Joe Morrow's Projects
Computational physics in python - https://github.com/rajeshrinet/compPhy
It's a Reverse Monte Carlo (RMC) package, designed with Artificial intelligence and Reinforcement Machine Learning algorithms to solve atomic/molecular model structure by moving its atoms positions until they have the greatest consistency with a set of experimental data and definitions.
Some tutorial-style examples for validating machine-learned interatomic potentials
Tutorial notebooks for the validation of machine learned interatomic potentials, to accompany arXiv:2211.12484 [physics.chem-ph]
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
Materials science with Python at the atomic-scale
PSP is a python toolkit for predicting atomic-level structural models for a range of polymer geometries.
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.
libAtoms/QUIP molecular dynamics framework: http://www.libatoms.org
Julia implementation of algorithm for counting primitive rings in an atomistic structure. Useful for materials simulations
This Python package is designed for mapping the solution space of machine learning models. An understanding of the organisation of the solution space can answer important questions about the reproducibility, explainability and performance of ML methods.
Accurate Neural Network Potential on PyTorch
Analysis code for the publication "Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning"