This work presents a scalable workflow for developing general and reliable reactive machine learning potentials for systems containing C, H, O, and N elements, achieving high accuracy and transferability for large-scale chemical simulations.
➡️ Read the full paper on ChemRxiv
If you use this work, please cite the following publication:
@article{li2025general,
title={General reactive machine learning potentials for CHON elements},
author={Li, Bowen and Mi, Sixuan and Xiao, Jin and Duo, Zhang and Shuwen, Zhang and Zhang, John and Wang, Han and Zhu, Tong},
year={2025}
}
This document outlines the steps to set up the deepmd-kit
environment and use the DPA-3 models. This guide is based on deepmd-kit-3.1.0
.
For detailed installation instructions, please refer to the official deepmd-kit
v3.1.0 release page: https://github.com/deepmodeling/deepmd-kit/releases/tag/v3.1.0.
-
After installation, activate the environment. By default, it is located in the
/home/xxx/deepmd-kit
directory.# Note: 'xxx' should be replaced with your actual user directory. source activate /home/xxx/deepmd-kit
-
The default environment does not include the
ASE
andxtb-python
packages, which must be installed manually.# Install ase pip install ase # Install xtb-python from the conda-forge channel conda install xtb-python -c conda-forge
There are two DPA-3 models available under the DFT tag: DPA-3-F@DFT.pt
and DPA-3-DF@DFT.pt
.
2.1. DPA-3-F@DFT.pt (Directly Trained Model)
This model is trained directly and can be used in Python through the ASE interface.
from deepmd.pt.utils.ase_calc import DPCalculator
from ase.io import read
# Initialize the calculator
calc = DPCalculator(model="DPA-3-F@DFT.pt", device='cuda')
# The remaining steps are consistent with other ASE calculators
data = read('test.xyz')
data.calc = calc
# Example calculations
energy = data.get_potential_energy()
forces = data.get_forces()
2.2. DPA-3-DF@DFT.pt ($\Delta$ -Learning Model)
This is a deepmd_xtb.py
) is needed.
import sys
# Add the directory containing 'deepmd_xtb.py' to the Python path.
# This example assumes the script is in '/home/xxx/ase_interface'.
sys.path.append('/home/xxx/ase_interface')
from deepmd_xtb import DP_XTB
# Initialize the calculator
calc = DP_XTB(model="DPA-3-DF@DFT.pt", device='cuda')
"Please note: The relevant model files will be released here after our paper is formally accepted."