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Bridging coarse-grained models by jump-in-sample simulations

机译:通过样本中的仿真桥接粗粒度模型

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We present an efficient method to construct coarse-grained (CG) models from models of, finer resolution. The method estimates the free energies in a generated sample of the CG conformational space and then fits the entire effective potential surface in the high-dimensional CG conformational space. A jump-in-sample algorithm that uses a random jumping walk in the CG sample is used to iteratively estimate the free energies. We test the method in a tetrahedral molecular fluid where we construct the intermolecular effective potential and evaluate the CG molecular model. Our algorithm for calculating the free energy involves an improved Wang-Landau (WL) algorithm, which not only works more efficiently than the standard WL algorithm, but also can work in high-dimensional spaces. (C) 2008 American Institute of Physics.
机译:我们提出了一种从更精细的分辨率模型构建粗粒度(CG)模型的有效方法。该方法估计生成的CG构象空间样本中的自由能,然后将整个有效势面拟合到高维CG构象空间中。在CG样本中使用随机跳跃游动的样本中跳跃算法用于迭代估算自由能。我们在四面体分子流体中测试该方法,在其中构造分子间有效电位并评估CG分子模型。我们计算自由能的算法涉及改进的Wang-Landau(WL)算法,该算法不仅比标准WL算法更有效,而且可以在高维空间中工作。 (C)2008美国物理研究所。

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