القائمة
Iterative workflow for model distillation. Generates a light ML force field from a pre-trained or fine-tuned model via active learning.
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
Prepare, validate, and submit UMA/fairchem machine-learning interatomic potential calculations on Genkai with the established PJM launcher and UMA virtual environment. Use for MLIP or UMA structure relaxation, molecular dynamics, energy/force inference, GPU calculations, PJM submission, restart preparation, or locating and reporting calculation outputs.
Read surface-research PDFs or JSON records and extract structured reaction, material, and modeling information for downstream agent workflows.
Build realistic oxide-surface candidates: oxygen-vacancy landscapes, adsorbate coverage landscapes, and metal-cluster-on-surface starting structures using ASE, Optuna, and optional UMA/FAIRChem calculators.
Filter and normalize paperread surface-extraction outputs into Agent-ready modeling inputs, including facet equivalence, loaded nanoparticle species, material class, reaction type, and executable modeling task mapping.
Prepare, stage, and summarize LOBSTER bonding-analysis calculations from completed VASP, CP2K, or Quantum ESPRESSO outputs.
Skill for executing ABACUS DFT materials calculations. Help users set up, run, and analyze ABACUS calculations.
| name | pfd-distillation |
| description | Iterative workflow for model distillation. Generates a light ML force field from a pre-trained or fine-tuned model via active learning. |
| metadata | {"dependent_skills":["machine-learning-force-field","molecular-dynamics","atomic-structure","deepmd"],"tags":["iterative","active-learning","distillation"]} |
Generate a light ML force field from a pre-trained or fine-tuned model by distillation.
Standard loop:
Key parameters to confirm:
