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Iterative workflow that fine-tunes pre-trained ML force fields using DFT-labeled data via active learning.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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-finetuning |
| description | Iterative workflow that fine-tunes pre-trained ML force fields using DFT-labeled data via active learning. |
| metadata | {"dependent_skills":["machine-learning-force-field","dft-calculation","molecular-dynamics","atomic-structure","deepmd","abacus"],"tags":["iterative","active-learning","fine-tune"]} |
Coordinate iterative fine-tuning of ML force fields from a pre-trained model.
Standard loop:
Key parameters to confirm:
