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Metal-mediated chemical reactions have been a vital area of research for over a century. Recently, there has been increasing effort to improve the performance of metal-mediated catalysis by optimizing the structure and chemical environment of active catalytic species towards process intensification and sustainability. Network-supported catalysts use a solid (rigid or flexible) support with embedded metal catalysts, ideally allowing for efficient precursor access to the catalytic sites and simultaneously not requiring a catalyst separation step following the reaction with minimal catalyst leaching. This minireview focuses on recent developments of network-supported catalysts to improve the performance of a wide range of metal-mediated catalytic reactions. We discuss in detail the different strategies to realize the combined benefits of homogeneous and heterogeneous catalysis in a metal catalyst support. We outline the unique versatility, tunability, properties, and activity of such hybrid catalysts in batch and continuous flow configurations. Furthermore, we present potential future directions to address some of the challenges and shortcomings of current flexible network-supported catalysts.
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Neural network embeddings based similarity search method for atomistic systems
With the popularity of machine learning growing in the field of catalysis there are increasing numbers of catalyst databases becoming available. These databases provide us with the opportunity to search for catalysts with desired properties, which could lead to the discovery of new catalysts. However, while there are search methods for molecules based on similarity metrics, for solid-state catalyst systems there is not yet a straightforward search method. In this work, we propose a neural network embeddings based similarity search method that is applicable for both molecules and solid-state catalyst systems. We illustrate how the search method works and show search examples for the QM9, Materials Project (MP) and Open Catalyst 2020 (OC20) databases. We show that the configurations found present similarity in terms of geometry, composition, energy and in the electronic density of states. These results imply the neural network embeddings have encoded effective information that could be used to retrieve molecules and materials with similar properties.
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- Award ID(s):
- 1921946
- PAR ID:
- 10558686
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Digital Discovery
- Volume:
- 1
- Issue:
- 5
- ISSN:
- 2635-098X
- Page Range / eLocation ID:
- 636 to 644
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D2DD00055E
