CompAugCycleGAN

📜 Paper: Predicting compositional changes of organic-inorganic hybrid materials with Augmented CycleGAN

⬇️ Download: Source code || Pretrained

🕛 Latest release: 2022/02/22

TOC

Python modules

There are three submodules:

1. cacgan.data contains classes used for processing amine-templated oxide data. atmo.csv is the csv form of ATMO dataset.
2. cacgan.analysis are functions for analyzing ATMO composition dataset
3. cacgan.gans is the main module for constructing Augmented CycleGAN. This is based on the Pytorch code of Augmented CycleGAN by Amjad Almahairi.

other functions include

• sinkhornOT.py is used for calculating earth mover's distance with user-defined distance matrix. This is based on the Sinkhorn algorithm implementation by Thomas Viehmann.
• settings.py contains parameters used in model training.

Scripts

• generate_dataset.py create composition datasets used for model training, by default these are two .pkl files in dataset: dataset_ab.pkl -- chemical compositions, dimset.pkl -- dimensionalities.
  • additionally, earth mover's distance matrix can be generated using the same script
• eval_tuned.py evaluates the pretrained/tuned model which can be downloaded from and placed in the tuned folder.
  • alternatively, use zenodo_get 10.5281/zenodo.5721355 in command line to download
• hpc_pbs folder contains code for hyperparameter tuning and submission script for HPC (PBS).
• tutorial.ipynb a notebook showing how to generate dataset and to train the augmented cycleGAN model.
• test_emd.py unittest for sinkhorn calculator (testing results against pyemd).
• test_layer unittest for layers used in models.

Dimensionality predictor

• Model tuning is done through the script dimpredictor/tune_models.py. Evaluations are done by dimpredictor/tuned.py.
• The scripts in dimpredictor/tandem are for connecting the composition generators to a dimensionality classifier.