Global optimization of reactive force fields

点击翻译按钮获取中文摘要

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.