MALA

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

• 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