yliu1240

Learning_from_models

The analyses applied on high-dimensional performance data of machine learning interatomic potentials (MLIPs), described in 'Learning from models: high-dimensional analyses on the performance of MLIPs'

Li-Al_MLIP_datasets

Eight datasets (including two training datasets, two validation datasets, and four testing datasets) for the publication "Assessing the accuracy of MLIPs in predicting the elemental ordering: a case study on Li-Al alloys"

Silicon_MLIP_datasets

Training, validation, and testing datasets for the publication "The discrepancies, the extrapolability, and the interpolability of machine learning interatomic potentials on simulating atomic dynamics"

Topological_Analysis

Topological analysis for Li local space, site, pathway in crystal structures

postqe

qeschema

West

WEST code

MechElastic

This python scripts can be used to calculate some important physical properties such as elastic moduli, melting temperature, Debye temperature, elastic wave velocities, and elastic anisotropy for all crystalline systems using the VASP output data for elastic tensor calculation. It can also be used to test the mechanical stability of any bulk system. It reads the elastic matrix written in the OUTCAR file. If not provided by user, this script will read the crystal type from the OUTCAR file.

yliu1240/MechElastic

This python scripts can be used to calculate some important physical properties such as elastic moduli, melting temperature, Debye temperature, elastic wave velocities, and elastic anisotropy for all crystalline systems using the VASP output data for elastic tensor calculation. It can also be used to test the mechanical stability of any bulk system. It reads the elastic matrix written in the OUTCAR file. If not provided by user, this script will read the crystal type from the OUTCAR file.