Features

Preprocessing of QE data using LAMMPS interface and LDOS parser (parallel via MPI)
Networks can be created and trained using pytorch (arallel via horovod)
Hyperparameter optimization using optuna, orthogonal array tuning and neural architecture search without training (NASWOT) supported
- optuna interface supports distributed runs and NASWOT can be run in parallel via MPI
Postprocessing using QE total energy module (available as separate repository)
- Network inference parallel up to the total energy calculation, which currently is still serial.
Reproducibility through single Parameters object, easy interface to JSON for automated sweeps
Modular design

Change notes:

full integration of Sandia ML-DFT code into MALA (network architectures, misc code still open)
Parallelization of routines:
- Preprocessing (both SNAP calculation and LDOS parsing)
- Network training (via horovod)
- Network inference (except for total energy)
Technical improvements:
- Default parameter interface is now JSON based
- internal refactoring