LML_retrain is an advanced coupling scheme to embed small DFT simulations in large-scale MD. To enable this embedding, we retrain (make small parameter adjustments to) linear machine learning potentials, giving seamless coupling between DFT and MD, to significantly extend the scope of hybrid simulation methods.

He segregration to screw dislocation in W

Acta Materialia, 2023: https://doi.org/10.1016/j.actamat.2023.118734

ArXiv preprint: https://arxiv.org/abs/2111.11262

Project funded by ANR JCJC MeMoPas: https://anr.fr/Project-ANR-19-CE46-0006

Team: Petr Grigorev (https://pgrigorev.github.io) and TD Swinburne (PI, https://tomswinburne.github.io), CNRS and CINaM Marseille

Python packages can be easily installed via pip or other package managers; see e.g. anaconda

• Running retraining scripts requires numpy and scipy

• Evaluating atomic structures and generating descriptor vectors requires Atomic Simulation Environment on top of the LAMMPS python module.

• LAMMPS should be compiled with ML-SNAP package which is not included by default, i.e. with a traditional make for LAMMPS

  cd /path/to/lammps/src
  make yes-ML-SNAP
  make yes-[OTHER PACKAGES]
  make mpi mode=shlib

• Script for calculating dislocation glide barriers depends on matscipy dislocation module.