MALA

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

• 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