Interpretable machine learning for polymer thermal conductivity (IMLforPTC) prediction via physical feature engineering
An interpretable machine learning framework for exploring high thermal conductivity polymer structures and extracted the chemical heuristic for high thermal conductivity polymers design through underlying mechanism analysis. The physical features engineering approach is applicable not only to organic thermal functional materials, but also for the discovery of high-performance polymers with other desired properties. Please refer to our work "Exploring High Thermal Conductivity Polymers via Interpretable Machine Learning with Physical Descriptors " for additional details.
To download, clone this repository
git clone https://github.com/SJTU-MI/IMLforPTC.git
To run most code in this repository, the relevant anaconda environment can be installed from environment.yml. To build this environment, run
cd ./IMLforPTC
conda env create -f environment.yml
conda activate imlforptc
However, some large data files are downloaded from external release repositories and a separate Mol2vec environment is required to represent the molecular structures. To build this environment, run
wget -i file.txt
chmod 777 file.sh
./file.sh
pip install git+https://github.com/samoturk/mol2vec
main: Core code implementation
Data01-descriptor_Statisticalselection: Process files generated by different down-selection stages for physical descriptors
Data02-different_descriptors: Optimized descriptors, graph descriptors of Morgan fingerprint, MACCS and Mol2vec, 2D vectors converted from polymer chemical structures by UMAP
Data03-ML_models: Trained machine learning models
Data04-model_accuracy: Accuracy statistics of machine learning models with different representations or physical descriptors at various down-selection stages
Data05-Polymer_data: PolyInfo (datasteA) and PI1M (datasteB) polymer datasets
01feature_engineering.ipynb: Feature engineering for down-selection of physical descriptors
02_1graph_descriptor.ipynb: Generation of morgan, morgan counts and MACCS fingerprints using RDkit
02_2Mol2vec.ipynb: Generation of Mol2vec fingerprints
02_3Similarity_plot.ipynb: Visualization of polymer data distribution in a 2D space by UMAP. The polymer structure is first transformed into a two-dimensional vector by UMAP_for_polymer.py
03_1ML_optimized_descriptor.ipynb: Machine learning models training with optimized descriptors
03_2ML_Mol2vec.ipynb: Machine learning models training with Mol2vec descriptors
03_3ML_MACCS.ipynb: Machine learning models training with MACCS descriptors
03_4ML_Morgan.ipynb: Machine learning models training with Morgan descriptors
03_5ML_cMargan.ipynb: Machine learning models training with Morgan counts descriptors
03_6ML_PCA.ipynb: Machine learning models training with PCA-based descriptors
04SHAP_RF.ipynb: Feature importance analysis by SHAP with RF model
05TC_Prediction.ipynb: Virtual screening of high thermal conductivity polymers in PolyInfo and PI1M datasets
06Model_accuracy.ipynb: Plotting of model accuracy corresponding to different down-selection stages or different representations
| AUTHORS | Xiang Huang, Shengluo Ma, Shenghong Ju |
|---|---|
| VERSION | 1.0 / January,2023 |
| EMAILS | shenghong.ju@sjtu.edu.cn |
- X. Huang, S. Ma, C.Y. Zhao, H. Wang, S. Ju, "Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors," npj computational materials (2023) [Link].
This work is under BSD-2-Clause License. Please, acknowledge use of this work with the appropiate citation to the repository and research article.