Machine learning of hierarchical ultrafast molecular forcefields (HUMF)

在本质上无限且不断增长的应用中，复杂的动态环境决定了整体性能。在溶液中和多相催化中的化学反应强烈且非平凡地依赖于分子环境，特别是与外部刺激（例如光）的相互作用。原则上，量子力学提供了现今可行的近似，主要是基于密度泛函理论，对于许多这些问题，但可达到的时间尺度在量子化学计算是如此之短，只有模型系统可以充分解决。分子力学方法，特别是分子动力学，允许在长达100，000倍的时间尺度上处理系统，但通常缺乏系统的适当表示。十年来，人们一直在努力使用机器学习方法来超越分子力学模拟中使用的力场的手动参数化，这已经证明可以用机器学习来参数化精确的力场，但这些方法的数值工作仍然与快速量子方法相当，而不是标准的分子动力学方法。在这里，我们将开发一种新的方法，该系统的分子表示，在其化学计量组成的分子构象的治疗。这使得分子力场的分层机器学习成为可能，其中计算效率高的力场由复杂的ML协议参数化，在实际模拟开始之前只需要评估一次。这两个分量，即力场的函数形式及其参数，都可以基于高度精确的量子力学数据来学习。在这个项目中，我们将证明这种方法的可行性，在溶液中的小有机分子的模型反应和多相催化。与其他项目合作，力场将应用于金属有机框架的生长，分子有机材料的激发态化学和电池应用。力场将在标准分子动力学代码中实现，并提供给社区。该项目将参与整个社区的努力，以生成和管理分子力场的培训数据。

In essentially in unlimited and ever-growing number of applications, the complex dynamic environment determines the overall properties. Chemical reactions in solution and in heterogeneous catalysis depend strongly and non-trivially on the molecular environment, in particular in interaction with external stimuli, such as light. In principle quantum mechanics offers nowadays workable approximations, mostly based on density functional theory, for many of these problems, but the timescales reachable in-initio quantum chemistry calculations are so short that only model systems can be adequately addressed. Molecular mechanics methods, in particular molecular dynamics, permit the treatment of systems on timescales which are up to 100,000 times longer but often lack adequate representations of the system. There has been a decade-long effort to use machine learning method to go beyond the manual parameterization of the force fields used in molecular mechanics simulations, which has demonstrated that accurate forcefields can be parameterized with machine learning, but the numerical effort of these methods is still comparable to fast quantum methods, rather than to standard molecular dynamics methods. Here we will develop a novel approach that decouples the molecular representation of the system, in terms of its stochiometric composition from the treatment of the molecular conformation. This enables hierarchical machine-learning of molecular force fields, where a computationally efficient forcefield is parameterized by a complex ML-protocol, which needs to be evaluated only once before the actual simulation starts. Both components, i.e. the functional form of the forcefield and its parameters can be learned based on highly accurate quantum mechanical data. In this project we will demonstrate the viability of this approach for model reactions of small organic molecules in solution and for heterogeneous catalysis. In cooperation with other projects, the force fields will be applied to the growth of metal organic frameworks, excited state chemistry in molecular organic materials and battery applications. The force fields will be implemented in standard molecular dynamics codes and made available to the community. The project will participate in a community-wide effort to generate and curate training data for molecular force fields.