Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
Abstract The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hub...
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| Main Authors: | , , , , , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/406216400ae242adb9a0f8c600fbfa6a |
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