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在广泛的应用中出现，包括大型分子动力学，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，系统状态的平均倾向或某些事件之间超出的平均时间。仿真数据易于生成，但通常使用较差。该项目的目的是开发数据驱动的方法，用于根据一组可以在有效的统计提取过程中使用的集体变量来自动检测系统的简化描述。这些最慢的自由度通常是最重要的自由度。该动力学的特征是在给定状态附近的波动，其描述了国家之间过渡的相对罕见的事件。有效地识别集体变量是设计粗粒模型设计的至关重要的第一步，它可以使可访问的仿真时间尺度的数量级增加。通过自动找到集体变量，我们可以极大地简化许多系统的快速研究和比较。该研究以扩散图的技术为基础，从而使用扩散操作员的特征函数来表征系统的亚稳态（缓慢变化）状态。在理性药物设计等领域中，自动粗胶质的潜在影响将最深刻地感受到，在这种领域中，有必要在与某些靶标相互作用时选择特定的药物分子，例如蛋白质。生物分子模拟取决于使用非常专业和强烈开发的仿真代码，这些代码是多年发展和政府投资的产物。为了加速该重要领域中新型算法的实施和测试，该项目包括EPSRC资助的雾（Molecular Integrator Software Tools）平台内软件开发的详细计划。该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，包括赖斯大学（得克萨斯州休斯顿）和纪念Sloan Kettering Cancer Research Center（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