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.

如果通过经典力学计算原子运动，并且是否从简单功能（力场）中提取原子之间的力，则可以在显微镜上模拟大分子组合物的动力学，包括数十亿个原子（直到毫秒的状态）。特别是在生物化学领域，在过去的几十年中建立了几个标准力场，可以模拟具有明确溶剂化的完整蛋白质，但不允许化学反应（破坏或形成化学键）。使用这些力场是如此普遍，即使有些专家也认为力场模拟确实无法捕获化学反应。但是，原则上，这是可能的。实际上，化学文献中有几个反应力场，但尚不清楚。这样做的主要原因是，反应力场比非反应性的参数更为复杂，并且包含更多的参数。成功使用力场需要拟合其参数来参考数据。大量参数将这种拟合转化为一个高度复杂的优化问题，其质量很大的最佳功能。因此，传统优化方法的应用需要大量的工作和经验，只是为了将不良参数设置为不糟糕的参数。但是，现代的全球优化方法非常适合此类任务。因此，在这个项目中，我们将借鉴我们在遗传/进化算法上的长期经验，并将其应用于反应力场的全球优化。我们对测试案例的初步研究表明，这是可能的和成功的。此外，他们发现了在多个层面上令人尴尬的并行化的可能性。因此，可以预期，这种优化计算将从现代的，大量平行的计算机架构中获得显着利润。在该项目中，我们将使用不同级别（高于高端电子结构结果）的参考数据来研究遗传/进化算法对反应力场的全球参数优化的现实和一般适用性，用于几种不同的反应力场和几种不同的化学系统，以及几种不同的化学系统。我们计划将这些研究引起的优化程序分发给用户社区，既可以灵活地独立应用，又嵌入到标准的量子化学软件包中。这将使最终用户能够生成自己的反应力场，特别是用于选择的化学反应，因此可以提高精度，并将其应用于大规模的反应性分子动力学模拟中。