FastTrack

Introduction

we have demonstrated a fast, accurate, and flexible framework for predicting atomic diffusion barriers in crystalline solids by integrating universal machine‐learning force fields with three‐dimensional potential‐energy‐surface sampling and interpolation.

Download / Installation

Install from source:

git clone https://github.com/atomly-materials-research-lab/FastTrack.git
cd FastTrack
vim FastTrack/config.py
pip install .

Set your ML force field parameters in config.py

(Optional) Development install:

pip install -e  .

Usage Example

A minimal example to get users started quickly.

Specify the machine learning force field in FastTrack/config.py, including the model and parameter paths.

from FastTrack import kkk  

barrier_energy = kkk("LiFePO4.cif",'Li',1)   #maximum lithiation limit
#or
barrier_energy = kkk("LiFePO4.cif",'Li',0)   #maximum delithiation limit

Citation

If you use this repository in your research, please cite the original work:

@article{Kang2025FastTrack,
  title   = {FastTrack: A fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential},
  author  = {Kang, Hanwen and Lu, Tenglong and Qi, Zhanbin and Guo, Jiandong and Meng, Sheng and Liu, Miao},
  journal = {AI for Science},
  volume  = {1},
  pages   = {015004},
  year    = {2025},
  publisher = {IOP Publishing},
  doi     = {10.1088/3050-287X/ae0808},
  url     = {https://doi.org/10.1088/3050-287X/ae0808}
}