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Route ABACUS requests to task-specific subskills based on user intent. Use when the user asks for any ABACUS DFT calculation and you need to determine whether the task is SCF, relaxation, MD, or electronic analysis.
Route NEP requests to task-specific subskills. NEP (Neuroevolution Potential) is the native machine-learning potential family of the GPUMD ecosystem — analogous to DeePMD-kit for LAMMPS. Use when the user asks for `nep.in`, `train.xyz`, `test.xyz`, NEP training, NEP89 reuse, prediction mode, fine-tuning, dipole / polarizability auxiliary models, or automation via NepTrain / NepTrainKit.
Train a first NEP potential from labeled extxyz data. Use when the user needs `nep.in`, `train.xyz`, `test.xyz`, parameter guidance, loss.out interpretation, or deployment of the resulting `nep.txt` back into GPUMD. NEP is the native machine-learning potential for GPUMD and plays the role that DeePMD plays for LAMMPS.
Route VASP DFT requests to task-specific subskills based on user intent. Use when the user asks for VASP workflows and you must decide between static SCF, relaxation, DOS, or band-structure task preparation. This orchestration skill does not own detailed input generation logic; it dispatches to the correct VASP subskill and enforces consistent handoff to submission skills.
Prepare VASP static SCF input tasks from a user-provided structure and essential DFT settings. Use when the user needs single-point electronic structure/total-energy calculations with INCAR generation, KSPACING-based k-point policy (or explicit KPOINTS on request), and POTCAR mapping instructions.
Prepare ABACUS single-point (static SCF) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit ABACUS INPUT/STRU/KPT generation, pseudopotential + orbital mapping, and basis-type selection (PW or LCAO).
Generate Quantum ESPRESSO DFT input tasks from a user-provided structure plus user-specified DFT settings. Use when the user wants to prepare QE calculations such as SCF, NSCF, relax, vc-relax, MD, bands, DOS, or phonons starting from a structure file or coordinates together with pseudopotentials, functional choice, cutoffs, k-point settings, smearing, spin/charge, and convergence parameters. This skill prepares the QE task only; use a separate submission skill such as dpdisp-submit to submit the generated task.
| name | dft-abacus |
| description | Route ABACUS requests to task-specific subskills based on user intent. Use when the user asks for any ABACUS DFT calculation and you need to determine whether the task is SCF, relaxation, MD, or electronic analysis. |
| compatibility | Requires a user-provided structure, compatible pseudopotentials/orbital files, and runnable ABACUS environment. |
| license | GPL-3.0-only |
| catalog-hidden | true |
| metadata | {"author":"qqgu","version":"0.2.0","repository":"https://github.com/deepmodeling/abacus-develop"} |
ABACUS (Atomic-orbital Based Ab-initio Computation at UStc) is an open-source DFT code supporting both PW (plane-wave) and LCAO (linear combination of atomic orbitals / numerical atomic orbital) basis sets. It is developed by the DeepModeling community and is tightly integrated with the DeePMD / NEP / dpdata ecosystem.
| User intent | Subskill |
|---|---|
| Single-point / total energy | dft-abacus/static |
| Relaxation (ion / cell) | dft-abacus/relax (planned) |
| Molecular dynamics | dft-abacus/md (planned) |
| Band / DOS / electronic | dft-abacus/electronic (planned) |
basis_type pw, behaves like QE.dpdata output support via out_level ie for NEP/DeePMD
training data generation.INPUT, STRU, KPT (not a single
input file like QE or CP2K).