Skip to main content

Automated Ab-Initio Materials Modeling and Data Analysis Toolkit: Python library for pre-, post-processing and data management of ab-initio high-throughput workflows for computational materials science.

Project description

aim2dat

aim2dat (Automated Ab-Initio Materials Modeling and Data Analysis Toolkit) is a library for pre-, post-processing and data management of ab-initio high-throughput workflows for computational materials science. For further details and documentation, please visit https://aim2dat.github.io.

Feature List

  • Managing and analysing sets of crystals and molecules.
  • Ab-initio high-throughput calculations based on AiiDA.
  • Plotting material's properties such as electronic band structures, projected density of states or phase diagrams.
  • Interface to machine learning routines via sci-kit learn.
  • Function analysis: discretizing and comparing 2-dimensional functions.
  • Parsers for the DFT codes CP2K, FHI-Aims and QuantumESPRESSO as well as phonopy and critic2.

Installation

pip install aim2dat

More detailed instructions are given in the documentation (https://aim2dat.github.io/installation.html).

Contributing

Contributions are very welcome and are directly handled via the code's github repository. Bug reports, feature requests or general discussions can be accomplished by filing an issue. Extensions or changes to the code can also be directly suggested by opening a pull request. Some guidelines for code contributions are given in the documentation (https://aim2dat.github.io/#contributing).

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

aim2dat-0.2.0.tar.gz (272.7 kB view details)

Uploaded Source

Built Distribution

aim2dat-0.2.0-py3-none-any.whl (364.6 kB view details)

Uploaded Python 3

File details

Details for the file aim2dat-0.2.0.tar.gz.

File metadata

  • Download URL: aim2dat-0.2.0.tar.gz
  • Upload date:
  • Size: 272.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.10

File hashes

Hashes for aim2dat-0.2.0.tar.gz
Algorithm Hash digest
SHA256 b9ec511441b1b8e1a80807620fe7219cfff8b23c48136dcf9e625830040a5901
MD5 76f57a662c9ae6d48776a473aa61a0c5
BLAKE2b-256 bf33116a59d6343d46d83cca50d5d1dffa54bb10bd9e8970b199e71207b13160

See more details on using hashes here.

File details

Details for the file aim2dat-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: aim2dat-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 364.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.10

File hashes

Hashes for aim2dat-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 057d89fe99281d6eb1d83d6fb37d5b06003093f015633abf57f8d93d28979ea4
MD5 93aa3b1c57a92f9319fee71a4fc9a1de
BLAKE2b-256 735bdb63d5487407f6ec1e141d64aa7fb01d41bd9d56754b4b72167a17ae939d

See more details on using hashes here.

Supported by

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