Multi-fidelity, active learning strategies for exciton transfer among adsorbed molecules

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New materials for photochemical applications are essential, e.g., for the further development of renewable energy devices. The development of such material is nowadays tackled by experiments and by computer-driven molecular simulations. Ideally, the full design process including material screening and optimizations could be done in-silico. This, however, requires time-efficient, high-accuracy and easy-to-use software for the analysis of photochemical properties of molecular aggregates or more precisely their excitonic properties. The long-term goal of this project is to develop methods that will make such an analysis feasible, noting that current molecular simulations by means of quantum mechanics / molecular mechanics (QM/MM) methods are prohibitively expensive. A promising tool to overcome the computational challenges is the use of cheap to evaluate machine learning models, replacing expensive quantum chemical calculations in the simulation pipeline. However, the practical long-term success of this tool can only be guaranteed, if such machine learning models indeed achieve high accuracy predictions at moderate costs for the generation of the quantum chemical training data and can be constructed in a (semi-)automatic way. In this project, we develop a multi-fidelity, active learning approach for exciton transfer within molecular aggregates. Multi-fidelity machine learning promises to strongly reduce the number of required highly accurate and thereby computationally expensive training samples by using hierarchies of training data obtained at different quantum chemical theory levels, basis set sizes, etc. Further technical improvements will be achieved in the automatic selection of best possible training calculations (active learning) and the constructions of bi-molecular models, i.e. machine learning models for properties that depend on two molecules. The overall approach is applied for the analysis of a light-harvesting material based on a molecular aggregate. As an example for such an aggregate, we focus on porphyrin molecules adsorbed on clay surfaces which experimentally have shown to posses interesting light-harvesting properties. While this model application will certainly gain from our novel contributions, our interest is to further share our expertise and tools on multi-fidelity molecular machine learning and on QM/MM simulations within the priority program and beyond.