v0.11.0post1

Minor Changes:

• Documentation and README updates.
• Minor bug fixes.

Full Changelog: v0.11.0...v0.11.0post1

v0.11.0

Major Changes:

• Model download from Zenodo is now multi-threaded. Users should see 15x faster speeds.
• Added an FAQ page to the documentation.
• KS2022 can filter out all structure-independent features to better compare polymorphs.
• Printing the Calculator object shows the models' location and relevant current state information.

Minor Changes:

• Minor bug fixes.
• It's now possible to load a single model with loadModels(), which is similar to downloadModels().
• Offline documentation is available inside the package.
• Documentation updates.

Full Changelog: v0.10.3...v0.11.0

v0.10.3

Major Changes:

• Some functionality upgrades related to handling models and files in environments with no write access to the pySIPFENN package directory, mostly dedicated towards High-Performance Computers (HPCs) users.
• Updates to the documentation. The core of pysipfenn is entirely and extensively described. The descriptor calculators are mostly covered.

Minor Changes:

• Final version of the workshop notebook (only minor changes)
• Minor bugfixes

New Contributors

• @shimanek made their first contribution in #3

Full Changelog: v0.10.2...v0.10.3

v0.10.2

Major Changes:

• This minor version release has no effect on the code functionalities, however it significantly expands the documentation for the package, which will now be hosted on Read The Docs page under:
  

• It also significantly expands the code documentation and type hinting. Something that new users should find very helpful. The coverage will be completed in the near future within the planned 0.10.3 release.

Full Commit Changelog: v0.10.1...v0.10.2

v0.10.1

First GitHub Release Notes

This is the first version release created and tagged on GitHub, corresponding to a second PyPI release after v0.10.0; although the SIPFENN software has been developed since 2019 by researchers at Penn State. It had 8 internal releases followed by the public release of v0.9.0 along with 2022 paper titled Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks (10.1016/j.commatsci.2022.111254) published in Computational Materials Science.

Major Changes:

The v0.10.0 has brought many changes, including:

• Translation of all code into pure Python, including structure featurization code.
• PyPI packaging
• New featurization code (KS2022) with up to x10 speed improvement; especially useful for ordered compounds.
• New featurization code (KS2022_dilute) working only on dilute structures (both pure elements and compounds) with up to x50 speed improvement.
• General improvements in the handling of models and data
• Many more in 70+ commits

Full Changelog from v0.9 to 0.10 Only: https://github.com/PhasesResearchLab/pySIPFENN/commits/v0.10.1

These changes will be described in a journal article in the near future, which will appear in the README.md after its publication.