spin_gnn

Code to train a neural network to predict fractional atom spin. The order of operations here is

1. parse_spins.py: queries the databases and dumps the spins to csv files. I wasn't in a rush, so this script is slow. This took the better part of a weekend to run.
2. preprocess_inputs.py: Splits the data into training and testing datasets, and converts the SMILES inputs into tensorflow-encoded graph arrays. These inputs are stored in a tfrecords fileformat, an unfriendly but efficient binary format for storing training data. See the tensorflow docs if you're curious.
3. train_model.py: Actually defines and runs the GNN model. There's a couple oddities to this specific example, in that the atom spins should sum to 1, and there's a variable number of atoms per example. So I have to write a couple custom keras objects in loss.py.
4. gpu_submit.sh: submits the train_model.py calculation to run on an Eagle GPU node.