PFD-kit is a cloud-base workflow for generating of deep-learning force fields from pre-trained atomic models (P) through fine-tuning (F) and distillation (D). Compared to training force fields from scratch, PFD requires significantly less training data by leveraging transferable knowledge from large pre-trained model. This reduces computational cost by an order of magnitude, making PFD well-suited for high-throughput calculations.
The workflow is especially useful for complex systems (e.g., high-entropy alloys, surfaces/interfaces) that are traditionally difficult to model. With its cloud-ready design, PFD-kit can be a powerful tool for computational materials science.
For complete tutorials and user guide, please refer to our Online Documentation
PFD-kit is bulit on the dflow package and incorporates components of DPGEN2 workflow. PFD-kit currently supports the Deep Potential and MatterSim (only fine-tuning) models, with additional models (e.g. MACE) planned.
- [PDF-kit: Workflow for finetune and fast distillation]
PFD-kit provides an automated workflow for generating machine-learning force fields from pre-trained atomic models via fine-tuning and knowledge distillation.
The PFD workflow begins with a pre-trained large atomic model (LAM) such as DPA, Mattersim, etc. Fine-tuning adapts the model to specific material domains with far less data than traditional from-scratch training. This greatly reduces computational cost while enabling accurate predictions for materials with highly complex structures and chemistries.
While fine-tuned models are accurate, their large architectures can be inefficient for large-scale simulations. To address this, PFD-kit applies knowledge distillation: a lightweight model is trained on data generated and labeled by the fine-tuned model. The distilled model achieves similar accuracy within the target domain but runs much faster during inference.
PFD-kit automates the entire fine-tuning and distillation process. It includes data-selection algorithms that maximize efficiency using entropy-based measures of atomic environments. Built on dflow, it supports both local and cloud execution.
PFD-kit can be built and installed form the source.
pip install git+https://github.com/ruoyuwang1995nya/pfd-kit.gitPFD jobs can be submittied through command line interface using the submit subcommand:
pfd submit input.jsonPFD-kit supports two major types of workflow, fine-tuning and distillation, and the workflow parameters are defined in the input.json script. Users also need to prepare required input files such as pre-trained model files, material structure files, training scripts, etc. For a complete guide on the PFD-kit usage and explanation of input script, please refer to the online documentation.
If you use PFD-kit in a publication, please cite the following paper:
@article{wang2025pfd,
title = {Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models},
author = {Wang, Ruoyu and Gao, Yuxiang and Wu, Hongyu and Zhong, Zhicheng},
journal = {Phys. Rev. Mater.},
volume = {9},
issue = {11},
pages = {113802},
numpages = {11},
year = {2025},
month = {Nov},
publisher = {American Physical Society},
doi = {10.1103/sbz6-btz8},
url = {https://link.aps.org/doi/10.1103/sbz6-btz8}
}