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Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.
Chemical Reviews ( IF 51.4 ) Pub Date : 2020-06-10 , DOI: 10.1021/acs.chemrev.0c00004
Kevin Maik Jablonka 1 , Daniele Ongari 1 , Seyed Mohamad Moosavi 1 , Berend Smit 1
Affiliation  

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.

中文翻译:


多孔材料中的大数据科学:材料基因组学和机器学习。



通过将金属节点与有机连接体相结合,我们有可能合成数百万种可能的金属有机框架(MOF)。我们拥有如此多的材料,这一事实开辟了许多令人兴奋的途径,但也带来了新的挑战。我们有太多的材料需要使用传统的强力方法进行处理。在这篇综述中,我们表明,拥有如此多的材料使我们能够使用大数据方法作为一种强大的技术来研究这些材料并发现复杂的相关性。综述的第一部分介绍了大数据科学的原理。我们展示了如何选择适当的训练集、用于在特征空间中表示这些材料的调查方法,并审查不同的学习架构以及评估和解释策略。在第二部分中,我们回顾了如何将机器学习的不同方法应用于多孔材料。我们特别讨论了在气体储存和分离领域的应用、这些材料的稳定性、它们的电子特性及其合成。鉴于科学界对机器学习的兴趣日益浓厚,我们预计该列表在未来几年将迅速扩大。
更新日期:2020-06-10
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