Graduate School of Natural Science and Technology, Kanazawa University, Kakuma, Kanazawa 920-1192, Japan.
Faculty of Mathematics and Physics, Kanazawa University, Kakuma, Kanazawa 920-1192, Japan.
J Chem Phys. 2018 Aug 21;149(7):072322. doi: 10.1063/1.5028466.
In the present paper, a generalized hybrid Monte Carlo method to generate the multicanonical ensemble has been developed, which is a generalization of the multicanonical hybrid Monte Carlo (HMC) method by Hansmann and co-workers [Chem. Phys. Lett. , 321 (1996)]. The generalized hybrid Monte Carlo (GHMC) method is an equations-of-motion guided Monte Carlo combined with partial momentum refreshment. We successfully applied our multicanonical GHMC to dense Lennard-Jones fluids and a coarse grained protein model. It is found that good computational efficiency can be gained in the case of the acceptance ratio around 60% for the models examined. While a large number of molecular dynamics (MD) steps in a single GHMC cycle is needed to yield good computational efficiency at a large mixing ratio of momenta with thermal noise vectors, corresponding to the original multicanonical HMC method, a small number of MD steps are enough to achieve good efficiency at a small mixing ratio. This property is useful to develop a composite algorithm combining the present GHMC method with other Monte Carlo moves.
在本文中，我们开发了一种广义的混合蒙特卡罗方法来生成多正则系综，这是 Hansmann 等人提出的多正则混合蒙特卡罗（HMC）方法的推广[Chem. Phys. Lett., 321 (1996)]。广义混合蒙特卡罗（GHMC）方法是一种运动方程引导的蒙特卡罗方法，结合了部分动量更新。我们成功地将我们的多正则 GHMC 应用于密 Lennard-Jones 流体和一个粗粒化蛋白质模型。结果发现，对于所研究的模型，接受率约为 60%时，可以获得良好的计算效率。虽然在单个 GHMC 循环中需要大量的分子动力学（MD）步骤，以在与热噪声向量的动量混合比较大的情况下产生良好的计算效率，这对应于原始的多正则 HMC 方法，但在动量混合比较小的情况下，只需少量的 MD 步骤即可实现良好的效率。这一特性有助于开发一种组合算法，将目前的 GHMC 方法与其他蒙特卡罗移动相结合。
