Dale Gaines II's Projects
High throughput workflows and automation for HPC
Crystal graph convolutional neural networks for predicting material properties.
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Contains pre-commit hooks (black, isort) for auto-formatting python code
Phonon code
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.
A suite of computational materials science tools.
quacc is a flexible and extensible platform for high-throughput computational materials science and quantum chemistry.
Automatically run VASP relaxation, static, bulk moduli, and elastic calculations
A work flow to run a series of DFT calculations automatically on HPCC.