Machine learning of hierarchical ultrafast molecular forcefields (HUMF)

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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.