Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

Machine learning potentials (MLPs) capable of accurately
describing
complex ab initio potential energy surfaces (PESs)
have revolutionized the field of multiscale atomistic modeling. In
this work, using an extensive density functional theory (DFT) data
set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies
(350,000 unique DFT calculations) found in the International Zeolite
Association (IZA) database, we have trained a DeePMD-kit MLP to model
the dynamics of silica frameworks. The performance of our model is
evaluated by calculating various properties that probe the accuracy
of the energy and force predictions. This MLP demonstrates impressive
agreement with DFT for predicting zeolite structural properties, energy–volume
trends, and phonon density of states. Furthermore, our model achieves
reasonable predictions for stress–strain relationships without
including DFT stress data during training. These results highlight
the ability of MLPs to capture the flexibility of zeolite frameworks
and motivate further MLP development for nanoporous materials with
near-ab initio accuracy.