v0.16.0

This version introduces 3 exciting changes! (1) The all new ModelAdjusters submodule automates tuning and can fetch data directly from OPTIMADE API; (2) A new manuscript detailing advantages of our featurization tools has been put on arXiv:2404.02849; and (3) the name of the software was updated to python toolset for Structure-Informed Property and Feature Engineering with Neural Networks to retain the pySIPFENN acronym but better reflect our strengths and development direction.

Major Changes:

• Submodule of ModelAdjusters has been set up for all kinds of model adjusting efforts.
• The LocalAdjuster implements local model tuning, plotting of results, and hyperparameter matrix search.
• The OPTIMADEAdjuster class combines it with the powerful OPTIMADE API to automate data fetching. Now, you can quickly

  from pysipfenn import Calculator, OPTIMADEAdjuster
  c = Calculator(autoLoad=False)
  c.loadModels("SIPFENN_Krajewski2022_NN30")
  ma = OPTIMADEAdjuster(c, 
    model="SIPFENN_Krajewski2022_NN30",  
    provider="mp",
    targetPath=("attributes", "_mp_stability", "gga_gga+u", "formation_energy_per_atom"),
    device="mps"  # MPS is for Apple M-series GPU
    )
  
  ma.fetchAndFeturize(
      'elements HAS "Hf" AND elements HAS "Mo" AND NOT elements HAS ANY "O","C","F","Cl","S"',
      parallelWorkers=4)
  ma.adjust()
  
  ma.plotStarting() # See the starting performance
  ma.plotAdjusted() # See the adjusted performance

  or to perform a hyperparameter search, replace the ma.adjust() with:

  ma.matrixHyperParameterSearch()
  ma.adjust(learningRate=0.0001, optimizer='AdamW', weightDecay=1e-05, epochs=37)

• The new manuscript on Efficient Structure-Informed Featurization and Property Prediction of Ordered, Dilute, and Random Atomic Structures has been uploaded to arXiv:2404.02849 and will be submitted to journal in a couple days after comments from collaborators.

Minor Changes:

• Added writeDescriptorsToNPY function to streamline persisting feature data into NumPy for our end-users. Appropriate tests were added. Thanks @rdamaral for making this contribution!
• Improved numerous docstrings.
• Random solution featurizer now exits gently on KeyboardInterrupt
• The LocalAdjuster and OPTIMADEAdjuster were (optionally) connected to ClearML for neat tracking of ML training histories.
• Minor bugfixes in several spots.

New Contributors

• @rdamaral made their first contribution in #15

Full Changelog: v0.15.1...v0.16.0