菜单
Iterative workflow for model distillation. Generates a light ML force field from a pre-trained or fine-tuned model via active learning.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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.
基于 SOC 职业分类
| 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:
