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Accurate large-scale simulations of siliceous zeolites by neural network potentials

The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computationally feasible protocol for identification of hypothetical zeolites that could be targeted experimentally is a great challenge. To tackle the challenge, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse database of density functional theory (DFT) data. This database was iteratively extended by active learning to cover not only low-energy equilibrium configurations but also high-energy transition states. We demonstrate that the resulting reactive NNPs retain the accuracy of the DFT reference for thermodynamic stabilities, vibrational properties, and reactive and non-reactive phase transformations. The novel NNPs outperforms specialized, analytical force fields for silica, such as ReaxFF, by order(s) of magnitude in accuracy, while speeding up the calculations in comparison to DFT by at least three orders of magnitude. As a showcase, we screened an existing zeolite database containing 330 thousand structures and revealed more than 20 thousand additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Hence, our NNPs are expected to be essential for future high-throughput studies on the structure and reactivity of hypothetical and existing zeolites.