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Resolution of the Band Gap Prediction Problem for Materials Design
Open accessAbstract
An important property with any new material is the band gap. Standard density functional theory methods grossly underestimate band gaps. This is known as the band gap problem. Here, we show that the hybrid B3PW91 density functional returns band gaps with a mean absolute deviation (MAD) from experiment of 0.22 eV over 64 insulators with gaps spanning a factor of 500 from 0.014 to 7 eV. The MAD is 0.28 eV over 70 compounds with gaps up to 14.2 eV, with a mean error of -0.03 eV. To benchmark the quality of the hybrid method, we compared the hybrid method to the rigorous GW many-body perturbation theory method. Surprisingly, the MAD for B3PW91 is about 1.5 times smaller than the MAD for GW. Furthermore, B3PW91 is 3-4 orders of magnitude faster computationally. Hence, B3PW91 is a practical tool for predicting band gaps of materials before they are synthesized and represents a solution to the band gap prediction problem.
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Density-functional thermochemistry. III. The role of exact exchange
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464913 · Cited by 103,767
Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874
Self-interaction correction to density-functional approximations for many-electron systems
Perdew, J. P., & Zunger, A. (1981) · Physical Review B · doi:10.1103/physrevb.23.5048 · Cited by 20,887
Topological insulators in Bi2Se3, Bi2Te3 and Sb2Te3 with a single Dirac cone on the surface
Zhang, H., Liu, C.-X., Qi, X.-L., Dai, X., Fang, Z., & Zhang, S.-C. (2009) · Nature Physics · doi:10.1038/nphys1270 · Cited by 6,359
Observation of a large-gap topological-insulator class with a single Dirac cone on the surface
Xia, Y., Qian, D., Hsieh, D., Wray, L., Pal, A., Lin, H., Bansil, A., Grauer, D., Hor, Y. S., Cava, R. J., et al. (2009) · Nature Physics · doi:10.1038/nphys1274 · Cited by 3,765
Experimental Realization of a Three-Dimensional Topological Insulator, Bi 2 Te 3
Chen, Y. L., Analytis, J. G., Chu, J.-H., Liu, Z. K., Mo, S.-K., Qi, X. L., Zhang, H. J., Lu, D. H., Dai, X., Fang, Z., et al. (2009) · Science · doi:10.1126/science.1173034 · Cited by 3,585
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Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b01294 · Cited by 1,552
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Metadata
- Authors
- Crowley, J. M., Tahir-Kheli, J., & Goddard, W. A.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-03-16
- Volume
- 7
- Issue
- 7
- Pages
- 1198-1203
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W2308963249
- Cited by
- 349
- References
- 6
- FWCI
- 11.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
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