Issue 39, 2023

Machine learning integrated photocatalysis: progress and challenges

Abstract

Discovering efficient photocatalysts has long been the goal of photocatalysis, which has traditionally been driven by serendipitous or try-and-error strategies. Recent developments in photocatalysis integrated with machine learning techniques promise to accelerate the discovery of photocatalysts, but are also facing significant challenges. In this review, advances in machine learning integrated photocatalysis are first presented from the perspective of three main photocatalytic processes: light harvesting, charge generation and separation, and surface redox reactions. Next, progress in using machine learning to understand complex photoactivity–structure relationships and identify the factors governing activity follows. A future photocatalysis paradigm is then provided with the integration of artificial intelligence, robots and automation. Lastly, we discuss the current challenges in machine learning integrated photocatalysis. This review aims to provide a systematic overview and guidelines to the broad scientific community interested in photocatalysis and artificial intelligence for solar fuel synthesis.

Graphical abstract: Machine learning integrated photocatalysis: progress and challenges

Article information

Article type
Highlight
Submitted
28 Cʼhwe. 2023
Accepted
12 Ebr. 2023
First published
17 Ebr. 2023

Chem. Commun., 2023,59, 5795-5806

Machine learning integrated photocatalysis: progress and challenges

L. Ge, Y. Ke and X. Li, Chem. Commun., 2023, 59, 5795 DOI: 10.1039/D3CC00989K

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