Skip to main content

Pymatgen extension for defects analysis

Project description

testing codecov zenodo pypi

📄 Full Documentation

This package is an extension to pymatgen for performing defect analysis. The package is designed to work with VASP inputs and output files and is meant to be used as a namespace package extension to the main pymatgen library. The new module has been redesigned to work closely with atomate2.

While the atomate2 automation framework is not required for this code to be useful, users are strongly encouraged to to adopt the atomate2 framework as it contains codified “best practices” for running defect calculations as well as orchestrating the running of calculations and storing the results.

Non-exhaustive list of features:

Reproducible definition of defects

Defects are defined based on the physical concept they represent, independent of the calculation details such as simulation cell size. As an example, a Vacancy defect is defined by the primitive cell of the pristine material plus a single site that represents the vacancy site in the unit cell.

Formation energy calculations

The formation energy diagram is a powerful tool for understanding the thermodynamics of defects. This package provides a simple interface for calculating the formation energy diagram from first-principles results. This package handles the energy accounting of the chemical species for the chemical potential calculations, which determines the y-offset of the formation energy. This package also performs finite-size corrections for the formation energy which is required when studying charged defects in periodic simulation cells.

Defect Position

Identification of the defect positions in a simulation cell after atomic relaxation is not trivial since the many atoms can collectively shift in response to the creation of the defect. Yet the exact location of the defect is required for the calculation of finite-size corrections as well as other physical properties. We devised a method based on calculating a SOAP-based distortion field that can be used to identify the defect position in a simulation cell. Note, this method only requires the reference pristine supercell and does not need prior knowledge of how the defect was created.

Defect Complexes

Multiple defects can be composed into defect complexes. The complex is can be treated as a normal defect object for subsequent analysis.

Defect Interactions

Simulation of defect-photon and defect-phonon interactions under the independent particle approximation.

Previous versions of the defects code

This package replaces the older pymatgen.analysis.defects modules. The previous module was used by pyCDT code which will continue to work with version 2022.7.8 of pymatgen.

Contributor

  • Lead developer: Dr. Jimmy-Xuan Shen

  • This code contains contributions from the original defects analysis module of pymatgen from Dr. Danny Broberg and Dr. Shyam Dwaraknath.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pymatgen-analysis-defects-2023.7.31.tar.gz (72.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pymatgen_analysis_defects-2023.7.31-py3-none-any.whl (67.2 kB view details)

Uploaded Python 3

File details

Details for the file pymatgen-analysis-defects-2023.7.31.tar.gz.

File metadata

File hashes

Hashes for pymatgen-analysis-defects-2023.7.31.tar.gz
Algorithm Hash digest
SHA256 f298dc14c3a8ac071b44219132c8658259d2d97b488af50998e2b5d53bf2a7ee
MD5 7141352a5338cfac0142921e4646f1bc
BLAKE2b-256 59a1d5c2a22e03ad45f468ed3369a0e6f6cdf9eee1d2e4e2a90952e9c93090a8

See more details on using hashes here.

File details

Details for the file pymatgen_analysis_defects-2023.7.31-py3-none-any.whl.

File metadata

File hashes

Hashes for pymatgen_analysis_defects-2023.7.31-py3-none-any.whl
Algorithm Hash digest
SHA256 d48c73884448f2556955387bae25df47089ce3d1224d12735352c22e1131fdde
MD5 f14240c9d6a215d98991a0c53fc25c8a
BLAKE2b-256 82f6a8f8c09ef7b5633f87a8c3bcad91df1d287df8f8f96f51d03947ea99ad67

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page