LAMMPS-DeePMD

This dataset includes:

01_VASP: Input files for density functional theory molecular dynamics simulations using VASP

02_DP-GEN: Input files for generating training datasets using DP-GEN

03_DeePMD: Input files and datasets for constructing neural network potentials using DeePMD

04_LAMMPS: Large-scale molecular dynamics simulations using LAMMPS with network potentials

05_OUTPUT: An example of solid-liquid coexisting molecular dynamics trajectory

Code availability:

All molecular dynamics simulations were carried out using the open-source LAMMPS code (https://www.lammps.org). 

Enhanced sampling is based on the open-source PLUMED library (https://www.plumed.org). 

These two codes are implemented in the open-source DeePMD-kit code (https://github.com/deepmodeling/deepmd-kit) for NNP construction. 

Active learning was performed using the open-source DP-GEN code (https://github.com/deepmodeling/dpgen). 

Density functional theory calculations were performed using VASP which is proprietary software available for purchase (at https://www.vasp.at).

本数据集包含以下内容：
01_VASP：使用VASP（VASP）进行密度泛函理论分子动力学模拟的输入文件
02_DP-GEN：使用DP-GEN（DP-GEN）生成训练数据集的输入文件
03_DeePMD：采用DeePMD（DeePMD）构建神经网络势所需的输入文件与数据集
04_LAMMPS：采用LAMMPS结合神经网络势开展大规模分子动力学模拟的相关文件
05_OUTPUT：固液共存分子动力学轨迹示例

代码可用性：
所有分子动力学模拟均通过开源代码LAMMPS（LAMMPS，https://www.lammps.org）完成。
增强采样流程依托开源库PLUMED（PLUMED，https://www.plumed.org）实现。
上述两款代码已集成至开源工具包DeePMD-kit（DeePMD-kit，https://github.com/deepmodeling/deepmd-kit）中，用于神经网络势（Neural Network Potential，简称NNP）的构建。
主动学习流程通过开源代码DP-GEN（DP-GEN，https://github.com/deepmodeling/dpgen）完成。
密度泛函理论计算采用商业软件VASP（VASP，https://www.vasp.at）完成，该软件需通过购买获取合法授权。