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Data-Driven Coarse-Graining using Space-Time Diffusion Maps

使用时空扩散图的数据驱动粗粒度

基本信息

批准号：
EP/P006175/1
负责人：
Benedict Leimkuhler
金额：
$ 38.84万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FP006175%2F1
关键词：
Data Driven Coarse Graining using

项目摘要

Dynamical systems with many degrees of freedom arise in a wide range of applications, including large scale molecular dynamics, climate and weather studies, and electrical power networks. The challenge in simulation is normally to extract statistical information, for example the average propensity of a given state of the system or the average time that elapses between certain events. Simulation data is easy to generate but often poorly utilized. The goal of this project is the development of a data-driven method for the automatic detection of a simplified description of the system based on a set of collective variables which can be used within efficient statistical extraction procedures. These slowest degrees of freedom are typically the most important ones. The dynamics are characterised as fluctuations in the vicinity of given state punctuated by relatively rare events describing transitions between the states. Efficiently identifying collective variables is the crucial first step in the design of coarse-grained models which can allow many order of magnitude increases in the accessible simulation timescale. By automatically finding collective variables, we can greatly simplify rapid study and comparison of many systems. The research builds on the technique of diffusion maps, whereby the eigenfunctions of a diffusion operator are used to characterise the metastable (slowly changing) states of the system. The potential impact of automatic coarse-graining will be felt most profoundly in fields such as rational drug design, where it is necessary to select specific drug molecules for their properties in interaction with some target, e.g. a protein. Bio-molecular simulation depends on the use of very specialised and intensely developed simulation codes which are the products of many years of development and government investment. In order to accelerate the implementation and testing of novel algorithms in this important area, this project includes a detailed plan for software development within the EPSRC-funded MIST (Molecular Integrator Software Tools) platform. Testing of the software methodology will be conducted via collaborations with chemists and pharmaceutical chemists, including researchers at Rice University (Houston, Texas) and Memorial Sloan Kettering Cancer Research Center (New York).

具有多个自由度的动力系统出现在广泛的应用中，包括大规模分子动力学、气候和天气研究以及电力网络。模拟中的挑战通常是提取统计信息，例如系统给定状态的平均倾向或某些事件之间经过的平均时间。模拟数据很容易生成，但往往利用率不高。该项目的目标是开发一种数据驱动的方法，用于基于一组集体变量自动检测系统的简化描述，这些变量可在有效的统计提取程序中使用。这些最慢的自由度通常是最重要的。动态的特征是给定状态附近的波动，其中不时出现描述状态之间转换的相对罕见的事件。有效识别集体变量是粗粒度模型设计中至关重要的第一步，它可以允许可访问的模拟时间尺度增加许多数量级。通过自动查找集体变量，我们可以大大简化许多系统的快速研究和比较。该研究建立在扩散图技术的基础上，利用扩散算子的本征函数来表征系统的亚稳态（缓慢变化）状态。自动粗粒度的潜在影响将在合理药物设计等领域受到最深刻的影响，在这些领域中，有必要根据其与某些靶点相互作用的特性来选择特定的药物分子，例如药物分子。一种蛋白质。生物分子模拟依赖于非常专业且高度开发的模拟代码的使用，这些代码是多年开发和政府投资的产物。为了加速这一重要领域新算法的实施和测试，该项目包括在 EPSRC 资助的 MIST（分子积分软件工具）平台内进行软件开发的详细计划。软件方法的测试将通过与化学家和药物化学家的合作进行，包括莱斯大学（德克萨斯州休斯顿）和纪念斯隆凯特琳癌症研究中心（纽约）的研究人员。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.

DOI：
10.1021/acs.jctc.0c00355
发表时间：
2020-08-11
期刊：
Journal of chemical theory and computation
影响因子：
5.5
作者：
Gkeka P;Stoltz G;Barati Farimani A;Belkacemi Z;Ceriotti M;Chodera JD;Dinner AR;Ferguson AL;Maillet JB;Minoux H;Peter C;Pietrucci F;Silveira A;Tkatchenko A;Trstanova Z;Wiewiora R;Lelièvre T
通讯作者：
Lelièvre T

Simplest random walk for approximating Robin boundary value problems and ergodic limits of reflected diffusions

用于逼近 Robin 边值问题和反射扩散的遍历极限的最简单随机游走