Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

Photochemical preparation of silver nanoparticles supported on zeolite crystals

International audienceA facile and rapid photochemical method to prepare supported silver nanoparticles (Ag-NPs) in a suspension of faujasite-type (FAU) zeolite nanocrystals is described. Silver cations are introduced by ion exchange in the zeolite and subsequently irradiated with a Xe-Hg lamp (200 W) in the presence of a photoactive reducing agent (2-hydroxy-2-methylpropiophenone). UV-Vis characterization indicates that irradiation time and intensity (I0) influence significantly the amount of silver cations reduced. The full reduction of silver cations takes place after irradiation (I0 = 100 % for 60 s), and a plasmon band of Ag-NPs appears at 380 nm. Transmission electron microscopy (TEM) combined with theoretical calculation of the plasmon absorbance band using Mie Theory shows that the Ag-NPs, stabilized in the micropores and on the external surface of the FAU zeolite nanocrystals, have almost a spheroidal shape with diameters of 0.75 and 1.12 nm, respectively. Ag-NPs, with a homogeneous distribution of size and morphology, embedded in a suspension of FAU zeolite are very stable (ca. 8 months), even without any stabilizers or capping agents