Difference between revisions of "Methods"
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===Defect configurations=== | ===Defect configurations=== | ||
− | + | Evolutionary computing and machine learning for discovering of low-energy defect configurations <ref>Arrigoni, M., Madsen, G.K.H. Evolutionary computing and machine learning for discovering of low-energy defect configurations. npj Comput Mater 7, 71 (2021). https://doi.org/10.1038/s41524-021-00537-1</ref> | |
==Phonon calculations== | ==Phonon calculations== |
Revision as of 11:27, 12 January 2022
Contents
Methods
VASP Wiki and Support Forum
The VASP manual contains information on all INCAR tags and tutorials and guides to several types of calculations (www.vasp.at/wiki/).
The VASP Support Forum (www.vasp.at/forum/) allows users to troubleshoot and discuss technical and scientific topics. The VASP developers are also active in answering questions.
Convergence and Efficiency
Convergence tests
How to and relevant examples.
Benchmark
VASP efficiency on Saga (number of nodes/cores)
Computational cost: atoms vs kpoints vs functional etc.
General calculations and relaxation
NELM, NSW
Systems
Supercells
A supercell can be made in VESTA by going into Edit -> Edit data -> Unit Cell...
When the window for the unit cell has opened press Transform, change the numbers in the Transformation matrix to make a supercell of your choice.
Surfaces and slabs
Slabs can be constructed using ASE.
A specific surface can be exposed using VESTA, here is a youtube tutorial for exposing the (110) surface of a TiO2 rutile structure, tutorials for other structures can be found on the same youtube channel.
To make a supercell or a slab from the new unit cell you have just created VESTA you have to export the unit cell in a .vasp format. Open the vasp file in VESTA and then follow the same procedure for the creation of a supercell as has been explained above.
Finite-size correction for slab supercell calculations of materials with spontaneous polarization [1]
Grain boundaries and interfaces
Typically modeled as Coincident Site Lattice (CSL) structures that are optimized by rigid body translation.
Materials project provides a list of matching structures and terminations under the Substrates section for a selected structure.
Special grain boundaries in perovskites [2]
Tarjei Bondevik, Akihide Kuwabara, Ole Martin Løvvik, Machine learning sampling to determine rigid body translation [3]
Disordered structures
Defect calculations
Charge correction
Self-Consistent Potential Correction for Charged Periodic Systems [4]
CoFFEE: Corrections For Formation Energy and Eigenvalues for charged defect simulations [5]
Defect configurations
Evolutionary computing and machine learning for discovering of low-energy defect configurations [6]
Phonon calculations
Nudged Elastic Band (NEB)
Polaron localization
Visualization of defect states
Bader charge analysis
FFT grid convergence?
Error messages
References
- ↑ Yoo, SH., Todorova, M., Wickramaratne, D. et al. Finite-size correction for slab supercell calculations of materials with spontaneous polarization. npj Comput Mater 7, 58 (2021) http://dx.doi.org/10.1038/s41524-021-00529-1
- ↑ B. M. Darinskiy, N. D. Efanova & D. S. Saiko (2020) Special grain boundaries in perovskite crystals, Ferroelectrics, 567:1, 13-19, https://doi.org/10.1080/00150193.2020.1791582
- ↑ Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures, Computational Materials Science, 164 (2019) 57-65. https://doi.org/10.1016/j.commatsci.2019.03.054
- ↑ Mauricio Chagas da Silva, Michael Lorke, Bálint Aradi, Meisam Farzalipour Tabriz, Thomas Frauenheim, Angel Rubio, Dario Rocca, and Peter Deák Phys. Rev. Lett. 126, 076401. https://doi.org/10.1103/PhysRevLett.126.076401
- ↑ Naik, Mit H., and Manish Jain. CoFFEE: corrections for formation energy and eigenvalues for charged defect simulations. Computer Physics Communications 226 (2018) 114-126. https://doi-org/10.1016/j.cpc.2018.01.011
- ↑ Arrigoni, M., Madsen, G.K.H. Evolutionary computing and machine learning for discovering of low-energy defect configurations. npj Comput Mater 7, 71 (2021). https://doi.org/10.1038/s41524-021-00537-1