Zeo-1, a computational data set of zeolite structures.

Area of Science:

• Computational Materials Science
• Solid-State Chemistry
• Crystallography

Background:

• Empirical potentials are increasingly vital for computational materials science, physics, and chemistry.
• High-quality reference data is essential for training and validating these fast computational models.
• Existing data often focuses on small organic molecules, leaving a gap for periodic systems.

Purpose of the Study:

• To present a comprehensive dataset of geometry-optimized bulk phase zeolite structures.
• To support the development and validation of empirical potentials for periodic silica systems.
• To provide reference data for computational modeling of zeolites.

Main Methods:

• Generation of over thirty thousand geometry-optimized bulk phase zeolite structures.
• Inclusion of a majority of framework types from the Database of Zeolite Structures.
• Calculation of system energies, nuclear gradients, and stress tensors for each structure.

Main Results:

• A large-scale dataset of over 30,000 optimized zeolite structures is now available.
• The dataset covers a wide range of zeolite framework types.
• Calculated properties include energies, gradients, and stress tensors, suitable for various computational applications.

Conclusions:

• The presented dataset addresses the need for high-quality reference data for periodic silica systems.
• This resource facilitates advancements in fast, empirical potential development and validation.
• The data is valuable for research in materials science, physics, and chemistry involving zeolites.