Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

MXene is a recent addition to the ever-growing family of 2D-materials, promising for optical, electronic, energy storage and photocatalytic applications. Utilizing statistical learning-based approach, we electronically characterize this vast class of materials by predicting their band gaps with GW level accuracy. Using a classification model, MXene having finite band gaps are filtered out and few of them are selected to build a machine learning model. The model is built by correlating the easily available elemental and computed properties as features with respect to calculated GW band gaps of selected MXene. Depending upon feature combinations, Gaussian process regression method resulted in optimized model yielding low root-mean-squared-error of 0.14 eV, which can be employed to estimate the accurate GW band gaps of tens of thousands of MXenes [1,2] within minutes. Our results demonstrate that machine learning model can bypass band gap underestimation problem of local and semi-local functionals used in DFT calculations, without subsequent correction using time-consuming GW approach.

References:
1. aNANt: a functional materials database. http://anant.mrc.iisc.ac.in
2. A. C. Rajan et. al. Chem. Mater. 30, 4031 (2018)