Skip to content

Latest commit

 

History

History
131 lines (103 loc) · 6.06 KB

CHANGELOG.md

File metadata and controls

131 lines (103 loc) · 6.06 KB
Raw

dev

Version 0.6.2 (March 2020)

  • Added module for site featurization and GA feature selection.
  • Fixed ML-NEB compatibility issue with FHI-AIMS ase calculator.
  • Compatibility updated for ASE 3.19.0
  • Compatibility updated for Pandas 0.24.0
  • Compatibility updated for Scikit-learn 0.22.0
  • Dropped support for python 2.
  • Dropped support for python 3.5
  • Added testing for python 3.7 and 3.8

Version 0.6.1 (April 2019)

  • Fixed compatibility issue with MLNEB and GPAW
  • Various bugfixes

Version 0.6.0 (January 2019)

  • Added ML-MIN algorithm for energy minimization.
  • Added ML-NEB algorithm for transition state search.
  • Changed input format for kernels in the GP.

Version 0.5.0 (October 2018)

  • Restructure of fingerprint module
  • Pandas DataFrame getter in FeatureGenerator
  • CatMAP API using ASE database.
  • New active learning module.
  • Small fixes in adsorbate fingerprinter.

Version 0.4.4 (August 2018)

  • Major modifications to adsorbates fingerprinter
  • Bag of site neighbor coordinations numbers implemented.
  • Bag of connections implemented for adsorbate systems.
  • General bag of connections implemented.
  • Data cleaning function now return a dictionary with 'index' of clean features.
  • New clean function to discard features with excessive skewness.
  • New adsorbate-chalcogenide fingerprint generator.
  • Enhancements to automatic identification of adsorbate, site.
  • Generalized coordination number for site.
  • Formal charges utility.
  • New sum electronegativity over bonds fingerprinter.

Version 0.4.3 (May 2018)

  • ConvolutedFingerprintGenerator added for bulk and molecules.
  • Dropped support for Python3.4 as it appears to start causing problems.

Version 0.4.2 (May 2018)

  • Genetic algorithm feature selection can parallelize over population within each generation.
  • Default fingerprinter function sets accessible using catlearn.fingerprint.setup.default_fingerprinters
  • New surrogate model utility
  • New utility for evaluating cutoff radii for connectivity based fingerprinting.
  • default_catlearn_radius improved.

Version 0.4.1 (April 2018)

  • AtoML renamed to CatLearn and moved to Github.
  • Adsorbate fingerprinting again parallelizable.
  • Adsorbate fingerprinting use atoms.tags to get layers if present.
  • Adsorbate fingerprinting relies on connectivity matrix before neighborlist.
  • New bond-electronegativity centered fingerprints for adsorbates.
  • Fixed a bug that caused the negative log marginal likelihood to be attached to the gp class.
  • Small speed improvement for initialize and updates to GaussianProcess.

Version 0.4.0 (April 2018)

  • Added autogen_info function for list of atoms objects representing adsorbates.
    • This can auto-generate all atomic group information and attach it to atoms.info.
    • Parallelized fingerprinting is not yet supported for output from autogen_info.
  • Added database_to_list for import of atoms objects from ase.db with formatted metadata.
  • Added function to translate a connection matrix to a formatted neighborlist dict.
  • periodic_table_data.list_mendeleev_params now returns a numpy array.
  • Magpie api added, allows for Voronoi and prototype feature generation.
  • A genetic algorithm added for feature optimization.
  • Parallelism updated to be compatable with Python2.
  • Added in better neighborlist generation.
    • Updated wrapper for ase neighborlist.
    • Updated CatLearn neighborlist generator.
    • Defaults cutoffs changed to atomic_radius plus a relative tolerance.
  • Added basic NetworkX api.
  • Added some general functions to clean data and build a GP.
  • Added a test for dependencies. Will raise a warning in the CI if things get out of date.
  • Added a custom docker image for the tests. This is compiled in the setup/ directory in root.
  • Modified uncertainty output. The user can ask for the uncertainty with and without adding noise parameter (regularization).
  • Clean up some bits of code, fix some bugs.

Version 0.3.1 (February 2018)

  • Added a parallel version of the greedy feature selection. Python3 only!
  • Updated the k-fold cross-validation function to handle features and targets explicitly.
  • Added some basic read/write functionality to the k-fold CV.
  • A number of minor bugs have been fixed.

Version 0.3.0 (February 2018)

  • Update the fingerprint generator functions so there is now a FeatureGenerator class that wraps round all type specific generators.
  • Feature generation can now be performed in parallel, setting nprocs variable in the FeatureGenerator class. Python3 only!
  • Add better handling when passing variable length/composition data objects to the feature generators.
  • More acquisition functions added.
  • Penalty functions added.
  • Started adding a general api for ASE.
  • Added some more test and changed the way test are called/handled.
  • A number of minor bugs have been fixed.

Version 0.2.1 (February 2018)

  • Update functions to compile features allowing for variable length of atoms objects.
  • Added some tutorials for hierarchy cross-validation and prediction on organic molecules.

Version 0.2.0 (January 2018)

  • Gradients added to hyperparameter optimization.
  • More features added to the adsorbate fingerprint generator.
  • Acquisition function structure updated. Added new functions.
  • Add some standardized input/output functions to save and load models.
  • The kernel setup has been made more modular.
  • Better test coverage, the tests have also been optimized for speed.
  • Better CI configuration. The new method is much faster and more flexible.
  • Added Dockerfile and appropriate documentation in the README and CONTRIBUTING guidelines.
  • A number of minor bugs have been fixed.

Version 0.1.0 (December 2017)

  • The first stable version of the code base!
  • For those that used the precious development version, there are many big changes in the way the code is structured. Most scripts will need to be rewritten.
  • A number of minor bugs have been fixed.