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arXiv 1707.04593cond-mat.softcond-mat.stat-mech
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使用机器学习揭示液态水中的两种局部秩序

Unveiling two types of local order in liquid water using machine learning

Adrián Soto, Deyu Lu, Shinjae Yoo, Mariví Fernández-Serra

摘要:

机器学习方法正在许多科学领域进行探索,目的是找到解决问题的方法,这些问题由于其复杂性而无法通过传统的科学方法来解决。一般来说,能够识别由对应的相变分离的物质的两个不同相的序参量是基于对称性参数构造的。该参数测量随着相变进行的有序度。然而,当两个不同的阶段是高度无序的,它是不平凡的,以确定破碎的对称性,找到一个序参量。这提出了一个很好的问题,需要使用机器学习程序来解决。室温液态水被假设为超临界液体,具有与两个母液相相关的两种不同分子级的波动,一个具有高密度,另一个具有低密度。这一假设的有效性与能够识别两种不同液相及其波动的序参量的存在有关。在这项工作中,我们展示了两种不同的机器学习程序如何能够识别液态水中的局部顺序。我们认为,为了从这种复杂性中学习相关特征,对可用数据进行初始的物理动机准备与数据集的质量一样重要,并且机器学习只有在与高级物理信息相结合时才能成为成功的分析工具。

英文摘要:

Machine learning methods are being explored in many areas of science, with the aim of finding solution to problems that evade traditional scientific approaches due to their complexity. In general, an order parameter capable of identifying two different phases of matter separated by a correspond- ing phase transition is constructed based on symmetry arguments. This parameter measures the degree of order as the phase transition proceeds. However, when the two distinct phases are highly disordered it is not trivial to identify broken symmetries with which to find an order parameter. This poses an excellent problem to be addressed using machine learning procedures. Room tem- perature liquid water is hypothesized to be a supercritical liquid, with fluctuations of two different molecular orders associated to two parent liquid phases, one with high density and another one with low density. The validity of this hypothesis is linked to the existence of an order parameter capable of identifying the two distinct liquid phases and their fluctuations. In this work we show how two different machine learning procedures are capable of recognizing local order in liquid water. We argue that when in order to learn relevant features from this complexity, an initial, physically motivated preparation of the available data is as important as the quality of the data set, and that machine learning can become a successful analysis tool only when coupled to high level physical information.

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。

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