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A new topological descriptor for water network structure

机译:水网结构的新拓扑描述符

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摘要

Bulk water molecular dynamics simulations based on a series of atomistic water potentials (TIP3P, TIP4P/Ew, SPC/E and OPC) are compared using new techniques from the field of topological data analysis. The topological invariants (the different degrees of homology) derived from each simulation frame are used to create a series of persistence diagrams from the atomic positions. These are averaged over the simulation time using the persistence image formalism, before being normalised by their total magnitude (the L1 norm) to ensure a size independent descriptor (L1NPI). We demonstrate that the L1NPI formalism is suitable for the analysis of systems where the number of molecules varies by at least a factor of 10. Using standard machine learning techniques, a basic linear SVM, it is shown that differences in water models are able to be isolated to different degrees of homology. In particular, whereas first degree homology is able to distinguish between all atomistic potentials studied, OPC is the only potential that differs in its second degree homology. The L1 normalised persistence images are then used in the comparison of a series of Stillinger–Weber potential simulations to the atomistic potentials and the effects of changing the strength of three-body interactions on the structures is easily evident in L1NPI space, with a reduction in variance of structures as interaction strength increases being the most obvious result. Furthermore, there is a clear tracking in L1NPI space of the λ parameter. The L1NPI formalism presents a useful new technique for the analysis of water and other materials. It is approximately size-independent, and has been shown to contain information as to real structures in the system. We finally present a perspective on the use of L1NPIs and other persistent homology techniques as a descriptor for water solubility.Electronic supplementary materialThe online version of this article (10.1186/s13321-019-0369-0) contains supplementary material, which is available to authorized users.
机译:使用来自拓扑数据分析领域的新技术,对基于一系列原子水势(TIP3P,TIP4P / Ew,SPC / E和OPC)的本体水分子动力学模拟进行了比较。从每个模拟框架派生的拓扑不变量(不同程度的同源性)用于从原子位置创建一系列余辉图。在使用持久性图像形式主义对这些时间进行平均后,再通过它们的总大小(L1范数)对其进行归一化,以确保大小无关的描述符(L1NPI)。我们证明L1NPI形式主义适用于分析分子数量至少相差10倍的系统。使用标准的机器学习技术,基本的线性SVM,可以证明水模型中的差异能够分离到不同程度的同源性。特别地,尽管一阶同源性能够区分所有研究的原子势,但OPC是唯一在其二阶同源性方面不同的势能。然后,将L1归一化的余辉图像用于一系列Stillinger-Weber势模拟与原子势的比较,并且在L1NPI空间中很容易看到改变三体相互作用强度对结构的影响,并且减小了随着相互作用强度的增加,结构的变化是最明显的结果。此外,在L1NPI空间中对λ参数有清晰的跟踪。 L1NPI形式主义为分析水和其他材料提供了一种有用的新技术。它大约与大小无关,并且已显示包含有关系统中实际结构的信息。我们最终提出了关于使用L1NPI和其他持久同源性技术作为水溶性描述子的观点。电子补充材料本文的在线版本(10.1186 / s13321-019-0369-0)包含补充材料,可供授权使用用户。

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