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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从简单函数（力场）中提取原子之间的力，则包含数十亿个原子的大型分子组装体的动力学可以在微观上长时间（长达毫秒）进行模拟。特别是在生物化学领域，过去几十年建立了几个标准力场，允许模拟具有显式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）。这些力场的使用非常普遍，甚至一些专家也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。实际上，化学文献中有几个反作用力场，但它们并不为人们所熟知。造成这种情况的主要原因是反作用力场比非反作用力场更复杂并且包含更多的参数。成功使用力场需要将其参数拟合到参考数据。大量参数将这种拟合转化为高度复杂的优化问题，具有许多质量差异很大的最优值。因此，传统优化方法的应用需要大量的工作和经验，只是为了将不好的参数集改进为不太坏的参数集。然而，现代全局优化方法非常适合此类任务。因此，在这个项目中，我们将利用多年的遗传/进化算法经验，并将其应用于反作用力场的全局优化。我们对测试用例的初步研究表明这是可能且成功的。此外，他们还发现了在多个层面上令人尴尬的并行化的可能性。因此，此类优化计算有望从现代大规模并行计算机架构中获益匪浅。在本项目中，我们将针对几种不同的反作用力场和几种不同的真实化学系统，使用不同级别的参考数据（直至高端电子结构结果），研究遗传/进化算法对反作用力场全局参数优化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，既作为灵活的独立应用程序，又嵌入到标准量子化学包中。这将允许最终用户生成自己的反作用力场，特别是针对他们选择的化学反应，从而提高准确性，并将其应用于大规模反应分子动力学模拟。