广义系综中大间隔采样参数的增强采样：温度空间随机游走的实例研究
Enhanced sampling in generalized ensemble with large gap of sampling parameter: case study in temperature space random walk
摘要：
我们提出了一个有效的采样方法计算的配分函数和加速配置采样。该方法在$λ$空间中执行随机游走，其中$λ$是表征正则系综的任何热力学变量，诸如倒数温度$β$或哈密顿量明确依赖的任何变量。（在$λ=β$的情况下为能量），定义为来自配分函数的动态更新的导数的值与从模拟直接测量的值之间的差。配分函数的高阶导数被包括以增强$λ$空间中的布朗运动。该方法对系统大小和$λ$窗口大小的敏感性比其他方法小得多。在二维伊辛模型上，证明了该方法渐近收敛于配分函数，且配分函数的对数误差比采用Wang-Landau递推格式的算法小得多.该方法也适用于非晶格模型蛋白质，$AB$模型，在这种情况下，许多低能量状态被发现在不同的模型。
英文摘要：
We present an efficient sampling method for computing a partition function and accelerating configuration sampling. The method performs a random walk in the $λ$ space, with $λ$ being any thermodynamic variable that characterizes a canonical ensemble such as the reciprocal temperature $β$ or any variable that the Hamiltonian explicitly depends on. The partition function is determined by minimizing the difference of the thermal conjugates of $λ$ (the energy in the case of $λ=β$), defined as the difference between the value from the dynamically updated derivatives of the partition function and the value directly measured from simulation. Higher-order derivatives of the partition function are included to enhance the Brownian motion in the $λ$ space. The method is much less sensitive to the system size, and the size of $λ$ window than other methods. On the two dimensional Ising model, it is shown that the method asymptotically converges the partition function, and the error of the logarithm of the partition function is much smaller than the algorithm using the Wang-Landau recursive scheme. The method is also applied to off-lattice model proteins, the $AB$ models, in which cases many low energy states are found in different models.
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。
