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Iterative workflow that fine-tunes pre-trained ML force fields using DFT-labeled data via active learning.
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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.
Baseado na classificação ocupacional SOC
| 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:
