  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.
