Adaptive Collective Variables: Automatic Identification and Application of Multiresolution Modelling

自适应集体变量：多分辨率建模的自动识别与应用

• 批准号: EP/J014265/1
• 负责人: Charles Laughton
• 金额: $ 4.25万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ014265%2F1
• 关键词: Adaptive; Collective; Variables; Automatic; Identification

Computer modelling is a powerful tool for understanding at the atomic level how biological molecules and systems work. These simulations require extremely powerful computers and even then they are not always able to produce simulations that are as long or as detailed as we really need. One approach to help overcome this is to concentrate most of the computer power on giving detailed information about small, key regions of the system while the surroundings are simulated more roughly. This is called multiscale, or multiresolution, modelling.At present there is no good software to do this. The aim of this project is to assemble a group of experts from the US and the UK who have the combination of skills and research experience to tackle this problem. In this network-building stage, we will meet to exchange detailed information about our current research activities, and plan how we can integrate them.We will also undertake a small number of pilot studies to check that our plans are feasable.

计算机模拟是在原子水平上理解生物分子和系统如何工作的强大工具。这些模拟需要非常强大的计算机，即使这样，它们也不总是能够产生我们真正需要的那么长或那么详细的模拟。帮助克服这一问题的一种方法是，将大部分计算机能力集中在提供有关系统小的关键区域的详细信息上，同时对周围环境进行更粗略的模拟。这被称为多尺度或多分辨率建模。目前还没有好的软件来做这件事。该项目的目的是召集一批来自美国和英国的专家，他们拥有解决这一问题的技能和研究经验。在这个网络建设阶段，我们将会面，就我们目前的研究活动交换详细信息，并计划如何整合这些活动。我们还将进行少量的试点研究，以检查我们的计划是否可行。