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D&D4 - Nanoporous Materials

Recent developments in chemistry and materials science have made it possible to make millions of novel nanoporous materials. We can generate a spectrum of distinct materials simply by changing the building blocks in the synthesis route of metal-organic frameworks (MOFs) and related advanced nanoporous materials. Crucially, these materials have important applications related to energy, such as in carbon capture, and other gas separations, gas storage, and catalysis. Indeed, their unique chemical tunability offers the potential to decisively change the field: instead of relying on traditional methods of trial and error, we now have the chemistry at hand to tailor-make materials.


The promise of finding just the right material among millions of hypothetical structures, however, seems remote because of practical limitations. The number of materials we can synthesize, characterize, and fully test is only a tiny fraction of all possibilities. These experimental limitations make it difficult, if not impossible, to systematically search the entire chemical space of these materials. To take full advantage of these materials, project D&D4 aims to develop computational techniques to rapidly screen large number of possible materials and to obtain fundamental molecular insights into what the ideal material looks like for applications of these materials ranging from gas separation, (photo)catalysis, and sensing.


The project is led by Berend Smit.

Group Leaders

Berend Smit
Project leader
EPFL, Sion
Jürg Hutter
Group leader
UZH, Zürich
Alfredo Pasquarello
Group leader
EPFL, Lausanne
Ivano Tavernelli
Group leader
IBM, Rüschlikon
Marco Ranocchiari
Group leader
PSI, Villigen PSI
Emiliana Fabbri
Group leader
PSI, Villigen PSI
View people involved

Left: MOFs are made for big-data science: The neurons of artificial neural networks, representing the big-data approach to chemistry, become one with the framework of a metal-organic framework (figure by Alexander Tokarev, from [1]).

Right: one out of a million; each data point represents a metal-organic framework used to discover novel materials for carbon capture (figure from [2]).

[1] K. M. Jablonka, D. Ongari, S. M. Moosavi, and B. Smit, Big-Data Science in Porous Materials: Materials Genomics and Machine Learning, Chemical Reviews 120, 8066 (2020). [Open Access URL]

[2] S. M. Moosavi, K. M. Jablonka, and B. Smit, The Role of Machine Learning in the Understanding and Design of Materials, Journal of the American Chemical Society 142, 20273 (2020). [Open Access URL]


Related publications (until January 2022)