A New Kind of Atlas of Zeolite Building Blocks

Authors: Benjamin A. Helfrecht1*, Scott M. Auerbach4, Giovanni Pireddu3, Rocio Semino2, Michele Ceriotti1*

  1. Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
  2. Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland and Institut Charles Gerhardt Montpellier UMR 5253 CNRS, Université de Montpellier, Place E. Bataillon, 34095 Montpellier Cedex 05, France
  3. Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland and Dipartimento di Chimica e Farmacia, Unversità degli Studi di Sassari, Via Vienna 2, 01700 Sassari, Italy
  4. Department of Chemistry and Department of Chemical Engineering, University of Massachusetts, Amherst, Amherst, Massachusetts 01003, USA
  • Corresponding authors emails: benjamin.helfrecht@epfl.ch, michele.ceriotti@epfl.ch

DOI10.24435/materialscloud:2019.0079/v1 (version v1, submitted on 06 November 2019)

How to cite this entry

Benjamin A. Helfrecht, Scott M. Auerbach, Giovanni Pireddu, Rocio Semino, Michele Ceriotti, A New Kind of Atlas of Zeolite Building Blocks, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0079/v1.

Description

We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether a more general representation of the atomic structure, furnished by the smooth overlap of atomic position (SOAP) method, is required to capture accurately structure–property relations. We assessed the quality of each descriptor by machine-learning the molar energy and volume for each hypothetical framework in the dataset. We have found that a SOAP representation with a cutoff length of 6 Å, which goes beyond near-neighbor tetrahedra, best describes the structural diversity in the Deem database by capturing relevant interatomic correlations. Kernel principal component analysis shows that SOAP maintains its superior performance even when reducing its dimensionality to those of the classical descriptors and that the first three kernel principal components capture the main variability in the dataset, allowing a 3D point cloud visualization of local environments in the Deem database. This “cloud atlas” of local environments was found to show good correlations with the contribution of a given motif to the density and stability of its parent framework. Local volume and energy maps constructed from the SOAP/machine learning analyses provide new images of zeolites that reveal smooth variations of local volumes and energies across a given framework and correlations between the contributions to volume and energy associated with each atom-centered environment.

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Files

File name Size Description
archive.tar.gz
MD5MD5: 3c1ecfacaae2b13830b408133d9f1dcb
882.8 MiB Silicon-centered environment descriptors and properties used in the corresponding study. The structures used to generate the descriptors are a subset of those found in the DEEM SLC PCOD database (http://www.hypotheticalzeolites.net/DATABASE/DEEM/)
README.txt
MD5MD5: 935d4d59674d16d8c64bbe96ffa70a6b
3.5 KiB Description of file contents

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

External references

Journal reference (Paper for which the dataset was generated)
B. A. Helfrecht, R. Semino, G. Pireddu, S. M. Auerbach, M. Ceriotti, J. Chem. Phys. 151, 154112 (2019). doi:10.1063/1.5119751
Journal reference (Paper in which the Deem database was originally created, described, and used)
R. Pophale, P. A. Cheeseman, M. W. Deem, Phys. Chem. Chem. Phys. 13, 12407-12412 (2011) doi:10.1039/c0cp02255a

Keywords

MARVEL Machine Learning ERC SCCER Zeolites

Version history

06 November 2019 [This version]