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首页> 外文期刊>The Journal of Chemical Physics >Ab initio sampling of transition paths by conditioned Langevin dynamics
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Ab initio sampling of transition paths by conditioned Langevin dynamics

机译：通过条件Langevin Dynamics的过渡路径AB Initio采样

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摘要

We propose a novel stochastic method to generate Brownian paths conditioned to start at an initial point and end at a given final point during a fixed time t(f) under a given potential U(x). These paths are sampled with a probability given by the overdamped Langevin dynamics. We show that these paths can be exactly generated by a local stochastic partial differential equation. This equation cannot be solved in general but we present several approximations that are valid either in the low temperature regime or in the presence of barrier crossing. We show that this method warrants the generation of statistically independent transition paths. It is computationally very efficient. We illustrate the method first on two simple potentials, the two-dimensional Mueller potential and the Mexican hat potential, and then on the multi-dimensional problem of conformational transitions in proteins using the "Mixed Elastic Network Model" as a benchmark. Published by AIP Publishing.
机译：我们提出了一种新颖的随机方法，以产生调节的棕色路径以在给定电位U（x）下的固定时间t（f）下的定时点处的初始点处的初始点。 这些路径采样，具有覆盖Langevin动态的概率。 我们表明这些路径可以由本地随机偏微分方程精确生成。 该等式通常不能解决，但是我们在低温制度或存在障碍交叉的情况下呈现有效的几个近似值。 我们表明该方法保证了统计上独立的过渡路径的产生。 它是计算方式非常有效。 我们首先在两个简单的潜力，二维穆勒潜在和墨西哥帽子潜力中说明了该方法，然后使用“混合弹性网络模型”作为基准的蛋白质化构象转变的多维问题。 通过AIP发布发布。

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