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

By Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit. Chemical Reviews, 2020

469 citations recorded by the source corpus.

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. 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 show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning 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. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

Subfields: Inorganic Chemistry, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/8bba999de25bfb288b3f7f88e1d907aab02638b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Inorganic Chemistry

1. Kevin Maik Jablonka
2. Seyed Mohamad Moosavi
3. Berend Smit

In Materials Chemistry

1. Kevin Maik Jablonka
2. Seyed Mohamad Moosavi
3. Berend Smit

Citations in the corpus

• The Role of Machine Learning in the Understanding and Design of Materials (2020, methodology): “17,42,43 Moosavi et al. 41 used an approach that closely follows the MOF chemistry, in which a MOF is described by four sets of descriptors, for the metal nodes, linkers, functional groups, and pore geometry.”