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

Title
Accelerated Data-driven Accurate Positioning of the Band-edges of MXenes
Authors
이광렬히로시 미즈세키Avanish MishraSwanti SatsangiArunkumar Chitteth RajanAbhishek K. Singh
Issue Date
2019-02
Publisher
Journal of Physical Chemistry Letters
Citation
VOL 10, NO 4-785
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.
URI
http://pubs.kist.re.kr/handle/201004/69656
ISSN
1948-7185
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KIST Publication > Article
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