guide-for-model-training

name	guide-for-model-training
description	General guideline for MLFF model training. Describes the decision flow for choosing between pre-trained model validation, fine-tuning on available data, and launching a full PFD workflow.
metadata	{"dependent_skills":["machine-learning-force-field","dft-calculation","deepmd","abacus"],"tags":["model","fine-tune","machine-learning-force-field"]}

Model training is a dynamic, iterative process. Follow this decision flow and revisit earlier steps if results are unsatisfactory.

Decision Flow

Stage 1 — Check the Pre-trained Model

• Always try the pre-trained model first.
• Query the available domain datasets. If not sure, ALWAYS ask user.
• Query the selected domain datasets for the target system. If it is available, validate the pre-trained model for it.
• If validation passes, stop here — no training is needed.

Stage 2 — Fine-tune on Available Data

• If dataset available and pre-trained model does not perform adequately, fine-tune it using the existing dataset.
• Prefer this over more complex workflows whenever data is available.
• Evaluate the fine-tuned model; if results are sufficient, stop here.

Stage 3 — PFD Workflow (Last Resort)

• If no available dataset or fine-tuned model is insufficient, launch the PFD workflow.
• PFD iteratively generates new data via MD exploration, labels it, and retrains until convergence.
• See pfd-finetuning guide for full details.

General Notes

• Always evaluate the model after each step before proceeding to the next.
• Prefer the simplest effective strategy — avoid PFD if fine-tuning suffices.
• Each step may require multiple rounds of interaction and parameter adjustment.