Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis1,2,3. Interzeolite transformations enable selective crystallization4,5,6,7, but are often too complex to be designed by comparing crystal structures. Here, computational and theoretical tools are combined to both exhaustively data mine polymorphic transformations reported in the literature and analyse and explain interzeolite relations. It was found that crystallographic building units are weak predictors of topology interconversion and insufficient to explain intergrowth. By introducing a supercell-invariant metric that compares crystal structures using graph theory, we show that diffusionless (topotactic and reconstructive) transformations occur only between graph-similar pairs. Furthermore, all the known instances of intergrowth occur between either structurally similar or graph similar frameworks. We identify promising pairs to realize diffusionless transformations and intergrowth, with hundreds of low-distance pairs identified among known zeolites, and thousands of hypothetical frameworks connected to known zeolite counterparts. The theory may enable the understanding and control of zeolite polymorphism.
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The zeolite datasets analysed during the current study are available online at the IZA Database (www.iza-structure.org/databases/) and at the Predicted Crystallography Open Database (www.crystallography.net/pcod/). Complete references for the literature analysed in this work, pairwise distances between known zeolites and isomorphism between hypothetical and known zeolites are available in the Supplementary Information.
The code used to download journal articles for large-scale text mining is available at www.github.com/olivettigroup/article-downloader. The code used to variationally compare crystal structures as supercell graphs is available from the corresponding author on request.
Davis, M. E. Ordered porous materials for emerging applications. Nature 417, 813–821 (2002).
Maldonado, M., Oleksiak, M. D., Chinta, S. & Rimer, J. D. Controlling crystal polymorphism in organic-free synthesis of Na-zeolites. J. Am. Chem. Soc. 135, 2641–2652 (2013).
Gallego, E. M. et al. ‘Ab initio’ synthesis of zeolites for preestablished catalytic reactions. Science 355, 1051–1054 (2017).
Honda, K. et al. Role of structural similarity between starting zeolite and product zeolite in the interzeolite conversion process. J. Nanosci. Nanotechnol. 13, 3020–3026 (2013).
Marler, B. & Gies, H. Hydrous layer silicates as precursors for zeolites obtained through topotactic condensation: a review. Eur. J. Mineral. 24, 405–428 (2012).
Eliášová, P. et al. The ADOR mechanism for the synthesis of new zeolites. Chem. Soc. Rev. 44, 7177–7206 (2015).
Li, C., Moliner, M. & Corma, A. Building zeolites from precrystallized units: nanoscale architecture. Angew. Chem. Int. Ed. 57, 15330–15353 (2018).
Goel, S., Zones, S. I. & Iglesia, E. Synthesis of zeolites via interzeolite transformations without organic structure-directing agents. Chem. Mater. 27, 2056–2066 (2015).
Baerlocher, C., McCusker, L. B. & Olson, D. H. Atlas of Zeolite Framework Types 6th edn (Elsevier, 2007).
Xie, B. et al. Organotemplate-free and fast route for synthesizing beta zeolite. Chem. Mater. 20, 4533–4535 (2008).
Iyoki, K., Itabashi, K. & Okubo, T. Progress in seed-assisted synthesis of zeolites without using organic structure-directing agents. Microporous Mesoporous Mater. 189, 22–30 (2014).
Itabashi, K., Kamimura, Y., Iyoki, K., Shimojima, A. & Okubo, T. A working hypothesis for broadening framework types of zeolites in seed-assisted synthesis without organic structure-directing agent. J. Am. Chem. Soc. 134, 11542–11549 (2012).
Verheyen, E. et al. Design of zeolite by inverse sigma transformation. Nat. Mater. 11, 1059–1064 (2012).
Zhao, Z. et al. Insights into the topotactic conversion process from layered silicate RUB-36 to FER-type zeolite by layer reassembly. Chem. Mater. 25, 840–847 (2013).
Van Tendeloo, L., Gobechiya, E., Breynaert, E., Martens, J. A. & Kirschhock, C. E. A. Alkaline cations directing the transformation of FAU zeolites into five different framework types. Chem. Commun. 49, 11737–11739 (2013).
O’Keeffe, M. & Hyde, S. T. The asymptotic behavior of coordination sequences for the 4-connected nets of zeolites and related structures. Z. Kristallogr. 211, 73–78 (1996).
Foster, M. D. et al. Chemically feasible hypothetical crystalline networks. Nat. Mater. 3, 234–238 (2004).
Treacy, M., Rivin, I., Balkovsky, E., Randall, K. & Foster, M. Enumeration of periodic tetrahedral frameworks. II. Polynodal graphs. Microporous Mesoporous Mater. 74, 121–132 (2004).
Witman, M. et al. Cutting materials in half: a graph theory approach for generating crystal surfaces and its prediction of 2D zeolites. ACS Cent. Sci. 4, 235–245 (2018).
Blatov, V. A. Topological relations between three-dimensional periodic nets. I. UNINODAL nets. Acta Crystallogr. A 63, 329–343 (2007).
Porter, D. A., Easterling, K. E. & Sherif, M. Phase Transformations in Metals and Alloys. 3rd edn (CRC Press, 2009).
Alberti, A., Cruciani, G. & Martucci, A. Reconstructive phase transitions induced by temperature in gmelinite-Na zeolite. Am. Mineral. 102, 1727–1735 (2017).
Dusselier, M., Kang, J. H., Xie, D. & Davis, M. E. CIT-9: a fault-free gmelinite zeolite. Angew. Chem. Int. Ed. 56, 13475–13478 (2017).
Schieber, T. A. et al. Quantification of network structural dissimilarities. Nat. Commun. 8, 13928 (2017).
Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87, 184115 (2013).
Jordá, J. L. et al. Synthesis of a novel zeolite through a pressure-induced reconstructive phase transition process. Angew. Chem. Int. Ed. 52, 10458–10462 (2013).
Deem, M. W., Pophale, R., Cheeseman, P. A. & Earl, D. J. Computational discovery of new zeolite-like materials. J. Phys. Chem. C 113, 21353–21360 (2009).
Keller, E. B., Meier, W. M. & Kirchner, R. M. Synthesis, structures of AlPO4-C and AlPO4-D, and their topotactic transformation. Solid State Ion. 43, 93–102 (1990).
Alberti, A. & Martucci, A. Reconstructive phase transitions in microporous materials: rules and factors affecting them. Microporous Mesoporous Mater. 141, 192–198 (2011).
Anderson, M. W. et al. Predicting crystal growth via a unified kinetic three-dimensional partition model. Nature 544, 456–459 (2017).
Kim, E. et al. Machine-learned and codified synthesis parameters of oxide materials. Sci. Data 4, 170127 (2017).
Jensen, Z. et al. A machine learning approach to zeolite synthesis enabled by automatic literature data extraction. ACS Cent. Sci. 5, 892–899 (2019).
Baerlocher, Ch. & McCusker, L. B. Database of Zeolite Structures (Structure Commission of the International Zeolite Association, 2019); www.iza-structure.org/databases/
Schröder, K. P. et al. Bridging hydrodyl groups in zeolitic catalysts: a computer simulation of their structure, vibrational properties and acidity in protonated faujasites (HY zeolites). Chem. Phys. Lett. 188, 320–325 (1992).
Pophale, R., Cheeseman, P. A. & Deem, M. W. A database of new zeolite-like materials. Phys. Chem. Chem. Phys. 13, 12407–12412 (2011).
Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).
Cordella, L. P., Foggia, P., Sansone, C. & Vento, M. A. (Sub)graph isomorphism algorithm for matching large graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1367–1372 (2004).
Hagberg, A. A., Schult, D. A. & Swart, P. J. in Proc. 7th Python in Science Conference (eds Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (SciPy, 2008).
Koda, D. S., Bechstedt, F., Marques, M. & Teles, L. K. Coincidence lattices of 2D crystals: heterostructure predictions and applications. J. Phys. Chem. C 120, 10895–10908 (2016).
Jäger, M. O. J., Morooka, E. V., Federici Canova, F., Himanen, L. & Foster, A. S. Machine learning hydrogen adsorption on nanoclusters through structural descriptors. npj Comput. Mater. 4, 37 (2018).
De, S., Bartók, A. P., Csányi, G. & Ceriotti, M. Comparing molecules and solids across structural and alchemical space. Phys. Chem. Chem. Phys. 18, 13754–13769 (2016).
D.S.-K. acknowledges the MIT Nicole and Ingo Wender Fellowship, the MIT Robert Rose Presidential Fellowship and the MIT Energy Initiative (MITEI) Storage Seed Fund for financial support. R.G.-B. thanks MIT DMSE, Toyota Faculty Chair and MITEI for support. The work of E.O. and Z.J. was partially funded by National Science Foundation Award no. 1534340, DMREF, the MIT-Sensetime Alliance on Artificial Intelligence, and the Office of Naval Research (ONR) under Contract no. N00014-16-1-2432. D.S.-K. and R.G.-B. thank A. Corma, M. Moliner and Y. Román-Leshkov for fruitful discussions.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices A–F, Supplementary Figs. 1–11, Tables 1–3 and refs. 1–262.
Normalized SOAP distance and D-measure for each of the 29,890 pairs of known zeolites. The csv file is sorted alphabetically by the zeolite IZA codes. The elements mij(A,B) (equation (D10)) of the transformation matrices M(A) and M(B) that minimize the graph distance between the frameworks (equation (D14)) are also given.
Hypothetical zeolites from PCOD with energy above quartz and their isomorphic IZA known zeolites. Only hypothetical zeolites with at least one isomorphic counterpart are shown in the table.