NOTE: GDPy is the name of this repository and the corresponding python package name is gdpx.

NOTE: gdpx is under active development and has not been released. The APIs are frequently changed and we cannot ensure any backward compatibility.

Install

gdpx is a pure python package. Since it does not include any codes that actually perform calculations and training, for example, VASP and DEEPMD, you should install them by yourselves.

Latest Version

$ python -m pip install git+https://github.com/hsulab/GDPy.git

Stable Release

$ conda install gdpx -c conda-forge

Table of Contents

• Overview
• Features
• Architecture
• Modules
• Authors
• License

Overview

Documentation: https://gdpx.readthedocs.io (Changed from gdpyx)

GDPy stands for Generating Deep Potential with Python (GDPy/GDP¥), including 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.

Features

• A unified interface to various MLIPs.
• A graph-and-node session to construct user-defined workflows.
• Versatile exploration algorithms to construct a general dataset.
• Automation workflows for dataset construction and MLIP training.

Architecture

Modules

Potential

We do not implement any MLIP but offers a unified interface to access. Certain MLIP could not be utilised before corresponding required packages are installed correctly.The calculations are performed by ase calculators using either python built-in codes (PyTorch, TensorFlow) or File-IO based external codes (e.g. lammps).

Supported MLIPs:

NOTE: We use a modified eann package to train and utilise.

NOTE: Allegro is supported as well through the nequip manager.

Other Potentials: Some potentials besides MLIPs are supported. Force fields or semi-empirical potentials are used for pre-sampling to build an initial dataset. Ab-initio methods are used to label structures with target properties (e.g. total energy, forces, and stresses).

Expedition

We take advantage of codes in well-established packages (ASE and LAMMPS) to perform basic minimisation and dynamics. Meanwhile, we have implemented several complicated alogirthms in GDPy itself.

Workflow

There are two kinds of workflows according to the way they couple the expedition and the training. Offline workflow as the major category separates the expedition and the training, which collects structures from several expeditions and then trains the MLIP with the collective dataset. This process is highly parallelised and is usually aimed at a general dataset. Online workflow, a really popular one, adopts an on-the-fly strategy to build a dataset during the expedition, where a new MLIP is trained to continue exploration once new candidates are selected (sometimes only one structure every time!). Thus, it is mostly used to train an MLIP for a particular system.

Authors

Jiayan Xu (jxu15@qub.ac.uk)

under the supervision of Prof. P. Hu at Queen's University Belfast.

License

GDPy project is under the GPL-3.0 license.