分子动力学数据中代表性局部原子排布的无监督学习
Unsupervised learning of representative local atomic arrangements in molecular dynamics data
AI总结：
针对分子动力学模拟数据依赖人工解读易遗漏关键信息的问题，研究结合UMAP降维、HDBSCAN聚类与对齐/形状匹配算法，定量表征局部配位环境并划分结构异构体家族，成功揭示分子液体电解质的阳离子配位细节。
摘要：
分子动力学（MD）模拟带来了数据挖掘挑战，因为这类模拟可生成海量数据，但对其信息内容的分析往往依赖有限或存在偏差的人工解读。若未能对MD数据提出恰当问题，我们可能错过其中隐藏的关键信息。我们结合降维算法UMAP（均匀流形近似与投影）和无监督层次聚类算法HDBSCAN（基于密度的带噪声应用空间聚类层次版本），对MD数据中化学物种的普遍配位环境进行定量表征。通过聚焦局部配位，我们提取给定配位球内所有不同的分子式，大幅缩减了待分析的数据量。随后我们将UMAP与HDBSCAN同对齐算法或形状匹配算法高效结合，将这些分子式划分为结构异构体家族，并标明其相对占比。该方法已被用于揭示基于分子液体的电解质中阳离子配位的细节。
英文摘要：
Molecular dynamics (MD) simulations present a data-mining challenge, given that they can generate a considerable amount of data but often rely on limited or biased human interpretation to examine their information content. By not asking the right questions of MD data we may miss critical information hidden within it. We combine dimensionality reduction (UMAP) and unsupervised hierarchical clustering (HDBSCAN) to quantitatively characterize prevalent coordination environments of chemical species within MD data. By focusing on local coordination, we significantly reduce the amount of data to be analyzed by extracting all distinct molecular formulas within a given coordination sphere. We then efficiently combine UMAP and HDBSCAN with alignment or shape-matching algorithms to partition these formulas into structural isomer families indicating their relative populations. The method was employed to reveal details of cation coordination in electrolytes based on molecular liquids.
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