fix: enable strict dpgen run argument validation (#1977)

Summary

restore the default strict dargs validation for dpgen run;
reject undefined top-level run parameters instead of silently accepting them;
add a regression test that exercises run_iter() with an unknown parameter.

Context

PR #952 explicitly set strict_check=False because several runtime-supported parameters were still missing from the generated argument schema (#771, #773, #774, #781, #782).

Those original schema gaps are now resolved: GROMACS was covered by #1959, CALYPSO native/external-input modes by #1960 (including the historical buffet path from #781), PWmat by #1961, and #782 had already been completed. The repository’s normalize() helper already defaults to strict checking, and the official run examples are exercised with strict validation in tests/test_check_examples.py.

This therefore removes the historical loose-check override rather than introducing a separate validation path.

Validation

added tests/test_run_argcheck.py to verify that an unknown run parameter raises ArgumentKeyError before workflow execution;
final branch is one commit directly on the current upstream master;
final diff contains only dpgen/generator/run.py and the regression test.

Agent: ChatGPT Model: GPT-5.6 Sol

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

DP-GEN (Deep Potential GENerator) is a software written in Python, delicately designed to generate a deep learning based model of interatomic potential energy and force field. DP-GEN is dependent on DeePMD-kit. With highly scalable interface with common softwares for molecular simulation, DP-GEN is capable to automatically prepare scripts and maintain job queues on HPC machines (High Performance Cluster) and analyze results.

If you use this software in any publication, please cite:

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, and Weinan E, DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models, Computer Physics Communications, 2020, 253, 107206.

Highlighted features

Accurate and efficient: DP-GEN is capable to sample more than tens of million structures and select only a few for first principles calculation. DP-GEN will finally obtain a uniformly accurate model.
User-friendly and automatic: Users may install and run DP-GEN easily. Once successfully running, DP-GEN can dispatch and handle all jobs on HPCs, and thus there’s no need for any personal effort.
Highly scalable: With modularized code structures, users and developers can easily extend DP-GEN for their most relevant needs. DP-GEN currently supports for HPC systems (Slurm, PBS, LSF and cloud machines), Deep Potential interface with DeePMD-kit, MD interface with LAMMPS, Gromacs, AMBER, Calypso and ab-initio calculation interface with VASP, PWSCF, CP2K, SIESTA, Gaussian, Abacus, PWmat, etc. We’re sincerely welcome and embraced to users’ contributions, with more possibilities and cases to use DP-GEN.

Download and Install

DP-GEN only supports Python 3.10 and above. You can setup a conda/pip environment, and then use one of the following methods to install DP-GEN:

Install via pip: pip install dpgen
Install via conda: conda install -c conda-forge dpgen
Install from source code: git clone https://github.com/deepmodeling/dpgen && pip install ./dpgen

To test if the installation is successful, you may execute

dpgen -h

Workflows and usage

DP-GEN contains the following workflows:

dpgen run: Main process of Deep Potential Generator.
Init: Generating initial data.
- dpgen init_bulk: Generating initial data for bulk systems.
- dpgen init_surf: Generating initial data for surface systems.
- dpgen init_reaction: Generating initial data for reactive systems.
dpgen simplify: Reducing the amount of existing dataset.
dpgen autotest: Autotest for Deep Potential.

For detailed usage and parameters, read DP-GEN documentation.

Tutorials and examples

Tutorials: basic tutorials for DP-GEN.
Examples: input files in JSON format.
Publications: Published research articles using DP-GEN.
User guide: frequently asked questions listed in troubleshooting.

License

The project dpgen is licensed under GNU LGPLv3.0.

Contributing

DP-GEN is maintained by DeepModeling’s developers. Contributors are always welcome.