Virtual Screening of Materials for Carbon Capture

Jain, Arvind; Babarao, Ravichandar; Thornton, Aaron W.

doi:10.1002/9781119091219.ch5

Cited by 3 publications

(2 citation statements)

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“…Gas separations are another important application of porous materials. , Given the importance of reducing CO 2 emission, , a lot of research has focused on finding materials for carbon capture, both experimentally − as well as by means of computational screening studies. ,, Gas separations require the (mixture) adsorption isotherms of the gases one would like to separate. In most screening studies, the mixture isotherms are predicted from the pure component isotherms using ideal adsorbed solution theory.…”

Section: Applications Of Supervised Machine Learningmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Applications Of Supervised Machine Learningmentioning

confidence: 99%

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

et al. 2020

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“…451,452 Given the importance of reducing CO 2 emission, 453,454 a lot of research has focused on find-ing materials for carbon capture, both experimentally [455][456][457][458] as well as by means computational screening studies. 15,459,460 Gas separations require the (mixture) adsorption isotherms of the gases one would like to separate. In most screening studies, the mixture isotherms are predicted from the pure component isotherms using ideal adsorbed solution theory.…”

Section: Gas Storage and Separationmentioning

confidence: 99%

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

Jablonka,

Ongari,

Moosavi

et al. 2020

Preprint

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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 material 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, 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.

show abstract