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Volume 6 Issue 2, February 2023

Machine bridges

Artificial intelligence and machine learning are becoming increasingly important in many aspects of twenty-first century life. This Focus issue provides an overview of how machine learning can be applied to facilitate rapid advances in catalyst discovery. The cover image comes from a Review by Hongliang Xin and colleagues, which discusses strategies to utilize machine learning to bridge the complexity gap that currently exists between real and computed catalytic systems.

See Mou et al

Image: Xue Han and Tianyou Mou. Cover design: Marina Spence.


  • Computational chemistry has become an increasingly common part of catalysis research. More recently, data-based methods such as machine learning have been suggested as a means to speed up discovery. This Focus issue features a collection of content dedicated to machine learning as pertaining to its potential impact on the field of catalysis.



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Research Highlights

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Comment & Opinion

  • Data science and machine learning have the potential to accelerate the discovery of effective catalysts; however, these approaches are currently held back by the issue of negative results. This Comment highlights the value of negative data by assessing the bottlenecks in data-driven catalysis research and presents a vision for a way forwards.

    • Toshiaki Taniike
    • Keisuke Takahashi
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  • Reaction networks provide complete mechanistic understanding of catalytic processes, although they can be highly complex and thus very challenging to obtain. This Perspective discusses the use of machine learning for the exploration of reaction networks in heterogeneous catalysis.

    • Johannes T. Margraf
    • Hyunwook Jung
    • Karsten Reuter
  • Computational chemistry has the potential to aid in the design of heterogeneous catalysts; however, there is currently a large gap between the complexity of real systems and what can be readily computed at scale. This Review discusses the ways in which machine learning can assist in closing this gap to facilitate rapid advances in catalyst discovery.

    • Tianyou Mou
    • Hemanth Somarajan Pillai
    • Hongliang Xin
    Review Article
  • Retrobiosynthesis aims to create novel biosynthetic pathways for the beneficial production of molecules of interest. This Review outlines how machine learning can help to advance retrobiosynthesis by improving retrosynthesis planning, enzyme identification and selection, and the engineering of enzymes and pathways.

    • Tianhao Yu
    • Aashutosh Girish Boob
    • Huimin Zhao
    Review Article
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