Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials
5 affiliations
- 1Faculty of Chemistry, University of Warsaw Warsaw 02-093 Poland
- 2Division of Physical Chemistry, Ruđer Bošković Institute Zagreb 10000 Croatia
- 3School of Metallurgy and Materials, University of Birmingham Elms Road, Edgbaston Birmingham B15 2TT UK
- 4School of Chemistry and Chemical Engineering, University of Southampton Southampton SO17 1BJ UK
- 5Symmetric Group 71-75 Shelton Street, Covent Garden London WC2H 9JQ UK
Abstract
Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal–organic frameworks (MOFs). In this article we employ CSP to perform high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm2). As the most polymorphic member of the zeolitic imidazolate framework (ZIF) family, ZnIm2 has at least 24 reported structural and topological forms, and new polymorphs are still being regularly discovered. With the aid of custom-trained machine-learned interatomic potentials (MLIPs) we have performed a high-throughput sampling of over 3 million randomly generated crystal packing arrangements and identified 9609 energy minima characterized by 1484 network topologies, including 855 topologies that have not been reported before. Of the experimentally reported structures of ZnIm2 falling within the search boundaries, all but one were ultimately matched with the predicted structures, demonstrating the power of the CSP method in sampling experimentally relevant ZIF structures. Finally, through a combination of topological analysis and density and porosity considerations, we have identified a set of structures representing promising targets for future experimental screening, as well as demonstrated how structures of mechanochemically synthesized MOFs could be identified via matching experimental powder diffraction patterns with simulated patterns from the predicted structures.
Related
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BibTeX
@article{xu2026hierarchical,
title = {{Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials}},
author = {Yizhi Xu and Jordan Dorrell and Katarina Lisac and Ivana Brekalo and James P. Darby and Andrew J. Morris and Mihails Arhangelskis},
journal = {Chemical Science},
year = {2026},
month = sep,
doi = {10.1039/d6sc04307k},
url = {https://doi.org/10.1039/d6sc04307k},
note = {CC BY 3.0},
}
RIS
TY - JOUR
TI - Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials
AU - Yizhi Xu
AU - Jordan Dorrell
AU - Katarina Lisac
AU - Ivana Brekalo
AU - James P. Darby
AU - Andrew J. Morris
AU - Mihails Arhangelskis
PY - 2026
DA - 2026/09/04
JO - Chemical Science
DO - 10.1039/d6sc04307k
UR - https://doi.org/10.1039/d6sc04307k
AB - Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal–organic frameworks (MOFs). In this article we employ CSP to perform high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm2). As the most polymorphic member of the zeolitic imidazolate framework (ZIF) family, ZnIm2 has at least 24 reported structural and topological forms, and new polymorphs are still being regularly discovered. With the aid of custom-trained machine-learned interatomic potentials (MLIPs) we have performed a high-throughput sampling of over 3 million randomly generated crystal packing arrangements and identified 9609 energy minima characterized by 1484 network topologies, including 855 topologies that have not been reported before. Of the experimentally reported structures of ZnIm2 falling within the search boundaries, all but one were ultimately matched with the predicted structures, demonstrating the power of the CSP method in sampling experimentally relevant ZIF structures. Finally, through a combination of topological analysis and density and porosity considerations, we have identified a set of structures representing promising targets for future experimental screening, as well as demonstrated how structures of mechanochemically synthesized MOFs could be identified via matching experimental powder diffraction patterns with simulated patterns from the predicted structures.
SN - 2041-6520
SN - 2041-6539
ER -