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This repository is the official code repository for the paper "General reactive machine learning potentials for CHON elements," providing the relevant DPA-3 models, usage instructions, and scripts.

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General reactive machine learning potentials for CHON elements

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


How to Cite

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}
}

DPA-3 Environment Setup and Model Usage

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.

1. Environment Setup

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.

Environment Activation and Configuration

  1. 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
  2. The default environment does not include the ASE and xtb-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

2. Model Usage

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 $\Delta$-Learning model, and its use requires combination with GFN2-xTB calculations. To use it with the ASE interface in Python, a helper script (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."

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