  The tremendous diversity of zeolite frameworks makes ab initio simulations of
their structure, stability, reactivity and, properties virtually impossible. To
enable large-scale reactive simulations of zeolites with ab initio quality, we
trained neural network potentials (NNP) with the SchNet architecture on a
structurally diverse DFT database. This database was iteratively extended by
active learning to cover the configuration space from low-density zeolites to
high-pressure silica polymorphs including low-energy equilibrium configurations
and high-energy transition states. The resulting reactive NNPs model
equilibrium structures, vibrational properties, and phase transitions at high
temperatures such as thermal zeolite collapse in excellent agreement with both
DFT and experiments. The novel NNPs allowed revision of a zeolite database
containing more than 330 thousand hypothetical zeolites previously generated
employing analytical force fields. NNP structure optimizations revealed more
than 20 thousand additional hypothetical frameworks in the thermodynamically
accessible range of zeolite synthesis. Additionally, the obtained zeolite
database provides vital input for future machine learning studies on the
structure, stability, reactivity and properties of hypothetical and existing
zeolites.
