v0.0.1-alpha

Generating Deep Potential with Python (GDPy/GDP¥)

GDPy includes a set of tools and Python modules to automate the structure exploration and the training for machine learning interatomic potentials (MLIPs). It mainly focuses on the applications in heterogeneous catalysis. The target systems are metal oxides, supported clusters, and solid-liquid interfaces.

Documentation: https://gdpyx.readthedocs.io

Features:

• A unified interface to various MLIPs.
• Versatile exploration algorithms to construct a general dataset.
• Automation workflows for dataset construction and MLIP training.

Functions:

• Implement abstractclasses for various functionalities, namely potential, driver, worker, scheduler and expedition.
• Support a unified interface to several machine learning potentials for production and training: eann, deepmd, lasp, and nequip/allegro, and other useful ones: reax and vasp.
• Support ase and lammps as backends to perform basic computations: minimisation and molecular dynamics. Transition-state search is under development.
• Support several expedition strategies: molecular dynamics (md), evolutionary global optimisation (evo), adsorption configuration search (ads), and reaction exploration (rxn).
• Support on-the-fly molecular dynamics expedition, further tests are needed.