Difference between revisions of "Methods"

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===Surfaces and slabs===
 
===Surfaces and slabs===
  
Slabs can be constructed using [[Software and resources#Atomic Simulation Environment (ASE)|ASE]]
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Slabs can be constructed using [[Software and resources#Atomic Simulation Environment (ASE)|ASE]].
  
 
===Grain boundaries and interfaces===
 
===Grain boundaries and interfaces===

Revision as of 13:49, 22 October 2021

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

Surfaces and slabs

Slabs can be constructed using ASE.

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 [1]

Tarjei Bondevik, Akihide Kuwabara, Ole Martin Løvvik, Machine learning sampling to determine rigid body translation [2]

Disordered structures

Defect calculations

Defect configurations

Machine learning approach [3]

Phonon calculations

Nudged Elastic Band (NEB)

Polaron localization

Visualization of defect states

Bader charge analysis

FFT grid convergence?

Error messages

References

  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
  2. 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
  3. 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