Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials

Abstract: Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal-organic frameworks (MOFs). In this article we demonstrate the high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm2), a highly polymorphic member of the zeolitic imidazolate (ZIF) family, with at least 24 reported structural and topological forms, with new polymorphs 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 9626 energy minima characterized by 1493 network topologies, including 864 topologies that have not been reported before. Comparisons with previously reported structures revealed 13 topological matches to the experimentally-observed structures of ZnIm2, demonstrating the power of the CSP method in sampling experimentally-relevant ZIF structures. Finally, through a combination of topological analysis, density and porosity considerations, we have identified a set of structures representing promising targets for future experimental screening. Finally, we demonstrate how CSP can be used to assist in the identification of the products of the mechanochemical synthesis.