Development of a Neural Network Potential for Metal-Organic Frameworks

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A lot of progress has been made in recent years in the development of machine learning (ML) potentials for atomistic simulations. An important class of ML potentials employs artificial neural networks to construct the functional relation between the atomic configuration and the potential energy. To date, neural network potentials (NNP) have been reported for a wide range of materials. They are trained to data from electronic structure calculations and then allow to perform simulations of large systems with the efficiency of simple empirical potentials while maintaining the accuracy of the underlying reference method. In this project the applicability and accuracy of high-dimensional NNPs for organic-inorganic hybrid materials will be investigated, which are very challenging for conventional potentials. For this purpose metal-organic-frameworks (MOFs) will be used as a prototypical and technologically important class of hybrid materials. MOFs consist of metal-oxo clusters that are connected by organic linker molecules to form very stable porous three-dimensional crystalline materials. A particular focus will be on the validation of the NNP that should be applicable to a wide range of MOFs, with implications for the development of potentials for general hybrid systems containing organic and inorganic subsystems.