Issue 28, 2023

Machine learning facilitating the rational design of nanozymes

Abstract

As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.

Graphical abstract: Machine learning facilitating the rational design of nanozymes

Supplementary files

Article information

Article type
Review Article
Submitted
15 apr 2023
Accepted
21 may 2023
First published
23 may 2023

J. Mater. Chem. B, 2023,11, 6466-6477

Machine learning facilitating the rational design of nanozymes

Y. Li, R. Zhang, X. Yan and K. Fan, J. Mater. Chem. B, 2023, 11, 6466 DOI: 10.1039/D3TB00842H

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