Features

Parallel preprocessing, network training and model inference
Distributed hyperparameter optimization (Optuna) and distributed training-free network architecture optimization (NASWOT)
Reproducibility through single Parameters object, easy interface to JSON for automated sweeps
Internal caching system for intermediate quantities (e.g. DOS, density, band energy) for improved performance
Modular design
OF-DFT-MD interface to create initial configurations for ML based sampling

Change notes:

MALA now operates internally in Angstrom consistently
- Volumetric data that has been created with MALA v1.0.0 can still be used, but unit conversion has to be added to the scripts in question
Implemented caching functionality
- The old post-processing API is still fully functional, but will not use the caching functions; instead, MALA now has a more streamlined API tying calculators to data
More flexible data conversion methods
Improved Optuna distribution scheme
Implemented parallel total energy inference
Reduced import time for MALA module
Several smaller bugfixes