Zhiqiang Li's repositories
30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
ai2-kit
A toolkit featured artificial intelligence × ab initio for computational chemistry research.
al-folio
A beautiful, simple, clean, and responsive Jekyll theme for academics
aml
A... M... L...
deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
DOS-VACF-LAMMPS
Python script for calculating vibrational density of states (DOS) from LAMMPS dump file
dpgen
The deep potential generator
GPUMD
Graphics Processing Units Molecular Dynamics
lammps
Lammps input files
lizhiqiang100
Config files for my GitHub profile.
material_simulation_scripts
Useful scripts for material simulation software & pkgs
modecode
Massively parallel vibrational mode calculator.
Mold
Lammps package including square-like well potential to implement Mold integration and Lattice mold techniques
potentials-library
Complete library directory for the potentials Python package, i.e. contains copies of records from potentials.nist.gov
psiflow
Interatomic potential development library
qm9pack
A Python package for data-mining the QM9 dataset
shc-python-tools
Python tools for calculating the spectral heat current distribution from LAMMPS NEMD simulations
somd
Molecular dynamics package designed for the SIESTA DFT code.
study
leaninging
TENSOAP
Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR)
TiO2-Water
This repo contains the training data used to train a Deep Neural Network Potential for the TiO2-Water interface
tools-and-data
A collection of tools and databases for atomistic machine learning
water_ice_nep
Supplement files of paper "Thermodynamics of Water and Ice from a Fast and Scalable First-Principles Neuroevolution Potential"