J. Phys. Chem. Lett. 10, 780-785 (2019) [pdf]

Accelerated Data-driven Accurate Positioning of the Band-edges of MXenes

Avanish Mishra, Swanti Satsangi, Arunkumar Chitteth Rajan, Hiroshi Mizuseki, Kwang-Ryeol Lee, Abhishek K. Singh
 

Functionalized MXene has emerged as one of the promising two-dimensional (2D) materials having more than tens of thousand of compounds, whose application may range from electronics to energy. In order to synthesize MXenes for these applications, accurate positioning of their band edges at absolute scale is essential, which is time-consuming to be estimate for the entire MXene database.Here, we develop a machine learning (ML) model for positioning the band edges at GW-level having a minimum root-mean-squared error (rmse) of 0.12 eV. A phenomenological model is proposed based on the combination of selected features having a correlation of 0.93 with GW band edges. These models will be useful to select the MXenes for the given applications.