Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction

This repository contains the code and data for the paper Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction by Karpovich et al.

Installation Instructions

• Clone this repository and navigate to it.
• Create the conda enivornment for the regression tasks. conda env create --name regression_env --file requirements_regression.txt
• Create the conda enivornment for the CVAE tasks. conda env create --name CVAE_env --file requirements_CVAE.txt
• Switch to the new environment, depending on which notebook you are running. conda activate <env_name>
• Add the environment to jupyter and activate it python -m ipykernel install --name <env_name>

Data

The full datasets used in the paper are available online. Data must be downloaded to an appropriate data folder before and preprocessed before any of the notebooks can be run. The data used in this work is from the following papers:

• Kononova, O., Huo, H., He, T., Rong Z., Botari, T., Sun, W., Tshitoyan, V. and Ceder, G. Text-mined dataset of inorganic materials synthesis recipes. Sci Data 6, 203 (2019). (https://doi.org/10.1038/s41597-019-0224-1)
  • Github link to dataset: (https://github.com/CederGroupHub/text-mined-synthesis_public)
• Wang, Z., Kononova, O., Cruse, K., He, T., Huo, H., Fei, Y., Zeng, Y., Sun, Y., Cai, Z., Sun, W. and Ceder, G. Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature. Sci Data 9, 231 (2022). (https://doi.org/10.1038/s41597-022-01317-2)
  • Github link to dataset: (https://github.com/CederGroupHub/text-mined-solution-synthesis_public)

Usage

Each folder pertains to a particular task (synthesis route classification or synthesis condition prediction) containing the associated Jupyter notebooks and python code.

• The rxn_classification folder contains the necessary code for the reaction classification tasks.
• The rxn_condition_prediction folder contains the necessary code for the reaction conditions prediction tasks.
  • The CVAE folder contains the necessary code for training and evaluating the conditional variational autoencoder model for condition prediction.
  • The regression folder contains the necessary code for training and evaluating the regression models for condition prediction.
• The data_extraction_featurization folder contains the necessary code for extracting the data and featurizing it for ML modeling.
• The supplementary_data folder contains data used for data extraction and preprocessing.

Cite

If you use this dataset or code in your work please cite as:

@article{doi:10.1021/acs.chemmater.2c03010,
author = {Karpovich, Christopher and Pan, Elton and Jensen, Zach and Olivetti, Elsa},
doi = {10.1021/acs.chemmater.2c03010},
journal = {Chemistry of Materials},
number = {3},
pages = {1062--1079},
title = {{Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction}},
url = {https://doi.org/10.1021/acs.chemmater.2c03010},
volume = {35},
year = {2023}
}

Disclaimer

This is research code shared without support or guarantee of quality. Please report any issues found by opening an issue in this repository.