示了一种顺序学习方法，其中基于最小化高堆积密度目标区域的不确定性来执行迭代实验。新的候选混合物是通过分类而不是回归来识别的，这提供了外推到高堆积密度混合物的卓越能力。