Leonid Komissarov^1*, Toon Verstraelen^1*

1 Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium

* Corresponding authors emails: leonid.komissarov@ugent.be, toon.verstraelen@ugent.be

DOI10.24435/materialscloud:48-qs [version v1]

Publication date: Jul 07, 2021

How to cite this record

Leonid Komissarov, Toon Verstraelen, Zeo-1: A computational data set of zeolite structures, Materials Cloud Archive 2021.103 (2021), https://doi.org/10.24435/materialscloud:48-qs

Description

Fast, empirical potentials are gaining increased popularity in the computational fields of materials science, physics and chemistry. With it, there is a rising demand for high-quality reference data for the training and validation of such models. In contrast to research that is mainly focused on small organic molecules, this work presents a data set of geometry-optimized bulk phase zeolite structures. Covering a majority of framework types from the Database of Zeolite Structures, this set includes over thirty thousand geometries. Calculated properties include system energies, nuclear gradients and stress tensors at each point, making the data suitable for model development, validation or referencing applications focused on periodic silica systems.

Materials Cloud sections using this data

Files

File name	Size	Description
zeo-1.tar.gz MD5md5:d772ff9c9b0ef5d0f05e19b3c24e13d3	157.2 MiB	Data repository, individual hash sums and helper scripts
README.md MD5md5:f9bc35aee8e77b5b3d4b4e3b631d0033	827 Bytes	Brief description of the repository contents

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

zeolite silica DFT bulk phase density-functional theory machine learning Horizon Europe H2020 Marie Curie Fellowship