# I-ReaxFF

**Repository Path**: fenggo/I-ReaxFF

## Basic Information

- **Project Name**: I-ReaxFF
- **Description**: I-ReaxFF: stand for Intelligent-Reactive Force Field
- **Primary Language**: Python
- **License**: AGPL-3.0
- **Default Branch**: master
- **Homepage**: https://fenggo.gitee.io/
- **GVP Project**: No

## Statistics

- **Stars**: 13
- **Forks**: 6
- **Created**: 2020-05-01
- **Last Updated**: 2026-04-13

## Categories & Tags

**Categories**: machine-learning

**Tags**: None

## README

# I-ReaxFF: stands for Intelligent-Reactive Force Field

- I-ReaxFF is a differentiable ReaxFF framework based on TensorFlow, with which we can get the first and higher-order derivatives of energies, and also can optimize **ReaxFF** and **ReaxFF-nn** (Reactive Force Field with Neural Networks) parameters with integrated optimizers in TensorFlow.

---

* ffield.json: the parameter file from machine learning
* reaxff_nn.lib  the parameter file converted from ffield.json for usage with GULP

## Installation
 The following package needs to be installed
1. TensorFlow, pip install tensorflow --user or conda install tensorflow
2. Numpy, pip install numpy --user
3. matplotlib, pip install matplotlib --user

Install this package after downloading this package and run the command in the shell in the I-ReaxFF root directory ``` pip install . --user ```. 
or using a command with editable mode: 
```shell
pip install . -e
```
Alternatively, this package can be installed without downloading the package through pip
``` pip install --user irff ```.

## Usage

1. Generating a dataset by DFT calculations
2. Prepare the parameter file 'ffield.json' 
3. Train the model

## Citation
1. Feng Guo et al., Intelligent-ReaxFF: Evaluating the reactive force field parameters with machine learning, Computational Materials Science 172, 109393, 2020. 

2. Feng Guo et al., ReaxFF-MPNN machine learning potential: a combination of reactive force field and message passing neural networks, Physical Chemistry Chemical Physics, 23, 19457-19464, 2021.

3. Feng Guo et al., ReaxFF-nn: A Reactive Machine Learning Potential in GULP/LAMMPS and the Applications in the Thermal Conductivity Calculations of Carbon Nanostructures, Physical Chemistry Chemical Physics, 27, 10571-10579, 2025.

### Use ReaxFF-nn with LAMMPS:
https://gitee.com/fenggo/ReaxFF-nn_for_lammps

https://github.com/fenggo/ReaxFF-nn_for_lammps