Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes

Abstract

Functionalized MXene has emerged a promising class of two-dimensional materials having more than tens of thousands of compounds, whose uses may range from electronics to energy applications. Other than the band gap, these properties rely on the accurate position of the band edges. Hence, to synthesize MXenes for various applications, a prior knowledge of the accurate position of their band edges at an absolute scale is essential; computing these with conventional methods would take years for all the MXenes. Here, we develop a machine learning model for positioning the band edges with GW level of accuracy having a minimum root-mean-squared error of 0.12 eV. An intuitive model is proposed based on the combination of Perdew-Burke-Ernzerhof band edge and vacuum potential having a correlation of 0.93 with GW band edges. These models can be utilized to identify MXenes for a desired application in an accelerated manner.