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A virtual pebble game to ensemble average graph rigidity

机译:一个虚拟的卵石游戏可以使平均图形刚度达到整体

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摘要

BackgroundThe body-bar Pebble Game (PG) algorithm is commonly used to calculate network rigidity properties in proteins and polymeric materials. To account for fluctuating interactions such as hydrogen bonds, an ensemble of constraint topologies are sampled, and average network properties are obtained by averaging PG characterizations. At a simpler level of sophistication, Maxwell constraint counting (MCC) provides a rigorous lower bound for the number of internal degrees of freedom (DOF) within a body-bar network, and it is commonly employed to test if a molecular structure is globally under-constrained or over-constrained. MCC is a mean field approximation (MFA) that ignores spatial fluctuations of distance constraints by replacing the actual molecular structure by an effective medium that has distance constraints globally distributed with perfect uniform density.
机译:背景技术身体酒吧Pebble Game(PG)算法通常用于计算蛋白质和聚合材料中的网络刚度属性。为了解决诸如氢键之类的波动相互作用,对约束拓扑进行了采样,并通过平均PG表征获得了平均网络性能。在更简单的复杂度上,麦克斯韦约束计数(MCC)为体-杆网络内的内部自由度(DOF)数提供了严格的下限,并且通常用于测试分子结构是否在全局范围内-约束或过度约束。 MCC是一种平均场近似(MFA),它通过用有效的介质替换实际的分子结构来忽略距离约束的空间波动,该有效的介质具有距离约束且以均匀的均匀密度全局分布。

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