Two-dimensional carbon/boron nitrides: modification, machine learning and beyond

Abstract

The exploration and functionalization of two-dimensional (2D) materials have opened new horizons in the fields of catalysis and materials science. Therein, 2D non-metallic nitrides have attracted significant attention due to the easy availability of material sources, versatile materials modification methods, and exceptional physicochemical properties. This review focuses on recent advances in the modification of carbon/boron nitride (CBN) and the critical role played by machine learning (ML) techniques because ML enables rapid screening and optimization of materials properties that would be infeasible with traditional experimental methodologies alone. The structural and electronic modifications of CBN materials are introduced, followed by an investigation of the direct relationship between doping, defect engineering, and the resulting optoelectronic properties. The mechanism behind these modifications is particularly discussed in detail using state-of-the-art computational methods. It demonstrates how the incorporation of ML into the development of 2D CBN materials can lead to significant advances in catalysis and hold promising implications for sustainable energy and environmental remediation.