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Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093
Self-describing sparse tensor data format for atomistic machine learning and beyond
DFTB+ general package for performing fast atomistic simulations
simple GNN potential version 2
Efficient and easy to use fortran implementation of the Ewald summation method
Automation software for calculating anharmonic phonon properties
A tool for computing Raman Spectra from Molecular Dynamics
End-to-end platform for building machine learning interatomic potentials (MLIPs). Automates dataset generation, training, and validation with LLM-guided DFT setup and ASE-based analysis tools. Buil…
A high performace ReaxFF/AIMD trajectory analysis tool based on graph theory.
Python codes for calculation of polarization displacement vector in ferroelectric materials
Benchmarking machine learning interatomic potentials with Grüneisen parameter.
Heat-conductivity benchmark test for foundational machine-learning potentials
Assessment and Application of Universal Machine Learning Interatomic Potentials in Solid-State Electrolyte Research
Tutorials on atomic simulations related to my research
An AiiDA workflow that implements a fully automated active learning scheme to train a neural network interatomic potential
Brillouin zones and paths for dispersion calculations in Julia.
An open-source Python package for creating fast and accurate interatomic potentials.
Neural Network Force Field based on PyTorch
Fortran code for generating and predicting interfaces between two crystals.
Latex template for CUHK PhD Thesis
Tutorials and data necessary to reproduce the results of publication Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
End-To-End Molecular Dynamics (MD) Engine using PyTorch