Recent advance on machine learning of <scp>MXenes</scp> for energy storage and conversion
Chengfei Qian, Kaiwen Sun, Weizhai Bao
Abstract
Transition metal carbide/nitride (MXene) is an emerging two-dimensional (2D) material in the field of energy storage and conversion due to the unique 2D structure and high ionic conductivity property, which has been extensively focused. However, the MXenes family possesses tremendous species variety and element composition with more than 90% of unknown family members. The traditional experimental methods always focused on single species, which is time-consuming and not systematic enough. Currently, machine learning, as an emerging computer technology, provided an advanced technique to boost the application of the MXene family comprehensively. In this review, we comprehensively summarize and comment on the latest application progress of machine learning in the development of MXene materials, focusing on the internal mechanism between machine learning and material development. Applications of MXene in the field of battery, supercapacitor, biological, and catalysis were fully reviewed. It can open up a new road map for the development of MXene materials in the new era.