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Accelerated discovery of porous materials for carbon capture by machine learning: A review

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Abstract

In the past decades, greenhouse gases (e.g., anthropogenic CO2 and CH4) have raised significant concerns due to the foreseeable dire consequences in climate change. Capturing them via adsorption using porous materials has drawn much attention due to their low synthesis and regeneration cost and high capacity. Recently, the flourishing machine learning (ML) has been introduced to various fields of materials science, which also has shown great potential in accelerating the materials discovery for carbon capture. In this article, we first describe the general workflow of applying ML to tackle materials problems. Then we systematically summarize the recent research progress in the application of ML for development of porous carbon and metal–organic frameworks for carbon capture. Finally, we discuss the existing challenges, possible solutions, and research directions. This article will inspire exploration of new frontiers in the carbon capture by development of ML in porous materials research in the future.

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Figure 1
Figure 2

Reproduced with permission from Reference 33. © 2019 American Chemical Society. (d) Architecture of the convolutional neural networks (CNNs). Reproduced with permission from Reference 35. © 2020 Wiley. (e) Relative importance of textural properties (TP), chemical composition (CC), and pressure (P) at different pressure range in the CO2 uptake in the porous carbon. Reproduced with permission from Reference 18. © 2020 Elsevier.

Figure 3

Reproduced with permission from Reference 46. © 2021 American Chemical Society. (b) Schematic of the automated reticular framework discovery platform empowered by the SmVAE. Reproduced with permission from Reference 47. © 2021 Springer Nature.

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Data sharing is not applicable to this article as no data sets were generated or analyzed during the current study.

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Acknowledgments

This work has been financially supported by the University of Missouri, National Science Foundation (Award Nos. 1825352 and 1933861), and National Natural Science Foundation of China (Award Nos. 61875256 and 61925506).

Conflict of interest

The authors declare no competing financial interests.

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Authors and Affiliations

  1. School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang, China

    Chi Zhang, Chen Xie, Hongxing Dong & Long Zhang

  2. Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, USA

    Chi Zhang, Yunchao Xie & Jian Lin

  3. Key Laboratory of Materials for High-Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, China

    Hongxing Dong & Long Zhang

Authors
  1. Chi Zhang
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  2. Yunchao Xie
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Correspondence to Yunchao Xie or Jian Lin.

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Zhang, C., Xie, Y., Xie, C. et al. Accelerated discovery of porous materials for carbon capture by machine learning: A review. MRS Bulletin 47, 432–439 (2022). https://doi.org/10.1557/s43577-022-00317-2

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