Transformative strategies in photocatalyst design: merging computational methods and deep learning

Abstract

Photocatalysis is a unique technology that harnesses solar energy through in-situ processes, operating without the need for external energy inputs. It is integral to advancing environmental, energy, chemical, and carbon-neutral objectives, promoting the dual goals of pollution control and carbon reduction. However, the conventional approach to photocatalyst design faces challenges such as inefficiency, high costs, and low success rates, highlighting the need for integrating modern technologies and seeking new paradigms. Here, we demonstrate a comprehensive overview of transformative strategies in photocatalyst design, combining computational materials science with deep learning technologies. The review covers the fundamental principles of photocatalyst design, followed by a comprehensive examination of computational methods and the workflow for deep-learning-assisted design. Deep learning approaches are extensively reviewed, focusing on the discovery of novel photocatalysts, microstructure design, property optimization, novel design approaches, application exploration, and mechanistic insights into photocatalysis. Finally, we highlight the synergy between multidimensional computation and deep learning, while discussing the challenges and future directions in photocatalyst development. This review offers a comprehensive summary of deep-learning-assisted photocatalyst design, offering transformative insights that not only enhance the development of photocatalytic technologies but also expand the practical applications of photocatalysis in various domains.

Keywords

Photocatalysis / materials design / computational method / deep learning / transformative strategy

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