القائمة
Convert crystal structure files between extxyz and CIF, including splitting multi-frame extxyz into per-frame CIF files.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | structure-conversion |
| description | Convert crystal structure files between extxyz and CIF, including splitting multi-frame extxyz into per-frame CIF files. |
| metadata | {"tools":["run_python_file"],"tags":["structure","conversion","extxyz","cif","ASE"],"entry_type":"capability","skill_level":"L1","dependent_skills":[]} |
Use the CLI script structure_conversion_tools.py (located alongside this file) to convert structure files between extxyz and cif. It mirrors the behavior of the former ase_structure_convert_tool and is especially useful for turning MatterGen multi-frame extxyz outputs into per-frame cif files.
Every command writes a JSON object to stdout and exits 0 on success or 1 on error. Always parse the JSON output and confirm "status": "success".
python skills/structure_conversion/structure_conversion_tools.py convert <input_file> <output_path> [options]
cif -> extxyzextxyz or xyz -> single cifextxyz or xyz -> directory of per-frame cif filesextxyz or xyz -> extxyzFor multi-frame extxyz input, never collapse all frames into one cif. Write one cif file per frame using stable names such as frame_000001.cif.
convertConvert a structure file and return output metadata.
Positional arguments
| Argument | Description |
|---|---|
input_file | Source structure path. Supported: .extxyz, .xyz, .cif |
output_path | Output file path or output directory |
Optional arguments
| Flag | Type | Description |
|---|---|---|
--output-format | str | Explicit target format: cif or extxyz. If omitted, infer from output_path |
JSON response fields
status, message, output_mode, output_format, frame_count, output_path, output_paths
extxyz to cif# Multi-frame extxyz to per-frame CIF directory
python skills/structure_conversion/structure_conversion_tools.py convert \
generated.extxyz converted_cifs --output-format cif
# Single CIF to extxyz
python skills/structure_conversion/structure_conversion_tools.py convert \
structure.cif structure.extxyz
mattergen generation and before downstream property predictionoutput_format is omitted, the script infers it from output_pathcif, the result is a directory, not a single fileGenerate VASP inputs (INCAR/POSCAR/POTCAR/KPOINTS) via pymatgen.io.vasp.sets for DFT calculations: MPRelaxSet (geometry relaxation), MPStaticSet (SCF), MPNonSCFSet (band structure), MatPESStaticSet (MLFF energy/force labeling). INCAR is driven by pymatgen defaults; the agent only supplies user_incar_settings overrides. Use when the user asks to prepare VASP calculations, run DFT, or generate MLFF training data from structures. Do NOT use for VASP result post-processing / analysis, non-VASP DFT codes, or molecular dynamics — this skill only generates input files and submits jobs.
Deep potential models finetuning and testing using the DeePMD-kit. Use this skill whenever finetuning a Deep Potential (DPA-1 / DPA-2 / DPA-3 / DPA-4) model or running model tests on a dataset. The oldest DP descriptors such as se_e2_a, se_e2_r, and se_e3 are no longer supported. Training from scratch is NEVER advised unless distilling a student model from a teacher model. Multitask fine-tuning is NOT supported.
Skill for running ASE calculations, including energy/force/stress evaluation, molecular dynamics (MD) and structure optimization. Now only use machine-learned force fields (MLFFs) as calculators.
Concept skill for Machine Learning Force Fields (MLFFs). Describes what MLFFs are, the distinction between fine-tuning and distillation, and which tool skills to use. Load this before selecting a specific MLFF framework (DeePMD, MatterSim, etc.).
Concept skill for Molecular Dynamics (MD) simulation. Describes MD ensembles, key parameters, and which tool skills to invoke for MD runs. Use this to understand MD concepts before selecting a specific simulation tool.
Equation-of-State (EOS) benchmark skill — compare DFT, pretrained, and finetuned model E(V) curves to evaluate force-field quality for bulk crystals and simple systems.