• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The Journal of Chemical Physics >A new class of enhanced kinetic sampling methods for building Markov state models
【24h】

A new class of enhanced kinetic sampling methods for building Markov state models

机译：建筑马尔可夫州模型的新一类增强动力学采样方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Markov state models (MSMs) and other related kinetic network models are frequently used to study the long-timescale dynamical behavior of biomolecular and materials systems. MSMs are often constructed bottom-up using brute-force molecular dynamics (MD) simulations when the model contains a large number of states and kinetic pathways that are not known a priori. However, the resulting network generally encompasses only parts of the configurational space, and regardless of any additional MD performed, several states and pathways will still remain missing. This implies that the duration for which the MSM can faithfully capture the true dynamics, which we term as the validity time for the MSM, is always finite and unfortunately much shorter than the MD time invested to construct the model. A general framework that relates the kinetic uncertainty in the model to the validity time, missing states and pathways, network topology, and statistical sampling is presented. Performing additional calculations for frequently-sampled states/pathways may not alter the MSM validity time. A new class of enhanced kinetic sampling techniques is introduced that aims at targeting rare states/pathways that contribute most to the uncertainty so that the validity time is boosted in an effective manner. Examples including straightforward 1D energy landscapes, lattice models, and biomolecular systems are provided to illustrate the application of the method. Developments presented here will be of interest to the kinetic Monte Carlo community as well. Published by AIP Publishing.
机译：马尔可夫状态模型（MSM）和其他相关动力网络模型经常用于研究生物分子和材料系统的长时间的动态行为。 MSM通常使用Brute-Force Moleculy Dynamics（MD）模拟构造自下而上，当模型包含许多州和不知道先验的动态路径时。然而，生成的网络通常只包含配置空间的部分，而且无论执行的任何额外的MD如何，仍将缺少多个状态和路径。这意味着MSM可以忠实地捕获真正动态的持续时间，我们术语作为MSM的有效时间，始终有限，不幸的是，比投资构造模型的MD时间要短得多。提出了一种将模型中的动力学不确定性与有效时间，缺失状态和途径，网络拓扑和统计采样相关的一般框架。对频繁采样状态/路径执行额外的计算可能不会改变MSM有效时间。引入了一种新的增强的动力学采样技术，其旨在瞄准为不确定性贡献最大的稀有状态/途径，以便以有效的方式提升有效时间。提供包括直接1D能量景观，晶格模型和生物分子系统的示例，以说明该方法的应用。此处提出的发展也会对动力学蒙特卡罗社区感兴趣。通过AIP发布发布。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号