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Exchange-correlation functionals for band gaps of solids: Benchmark, reparametrization and machine learning

Authors: P. Borlido, J. Schmidt, A.W. Huran, F. Tran, M.A.L. Marques, and S. Botti

Ref.: NPJ Comput. Mater. 6, 96 (2020)

Abstract: We conducted a large-scale density-functional theory study on the influence of the exchange-correlation functional in the calculation of electronic band gaps of solids. First, we use the large materials dataset that we have recently proposed [Borlido et al., J. Chem. Theory Comput. 15,5069 (2019)] to benchmark 21 different functionals, with a particular focus on approximations of the meta-generalized gradient family. Combining these data with the results for 12 functionals in our previous work, we can analyze in detail the characteristics of each approximation and identify its strong and/or weak points. Beside confirming that mBJ, HLE16 and HSE06 are the most accurate functionals for band gap calculations, we reveal several other interesting functionals, chief among which are the local Slater potential approximation, the GGA AK13LDA, and the meta-GGAs HLE17and TASK. We also compare the computational efficiency of these different approximations. Relying on these data, we investigate the potential for improvement of a promising subset of functionals by varying their internal parameters. The identified optimal parameters yield a family of functionals fitted for the calculation of band gaps. Finally, we demonstrate how to train machine learning models for accurate band gap prediction, using as input structural and composition data, as well asapproximate band gaps obtained from density-functional theory

Citations: 1 (Google scholar)

DOI: 10.1038/s41524-020-00360-0


	doi = {10.1038/s41524-020-00360-0},
	url = {https://doi.org/10.1038%2Fs41524-020-00360-0},
	year = 2020,
	month = {jul},
	publisher = {Springer Science and Business Media {LLC}},
	volume = {6},
	number = {1},
	author = {Pedro Borlido and Jonathan Schmidt and Ahmad W. Huran and Fabien Tran and Miguel A. L. Marques and Silvana Botti},
	title = {Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning},
	journal = {npj Computational Materials}