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Inverse design of nanoporous crystalline reticular materials with deep generative models

Nature Machine Intelligence volume 3, pages 76–86 (2021)Cite this article

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Abstract

Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks in different topologies, with many having desirable properties for gas storage, separation, catalysis, biomedical applications and so on. The notable variety of building blocks makes reticular chemistry both promising and challenging for prospective materials design. Here we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder for the generative design of reticular materials. We demonstrate the automated design process with a class of metal–organic framework (MOF) structures and the goal of separating carbon dioxide from natural gas or flue gas. Our model shows high fidelity in capturing MOF structural features. We show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported.

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Fig. 1: Reticular framework identification and representation, exemplified with MOF structures from the CoRE MOF database26.
Fig. 2: Schematic of the automated reticular framework discovery platform empowered by the SmVAE.
Fig. 3: Illustration of the latent space of the jointly trained SmVAE using PCA analysis conditioned by MOF properties and exemplified sampling of the latent space.
Fig. 4: Reticular framework design and optimization using the SmVAE with natural gas separation (CO2 uptake) as the exemplified target.

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Data availability

Data for the training of the SmVAE including the augmented two million MOF set and the tabulated textural and gas-separation property data for the randomly selected MOF structures are available at https://github.com/zhenpengyao/Supramolecular_VAE/tree/master/data.

Code availability

Code for the SmVAE is available at https://doi.org/10.24433/CO.8185164.v1.

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Acknowledgements

Z.Y., N.S.B., B.J.B., S.G.H.K., O.K.F., R.Q.S. and A.A.-G. were supported as part of the Nanoporous Materials Genome Center by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362. Funding for T.B., S.P.C. and T.K.W. were provided by NSERC. Computations were made on the supercomputer ‘beluga’ from École de technologie supérieure, managed by Calcul Québec and Compute Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation (CFI), the ministère de l’Économie, de la science et de l’innovation du Québec (MESI) and the Fonds de recherche du Québec - Nature et technologies (FRQ-NT). This research was supported in part through the computational resources and staff contributions provided for the Quest high-performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. A.A.-G. is a Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow.

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Authors and Affiliations

  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Zhenpeng Yao, Benjamín Sánchez-Lengeling & Alán Aspuru-Guzik

  2. Department of Chemistry, University of Toronto, Toronto, Ontario, Canada

    Zhenpeng Yao, Sai Govind Hari Kumar & Alán Aspuru-Guzik

  3. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA

    N. Scott Bobbitt, Benjamin J. Bucior, Omar K. Farha & Randall Q. Snurr

  4. Department of Chemistry and Biomolecular Science, University of Ottawa, Ottawa, Ontario, Canada

    Sean P. Collins, Thomas Burns & Tom K. Woo

  5. Department of Chemistry, Northwestern University, Evanston, IL, USA

    Omar K. Farha

  6. Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

    Alán Aspuru-Guzik

  7. Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada

    Alán Aspuru-Guzik

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  1. Zhenpeng Yao
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Contributions

Z.Y. conceived the overall project. Z.Y. B.J.B. and R.Q.S. designed the reticular framework representation approach. N.S.B. and R.Q.S. conducted the MOF property determination calculations. Z.Y. and B.S.-L. developed the deep learning variational autoencoder. S.P.C., T.B. and T.K.W. did the charge calculations for the framework charges for property simulations. A.A.-G. led the project and provided the overall directions. All authors participated in preparing the manuscript.

Corresponding authors

Correspondence to Zhenpeng Yao, Randall Q. Snurr or Alán Aspuru-Guzik.

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Competing interests

O.K.F. and R.Q.S. have a financial interest in NuMat Technologies, a startup company that is seeking to commercialize MOFs.

Additional information

Peer review information Nature Machine Intelligence thanks Jihan Kim, Joshua Schrier and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–3, Figs. 1–12, structures and reference.

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Supplementary Data 1

Crystallographic information file: GMOF-1.cif.

Supplementary Data 2

Crystallographic information file: GMOF-2.cif.

Supplementary Data 3

Crystallographic information file: GMOF-3.cif.

Supplementary Data 4

Crystallographic information file: GMOF-4.cif.

Supplementary Data 5

Crystallographic information file: GMOF-5.cif.

Supplementary Data 6

Crystallographic information file: GMOF-4.cif.

Supplementary Data 7

Crystallographic information file: GMOF-4.cif.

Supplementary Data 8

Crystallographic information file: GMOF-4.cif.

Supplementary Data 9

Crystallographic information file: GMOF-4.cif.

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Yao, Z., Sánchez-Lengeling, B., Bobbitt, N.S. et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat Mach Intell 3, 76–86 (2021). https://doi.org/10.1038/s42256-020-00271-1

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