权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Global optimization of reactive force fields

反作用力场的全局优化

基本信息

批准号：
245894149
负责人：
Professor Dr. Bernd Hartke
金额：
--
依托单位：
Institut für Physikalische Chemie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/245894149?language=en
关键词：
Global optimization reactive force fields

项目摘要

The dynamics of large molecular assemblies, comprising billions of atoms, can be simulated for microscopically long times (up to the regime of milliseconds), if atomic movement is calculated via classical mechanics and if forces between atoms are extracted from simple functions (force fields). Particularly in the area of biochemistry, several standard force fields were established in the past decades, allowing for simulations of complete proteins with explicit solvation, but not allowing for chemical reactions (breaking or forming chemical bonds). Usage of these force fields is so commonplace that even some experts assume that force-field simulations really cannot capture chemical reactions. In principle, however, this is possible. Actually, there are several reactive force fields in the chemical literature, but they are not well known. A main reason for this is that reactive force fields are more complex and contain far more parameters than non-reactive ones. Successful use a force field requires fitting of its parameters to reference data. A large number of parameters transform this fitting into a highly complicated optimization problem with very many optima of widely varying quality. Application of traditional optimization methods hence requires a lot of work and experience, just to improve a bad parameter set to a less bad one.Modern global optimization methods, however, are very well suited for such tasks. Hence, in this project, we will draw upon our long years of experience with genetic/evolutionary algorithms and apply them to the global optimization of reactive force fields. Our preliminary studies on test cases have shown that this is possible and successful. In addition, they have uncovered possibilities for embarrassing parallelization on several levels. Hence, such optimization calculations can be expected to profit significantly from modern, massively parallel computer architectures.In this project, we will investigate the realistic and general applicability of genetic/evolutionary algorithms to global parameter optimization of reactive force fields, using reference data on different levels (up to high-end electronic structure results), for several different reactive force fields and for several different real chemical systems. We plan to distribute the optimization routines resulting from these studies to the user community, both as flexible stand-alone application and embedded into standard quantum-chemistry packages. This will allow end-users to generate their own reactive force fields, specifically for chemical reactions of their choice and hence with heightened accuracy, and to apply them in large-scale reactive molecular dynamics simulations.

如果原子的运动是通过经典力学计算的，如果原子之间的力是从简单的函数(力场)中提取的，那么由数十亿个原子组成的大分子组装的动力学可以在微观上模拟很长的时间(高达几毫秒的范围)。特别是在生物化学领域，在过去的几十年里建立了几个标准力场，允许通过显式溶剂化来模拟完整的蛋白质，但不允许化学反应(断裂或形成化学键)。这些力场的使用是如此普遍，以至于甚至一些专家都认为，力场模拟确实无法捕捉化学反应。然而，原则上，这是可能的。事实上，在化学文献中有几个反应力场，但它们并不为人所熟知。一个主要原因是反作用力比非反作用力要复杂得多，包含的参数也多得多。成功使用力场需要将其参数与参考数据进行拟合。大量的参数将这种拟合转化为一个高度复杂的优化问题，该问题具有非常多的质量差异很大的最优解。因此，传统优化方法的应用需要大量的工作和经验，仅仅是为了将一个坏的参数集改进成一个不那么坏的参数集，而现代的全局优化方法非常适合于这样的任务。因此，在这个项目中，我们将利用我们在遗传/进化算法方面的多年经验，将它们应用于反作用力的全局优化。我们对测试案例的初步研究表明，这是可能的，也是成功的。此外，他们还发现了几个层面上令人尴尬的并行化的可能性。因此，这种优化计算有望从现代大规模并行计算机体系结构中获益。在这个项目中，我们将使用不同水平(直到高端电子结构结果)的参考数据，针对几个不同的反应力场和几个不同的实际化学体系，研究遗传/进化算法在反作用力场全局参数优化中的现实和普遍适用性。我们计划将这些研究产生的优化例程分发给用户社区，既可以作为灵活的独立应用程序，也可以嵌入到标准的量子化学包中。这将允许最终用户生成他们自己的反应力场，特别是针对他们选择的化学反应，因此具有更高的准确性，并将它们应用于大规模反应分子动力学模拟。