machine-learning-force-field

name	machine-learning-force-field
description	Concept skill for Machine Learning Force Fields (MLFFs). Describes what MLFFs are, the distinction between training and inference, and which tool skills to use. Load this before selecting a specific MLFF framework (DeePMD, MatterSim, etc.).
metadata	{"dependent_skills":["deepmd","mattersim","ase-deepmd"],"tags":["MLFF","machine-learning-potential","deep-potential","force-field","training"]}

Machine Learning Force Field (MLFF)

A Machine Learning Force Field (MLFF) is a surrogate model trained on DFT reference data that predicts atomic energies and forces at a fraction of the computational cost. MLFFs enable large-scale and long-timescale molecular dynamics simulations that would be prohibitively expensive with DFT directly.

Workflow Phases

Phase	Description
Pre-trained model	A general-purpose model trained on large, diverse datasets. Try this first.
Fine-tuning	Adapts a pre-trained model to a target system using domain-specific DFT data.
Training from scratch	Used in distillation: train a lightweight student model on teacher-labeled data.
Inference / MD	Deploy the trained model for structure relaxation or molecular dynamics.

When to Use

• Run MD exploration to sample the configuration space of a new material.
• Relax generated structures before DFT validation.
• Screen large candidate sets efficiently before expensive DFT runs.
• Replace DFT in high-throughput workflows after validating accuracy.

Available Tool Skills

Skill	Framework	Best For
deepmd	DeePMD-kit	Training and fine-tuning DPA-1/DPA-2 models; PFD workflow
mattersim	MatterSim	Pre-trained universal MLFF; structure relaxation and MD
ase-deepmd	ASE + DeePMD	Running MD with a trained DeePMD model via ASE interface

Load the appropriate tool skill for detailed instructions (e.g., load_skill("deepmd")).

Choosing a Framework

• Use deepmd when you need to train or fine-tune a model on your own DFT dataset.
• Use mattersim when you want a pre-trained universal model without retraining.
• Use ase-deepmd when you need to run MD simulations with a trained DeePMD potential.