This project is qualified for participation in the "National Pioneer Cup on Intelligent Computing – Shandong University of Science and Technology Selection

Cu-Sn Machine Learning Interatomic Potential

This repository contains a machine learning interatomic potential (MLIP) model for the Cu-Sn alloy system, developed using the Deep Potential Generator (DP-GEN) and DeepMD-kit frameworks. The trained model is provided as frozen_model.pb.

Using this DP model, we performed molecular dynamics (MD) simulations to compute the energy–volume (E–V) curve, elastic moduli, and phonon spectra of Cu-Sn compounds. The simulation results show excellent agreement with density functional theory (DFT) calculations, demonstrating that the developed model achieves DFT-level accuracy while maintaining significantly higher computational efficiency.

Software Used

DP-GEN: Automated active learning workflow for training interatomic potentials

DeepMD-kit: Neural network potential training package

LAMMPS: Molecular dynamics engine for simulation

Repository Structure

├── result # MD results: energy–volume curves, phonon spectra

│ ├── E-V.jpg # Energy-Volume curve

│ └── Phonon dispersion relation.jpg # Phonon dispersion relation

├── frozen_model.pb # Final trained Deep Potential model

└── Research on Efficient Molecular Dynamics Simulation Methods Based on Machine Learning Potentials/ # Experimental report and summary