Data repository for the publication: Kazuki Morita, Daniel W. Davies, Keith T. Butler, and Aron Walsh Modelling the dielectric constants of crystals using machine learning

Background

This work is mainly composed of two parts: machine learning and SHAP analysis. Overall aim is to extract physically and chemically meaningful trends from trained machine learning model.

Contents

Setting up Docker environment

• build docker image docker build --rm -t mlshap .
• run container (this will run jupyter lab directly) docker run -it -p 8080:8080 --name mlshap mlshap
• port 8080 is used by default

Notebooks

Steps 1: Machine learning

• Train a support vector regression (SVR) model
• Check the performance as done in the paper

Step 2: Shapley additive explanation analysis

• analyse the model and its prediction using shapley additive explanations(SHAP)

Data

All the required dataset is in the dataset directory.

Dependencies

The notebooks and the dataset make use of many Python packages:

• matplotlib
• numpy
• pandas
• pymatgen
• scikit-learn
• SHAP
• SMACT

The jupyter notebooks and the datasets follow the copyrights of above packages. For specific package versions see the Dockerfile.

Caveats

• The dataset was downloaded from Materials Project on March 2020 and it may differ from the current version of the database.
• Docker should reproduce the result to a high degree, but the result may slightly differ from the main paper.
• Many different libraries are used and I am not an expert in all of them: some of the code is probably far from elegant!