基于分子动力学与机器学习的非饱和黏土纳米尺度土水保持机制
Nanoscale soil-water retention mechanism of unsaturated clay via MD and machine learning
AI总结：
本文利用分子动力学模拟和神经网络机器学习，从纳米尺度揭示了非饱和黏土中吸附压力与毛细压力的演变规律，证明低含水率时吸附效应主导、含水率升高后毛细作用主导土水保持机制。
摘要：
本文通过分子动力学和机器学习研究非饱和黏土在纳米尺度下的土水保持机制。选择叶蜡石是因为其结构稳定，并且是其他2:1型黏土矿物的前驱体。开展了一系列低饱和度黏土的分子动力学模拟。采用质心法将土壤水表示为点云。通过alpha-shape方法数值测量水-气界面面积。通过区分不同质量含水率下的吸附压力与毛细压力，并考虑表观毛细界面面积（即单位水体积对应的水-气界面面积），分析了纳米尺度下的土水保持机制。利用水分子数密度剖面来量化吸附效应。采用基于神经网络的机器学习技术构建基质吸力、质量含水率与表观水-气界面面积之间的函数关系。数值结果从纳米尺度视角证明：吸附效应主要由黏土表面与水之间的范德华力和羟基水化作用主导。随着质量含水率增加，吸附压力降低，毛细作用在纳米尺度土水保持机制中发挥主导作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
