Package: gdpx
NOTE: This package has been renamed asgdpxfromGDPyas we found unfortunately the name ofgdpyhas been taken for a long time. Thexinstead ofycan be seen as an extended and improved verion of the original one. The development ofgdpxwill still be undergoing in thisGDPyrepository. Also, the documentation is changed tohttps://gdpx.readthedocs.io.NOTE:gdpxis under active development and has not been released. The APIs are frequently changed and we cannot ensure any
backward compatibility.Installgdpxis a pure python package. Since it does not include any codes that actually perform calculations and training, for example,VASPandDEEPMD, you should install them by yourselves.Latest Version$ python -m pip install git+https://github.com/hsulab/GDPy.gitStable ReleaseWe have pull request to addgdpxto conda-forge but it can only be installed from a personal conda channel right now.$ conda install gdpx -c jxu1223 -c conda-forgeTable of ContentsOverviewFeaturesArchitectureModulesAuthorsLicenseOverviewDocumentation: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.FeaturesA 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.ArchitectureModulesPotentialWe 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 byasecalculatorsusing eitherpythonbuilt-in codes (PyTorch, TensorFlow)
or File-IO based external codes (e.g.lammps).Supported MLIPs:MLIPsRepresentationRegressorImplemented Backendeann(Rescursive) Embedded Atom DescriptorNN/PyTorchASE/Python, ASE/LAMMPSdeepmdDeep Potential DescriptorsNN/TensorflowASE/Python, ASE/LAMMPSlaspAtom-Centered Symmetry FunctionsNN/LASPASE/LASPnequipE(3)-Equivalent Message PassingNN/PyTorchASE/Python, ASE/LAMMPSNOTE: 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-initiomethods are used to label structures with target properties (e.g. total energy, forces, and stresses).Name.DescriptionBackendNotesreaxReactive Force FieldLAMMPSxtbTight BindingxtbUnder developmentVASPPlane-Wave Density Functional TheoryVASPCP2KDensity Functional TheoryCP2KExpeditionWe 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.NameCurrent AlgorithmBackendMolecular Dynamics (md)Brute-Force/Biased DynamicsASE, LAMMPSEvolutionary Global Optimisation (evo)Genetic AlgorithmASE/GDPyBasin HoppingMonte Carlo like Global OptimisationGDPyAdsorbate Configuration (ads)Adsorbate Configuration Graph SearchGDPyReaction Event Exploration (rxn)Artificial Force Induced Reaction (AFIR)GDPyGrand Cononical Monte Carlo (gcmc)Monte Carlo with Variable CompositionGDPyWorkflowThere are two kinds of workflows according to the way how they couple the expedition and the training. Offline workflow as the major category separates the expedition and the training, which collects a large number of structures from several expeditions and then train the MLIP with the collective dataset. This process is highly parallelised, and is usually aimed for a general dataset. Online workflow, a really popular one, adopts an on-the-fly strategy to build 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 a MLIP for a very specific system.TypeSupported ExpeditionOfflinemd, evo, ads, rxnOnlinemdAuthorsJiayan Xu(jxu15@qub.ac.uk)under the supervision ofProf. P. Huat Queen's University Belfast.LicenseGDPy project is under the GPL-3.0 license.