High Throughput DFT Calculation Resources

Python functions / libraries / other resources useful for running High Throughput (query-based) DFT calculations on nanoHUB

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Version 1.0 - published on 20 Oct 2017

doi:10.4231/D3599Z40K cite this

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    Lattice parameter comparison for ABO3 cubic Pervoskites Illustration of a band structure as obtained from the tool



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Free access to powerful computing tools is one goal of nanoHUB, and while many available tools successfully facilitate single simulations, none adequately facilitate high throughput simulation sets. As most users who require high throughput capabilities are more experienced intermediate users, a more exposed tool is appropriate. Therefore, this Jupyter notebook and python-based tool was developed to fulfill this need.

This tool allows a user to query the Materials Project database to collect a set of materials and then configure and generate quantumESPRESSO input files for the selected materials. These quantumESPRESSO simulations can then be run in parallel on nanoHUB. After the simulations are complete, users can create their own python code to parse quantumESPRESSO output or use the basic included parsers to retrieve simulation results. These results may be exported or visualized directly in the Jupyter notebook using a wide selection of powerful scientific libraries including Matplotlib and Plotly.

The HTDFT tool is built on a library of functions called HTDFTnano, which were developed for high throughput quantumESPRESSO simulations on nanoHUB. These functions include querying the Materials Project database, configuring quantumESPRESSO input files, selecting appropriate pseudopotentials (GGA(PBE) and LDA (PZ and PW)), and submitting, monitoring, and managing simulations on nanoHUB.

Along with this function library, tutorial notebooks are included in the tool to provide context and applicable references for configuring or creating new notebooks. The tutorial and tool notebooks include examples of data analysis and plotting using Plotly, matplotlib, and pymatgen. Specifically, it shows how we obtain relaxed lattice parameters and electronic bandstructures for a set of cubic perovskite materials from an existing dataset of pre-run quantumESPRESSO simulations using GGA (PBE) exchange and correlation functional and plot them against those values reported in Materials Project, which were calculated using VASP and within the PBE-GGA approximation. 

HTDFT and HTDFTnano are under active development, and as more features are added to HTDFTnano, example notebooks will be added or modified to reflect new capabilities.

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QUANTUMESPRESSO (http://www.quantum-espresso.org)


DFT Simulation engine: QUANTUMESPRESSO (http://www.quantum-espresso.org)


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[3] Ong, Shyue Ping, et al. "Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis." Computational Materials Science 68 (2013): 314-319. 
[4] Kohn, Walter, and Lu Jeu Sham. "Self-consistent equations including exchange and correlation effects." Physical review 140.4A (1965): A1133. 
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[7] Perdew, John P., and Alex Zunger. "Self-interaction correction to density-functional approximations for many-electron systems." Physical Review B 23.10 (1981): 5048.

Cite this work

Researchers should cite this work as follows:

  • Austin Jacob Zadoks, Karthik Guda Vishnu, Sam Reeve, David M Guzman, Alejandro Strachan (2017), "High Throughput DFT Calculation Resources," https://nanohub.org/resources/htdft. (DOI: 10.4231/D3599Z40K).

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