Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene
By A. Rajan, Avanish Mishra, S. Satsangi, R. Vaish, H. Mizuseki, Kwang-Ryeol Lee, Abhishek K. Singh. Chemistry of Materials, 2018
330 citations recorded by the source corpus.
MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew–Burke–Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machin...
Subfields: Electrical and Electronic Engineering, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/5248d7fb049263d0f70e66a62a0dd048d07177ff
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In Electrical and Electronic Engineering
In Materials Chemistry
- Miguel A. L. Marques
- Silvana Botti
- Jonathan Schmidt
- Pedro Borlido
- Ahmad W. Huran
- Seiji Yunoki
- Mohammad Khazaei
- Avanish Mishra
Citations in the corpus
- Recent advances in MXenes: From fundamentals to applications (2019, methodology): “d [67]. The chemical formula considered in this database is MM 0XTT , where M/M0is an early transition metal, X is C or N, and T/T0stands for 14 dierent termination groups such as F, OH, CN, and SCN [68]. The electronic properties have been predicted with a combination of DFT and machine learning. I”
- Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning (2020, result): “57 , which leads to a slightly improved accuracy in comparison to experimental data.”