HydraGNN - GRSI 2023

Branch for documentation and experimental progress for the application of physics informed message passing layers to material property prediction in metal alloys and inorganic compounds done by Justin Baker as a part of the Graduate Research Student Internship over the Summer of 2023.

HydraGNN

Distributed PyTorch implementation of multi-headed graph convolutional neural networks

Dependencies

To install required packages with only basic capability (torch, torch_geometric, and related packages) and to serialize+store the processed data for later sessions (pickle5):

pip install -r requirements.txt
pip install -r requirements-torchdep.txt

If you plan to modify the code, include packages for formatting (black) and testing (pytest) the code:

pip install -r requirements-dev.txt

Detailed dependency installation instructions are available on the Wiki

Installation

After checking out HydgraGNN, we recommend to install HydraGNN in a developer mode so that you can use the files in your current location and update them if needed:

python -m pip install -e .

Or, simply type the following in the HydraGNN directory:

export PYTHONPATH=$PWD:$PYTHONPATH

Alternatively, if you have no plane to update, you can install HydraGNN in your python tree as a static package:

python setup.py install

Running the code

There are two main options for running the code; both require a JSON input file for configurable options.

Training a model, including continuing from a previously trained model using configuration options:
```
import hydragnn
hydragnn.run_training("examples/configuration.json")
```

Making predictions from a previously trained model:

import hydragnn
hydragnn.run_prediction("examples/configuration.json", model)

Datasets

Built in examples are provided for testing purposes only. One source of data to create HydraGNN surrogate predictions is DFT output on the OLCF Constellation: https://doi.ccs.ornl.gov/

Detailed instructions are available on the Wiki

Configurable settings

HydraGNN uses a JSON configuration file (examples in examples/):

There are many options for HydraGNN; the dataset and model type are particularly important:

["Verbosity"]["level"]: 0, 1, 2, 3, 4
["Dataset"]["name"]: CuAu_32atoms, FePt_32atoms, FeSi_1024atoms
["NeuralNetwork"]["Architecture"]["model_type"]: PNA, MFC, GIN, GAT, CGCNN, SchNet, DimeNet

Citations

"HydraGNN: Distributed PyTorch implementation of multi-headed graph convolutional neural networks", Copyright ID#: 81929619 https://doi.org/10.11578/dc.20211019.2

Contributing

We encourage you to contribute to HydraGNN! Please check the guidelines on how to do so.

JustinBakerMath/HydraGNN