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.
By combining metalnodes with orn class="Chemical">ganic 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.
Authors: Manfred K Warmuth; Jun Liao; Gunnar Rätsch; Michael Mathieson; Santosh Putta; Christian Lemmen Journal: J Chem Inf Comput Sci Date: 2003 Mar-Apr
Authors: George S Fanourgakis; Konstantinos Gkagkas; Emmanuel Tylianakis; George E Froudakis Journal: J Am Chem Soc Date: 2020-02-12 Impact factor: 15.419
Authors: Li-Chiang Lin; Adam H Berger; Richard L Martin; Jihan Kim; Joseph A Swisher; Kuldeep Jariwala; Chris H Rycroft; Abhoyjit S Bhown; Michael W Deem; Maciej Haranczyk; Berend Smit Journal: Nat Mater Date: 2012-05-27 Impact factor: 43.841
Authors: Andreas K Maier; Christopher Syben; Bernhard Stimpel; Tobias Würfl; Mathis Hoffmann; Frank Schebesch; Weilin Fu; Leonid Mill; Lasse Kling; Silke Christiansen Journal: Nat Mach Intell Date: 2019-08-09
Authors: Peter G Boyd; Arunraj Chidambaram; Enrique García-Díez; Christopher P Ireland; Thomas D Daff; Richard Bounds; Andrzej Gładysiak; Pascal Schouwink; Seyed Mohamad Moosavi; M Mercedes Maroto-Valer; Jeffrey A Reimer; Jorge A R Navarro; Tom K Woo; Susana Garcia; Kyriakos C Stylianou; Berend Smit Journal: Nature Date: 2019-12-11 Impact factor: 49.962
Authors: Yangzesheng Sun; Robert F DeJaco; Zhao Li; Dai Tang; Stephan Glante; David S Sholl; Coray M Colina; Randall Q Snurr; Matthias Thommes; Martin Hartmann; J Ilja Siepmann Journal: Sci Adv Date: 2021-07-21 Impact factor: 14.136