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Cluster analysis of accelerated molecular dynamics simulations: A case study of the decahedron to icosahedron transition in Pt nanoparticles

机译:加速分子动力学模拟的集群分析 - 以纳米粒子划分为例

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Modern molecular-dynamics-based techniques are extremely powerful to investigate the dynamical evolution of materials. With the increase in sophistication of the simulation techniques and the ubiquity of massively parallel computing platforms, atomistic simulations now generate very large amounts of data, which have to be carefully analyzed in order to reveal key features of the underlying trajectories, including the nature and characteristics of the relevant reaction pathways. We show that clustering algorithms, such as the Perron Cluster Cluster Analysis, can provide reduced representations that greatly facilitate the interpretation of complex trajectories. To illustrate this point, clustering tools are used to identify the key kinetic steps in complex accelerated molecular dynamics trajectories exhibiting shape fluctuations in Pt nanoclusters. This analysis provides an easily interpretable coarse representation of the reaction pathways in terms of a handful of clusters, in contrast to the raw trajectory that contains thousands of unique states and tens of thousands of transitions. Published by AIP Publishing.
机译:基于现代分子动力学的技术非常强大,可以调查材料的动态演化。随着模拟技术的复杂性的增加和大规模并行计算平台的u特异性,原子模拟现在产生了非常大量的数据,必须仔细分析,以便揭示潜在轨迹的关键特征,包括性质和特征相关反应途径。我们表明聚类算法,例如珀罗群集聚类分析,可以提供极大地促进复杂轨迹的解释的减少表示。为了说明这一点,用于识别在PT纳米能器中表现出形状波动的复杂加速分子动力学轨迹中的关键动力学步骤。该分析提供了一种易于解释的粗略表示反应途径,与少数群集相比,与含有数千个独特的州和数万个过渡的原始轨迹相反。通过AIP发布发布。

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