com um clique
dpdata
dpdata contém 4 skills coletadas de deepmodeling, com cobertura ocupacional por repositório e páginas de detalhe dentro do site.
Skills neste repositório
Create and install dpdata plugins (especially custom Format readers/writers) using Format.register(...) and pyproject.toml entry_points under 'dpdata.plugins'. Use when extending dpdata with new formats or distributing plugins as separate Python packages.
Use dpdata Python Driver plugins to label systems (energies/forces/virials) via System.predict(), list available drivers, and build Driver objects (ase/deepmd/gaussian/sqm/hybrid). Use when working with dpdata Python API (not CLI) and you need driver-based energy/force prediction, plugin registration keys, or examples of using dpdata with ASE calculators or DeePMD models.
Minimize geometries with dpdata minimizer plugins via System.minimize(), including how minimizers relate to drivers (ASEMinimizer needs a dpdata Driver) and how to list supported minimizers (ase/sqm). Use when doing geometry optimization/minimization through dpdata Python API.
Convert and manipulate atomic simulation data formats using dpdata CLI. Use when converting between DFT/MD output formats (VASP, LAMMPS, QE, CP2K, Gaussian, ABACUS, etc.), preparing training data for DeePMD-kit, or working with DeePMD formats. Supports 50+ formats including deepmd/raw, deepmd/comp, deepmd/npy, deepmd/hdf5.