Materials Science

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 (PES) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) dataset (denoted as Si-ZEO22) consisting of 187 unique silica topologies 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 motivates further MLP development for nanoporous materials with near-ab initio accuracy.


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Supplementary material

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DFT/DP predictions for all zeolite topologies
Information on the dataset size, test set prediction errors, optimized lattice constants, and mechanical property calculation results for each topology included in the Si-ZEO22 dataset.
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Training parameters
DeePMD-kit training parameter .json file used to train the final model.

Supplementary weblinks

Si-ZEO22 DFT Dataset
Diverse DFT dataset generated from NVT and NPT molecular dynamics simulations across different temperatures and pressures to be used for training machine learning interatomic potentials. Includes DFT energies and forces calculated in VASP using the RPBE-D3BJ functional (400 eV energy cutoff).