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Introduction
miniGAP is a proxy application for molecular and materials property prediction using the Gaussian Process Approximation. This is code is meant to run in multiple architectures, such as many-core and accelerators.
Installation
This code could be installed within an conda
enviroment as:
conda env create -f environment.yml
Then the new environment is activated as:
conda activate minigap
Creating an custom kernel in Jupiter
conda activate minigap
python -m ipykernel install --user --name "minigap"
Dependencies:
- python >= 3.6
- dscribe
- SYCL compiler
- Tensorflow
- Tensorflow-probability
- GPflow
- scikit-learn
What is inside?
- data: Initial XYZ, sample trajectories, and downloaded material.
- code: Repo specific modules for training and creating the models.
- notebooks:
- results: Figures and models
- media: Assorted Images
Contributors
Contributions are always welcome. Contributors should fork this repository and submit a merge request for review of the code.
References
Dscribe
GAP
Copyright 2021 Argonne UChicago LLC