Machine learning approaches for the optimization of packing densities in granular matter
Machine learning approaches for the optimization of packing densities in granular matter
复制标题
用于优化颗粒物质堆积密度的机器学习方法
DOI：
10.1039/d2sm01430k
复制
发表时间：
2023
期刊：
影响因子：
3.4
通讯作者：
Makse, Hernán A.
中科院分区：
文献类型：
--
作者：
Baule, Adrian;Kurban, Esma;Liu, Kuang;Makse, Hernán A.
颗粒物质的密度以及这种密度如何取决于组成颗粒的形状的基本问题一直是一个长期存在的科学问题。以前的工作主要集中在基于模拟或平均场理论的经验方法，以调查形状变化对所得的堆积密度的影响，集中在一小组预定义的形状，如二聚体，椭球体和spherocylinders。在这里，我们讨论机器学习方法如何支持在高维形状空间中搜索最佳密集包装形状。我们应用降维和回归技术的基础上，随机森林和神经网络找到新的密集堆积形状的数值优化。此外，在降维的形状表示的回归函数的调查，使我们能够确定方向的堆积密度景观，导致强烈的非单调变化的堆积密度。通过机器学习获得的预测与包装模拟进行了比较。我们的方法可以更广泛地应用于通过改变其组成颗粒的形状来优化颗粒物质的性质。
The fundamental question of how densely granular matter can pack and how this density depends on the shape of the constituent particles has been a longstanding scientific problem. Previous work has mainly focused on empirical approaches based on simulations or mean-field theory to investigate the effect of shape variation on the resulting packing densities, focusing on a small set of pre-defined shapes like dimers, ellipsoids, and spherocylinders. Here we discuss how machine learning methods can support the search for optimally dense packing shapes in a high-dimensional shape space. We apply dimensional reduction and regression techniques based on random forests and neural networks to find novel dense packing shapes by numerical optimization. Moreover, an investigation of the regression function in the dimensionally reduced shape representation allows us to identify directions in the packing density landscape that lead to a strongly non-monotonic variation of the packing density. The predictions obtained by machine learning are compared with packing simulations. Our approach can be more widely applied to optimize the properties of granular matter by varying the shape of its constituent particles.