Investigation of water desalination/purification with molecular dynamics and machine learning techniques

IF 1.4 Q4 MATERIALS SCIENCE, MULTIDISCIPLINARY AIMS Materials Science Pub Date : 2022-01-01 DOI:10.3934/matersci.2022054
Christos Stavrogiannis, F. Sofos, T. Karakasidis, D. Vavougios
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Abstract

This paper incorporates a number of parameters, such as nanopore size, wall wettability, and electric field strength, to assess their effect on ion removal from nanochannels filled with water. Molecular dynamics simulations are incorporated to monitor the process and a numerical database is created with the results. We show that the movement of ions in water nanochannels under the effect of an electric field is multifactorial. Potential energy regions of various strength are formed inside the nanochannel, and ions are either drifted to the walls and rejected from the solution or form clusters that are trapped inside low potential energy regions. Further computational investigation is made with the incorporation of machine learning techniques that suggest an alternative path to predict the water/ion solution properties. Our test procedure here involves the calculation of diffusion coefficient values and the incorporation of four ML algorithms, for comparison reasons, which exploit MD calculated results and are trained to predict the diffusion coefficient values in cases where no simulation data exist. This two-fold computational approach constitutes a fast and accurate solution that could be adjusted to similar ion separation models for property extraction.
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用分子动力学和机器学习技术研究海水淡化/净化
本文结合了一些参数,如纳米孔大小、壁润湿性和电场强度,来评估它们对从充满水的纳米通道中去除离子的影响。结合分子动力学模拟来监测过程,并根据结果创建了数值数据库。我们证明了离子在电场作用下在水纳米通道中的运动是多因素的。纳米通道内部形成了不同强度的势能区,离子要么漂移到壁上,被溶液排斥,要么形成团簇,被困在低势能区。进一步的计算研究结合了机器学习技术,提出了预测水/离子溶液性质的替代途径。我们这里的测试过程涉及扩散系数值的计算和四种ML算法的结合,出于比较的原因,这些算法利用MD的计算结果并经过训练,可以在没有模拟数据的情况下预测扩散系数值。这种双重计算方法构成了一个快速和准确的解决方案,可以调整到类似的离子分离模型的性质提取。
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来源期刊
AIMS Materials Science
AIMS Materials Science MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
3.60
自引率
0.00%
发文量
33
审稿时长
4 weeks
期刊介绍: AIMS Materials Science welcomes, but not limited to, the papers from the following topics: · Biological materials · Ceramics · Composite materials · Magnetic materials · Medical implant materials · New properties of materials · Nanoscience and nanotechnology · Polymers · Thin films.
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