Machine learning (ML)-assisted development of 2D green catalysts to support sustainability

Le résumé fourni par la source

adopting data-driven predictions, thereby assisting in the design and development of a catalyst with desired properties for piezo-catalysis, water splitting, and photocatalysis. This report explores the need for ML to forecast material properties, optimize reaction conditions, and find new catalysts by enhancing computational techniques, such as the density functional theory (DFT), that require a lot of resources. This is a new approach that faces some challenges, which are systematically discussed in this report. The outcomes of this report will serve as guidelines for scholars to explore ML-assisted development of green 2D catalysts, which are needed to achieve high-performance catalysis, thereby managing and maintaining a sustainable environment.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.