论文标题
通过MD和机器学习的不饱和粘土的纳米级土壤保留机制
Nanoscale soil-water retention mechanism of unsaturated clay via MD and machine learning
论文作者
论文摘要
在本文中，我们通过分子动力学和机器学习研究了不饱和粘土的纳米级土壤水分保留机制。硫属石是由于其稳定的结构而选择的，也是其他2：1粘土矿物的前体。在低饱和度下进行了一系列的粘土分子动力学模拟。土壤水是通过质量中心方法的点云代表的。通过Alpha形方法数值测量水上界面区域。通过区分不同质量水含量的吸附压力和毛细管压力，并考虑明显的毛细管界面面积（即每单位水量的水空气面积），分析了纳米级的土壤保留机制。水数密度谱用于量化吸附效果。一种基于神经网络的机器学习技术被用来在矩阵吸力，质量水含量和明显的水上接口区域之间构建功能关系。从纳米级的角度来看，我们的数值结果表明，吸附效果由范德华力和粘土表面和水之间的羟基水合支配。随着质量水含量的增加，吸附压力降低，毛细血管在纳米级的土壤水分保留机制中起着重要作用。
In this article, we investigate the nanoscale soil-water retention mechanism of unsaturated clay through molecular dynamics and machine learning. Pyrophyllite was chosen due to its stable structure and as the precursor of other 2:1 clay minerals. A series of molecular dynamics simulations of clay at low degrees of saturation were conducted. Soil water was represented by a point cloud through the center-of-mass method. Water-air interface area was measured numerically by the alpha-shape method. The soil-water retention mechanism at the nanoscale was analyzed by distinguishing adsorptive pressure and capillary pressure at different mass water contents and considering the apparent capillary interface area (i.e., water-air interface area per unit water volume). The water number density profile was used to quantify the adsorption effect. A neural-network based machine learning technique was utilized to construct function relationships among matric suction, the mass water content, and the apparent water-air interface area. Our numerical results have demonstrated from a nanoscale perspective that the adsorption effect is dominated by the van der Waals force and hydroxyl hydration between the clay surface and water. As the mass water content increases, the adsorption pressure decreases, and capillarity plays a prominent role in the soil-water retention mechanism at the nanoscale.
