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

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    Big-Data Science in Porous Materials: Materials Genomics and Machine LearningCrossref
    Nice explaination about big data science.
    Average rating:
        Rated 4 of 5.
    Level of importance:
        Rated 4 of 5.
    Level of validity:
        Rated 4 of 5.
    Level of completeness:
        Rated 4 of 5.
    Level of comprehensibility:
        Rated 3 of 5.
    Competing interests:
    None

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    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). 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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      Review information

      10.14293/S2199-1006.1.SOR-CHEM.A7453404.v1.RKKOAK

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      Review text

      Correlation among bigdata science, Materials Genomics and Machine Learning need to explained in more details to justify the title of topic.

      Literature review is also required in more details to give the proper justification towards the scope of this work .

       

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