Using the CGCNN transfer learning model to predict the voltages of Li, Na, K, Mg, Ca, Zn, Y, and Al-ion battery electrodes
The package provides all the files that are used in the article of "Accurately predicting voltage of electrode materials in metal-ion batteries using interpretable deep transfer learning"
The CGCNN model is provided by Xie Tian et.al (https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301). They also provide their model in github (http://github.com/txie-93/cgcnn).
Please cite the following work if you want to use this model.
@article{PhysRevLett.120.145301,
title = {Accurately predicting voltage of electrode materials in metal-ion batteries using interpretable deep transfer learning},
author = {Zhang Xiuying and Shen Lei},
journal = {},
volume = {},
issue = {},
pages = {},
numpages = {},
year = {2021},
month = {},
publisher = {},
doi = {},
url = {}
}The work of the CGCNN model is also suggested to cite when using this model.
url={https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301}This package requires:
If you are new to Python, the easiest way of installing the prerequisites is via conda. After installing conda, run the following command to create a new environment named cgcnn and install all prerequisites.
The files in this folder are just the same as the corresponding files in the CGCNN model that Xie Tian et.al give (http://github.com/txie-93/cgcnn).
We used files in this folder to train the model for the voltage prediction of Li-ion battery electrodes.
The files in this folder are used to have a visualization of the CGCNN model.
The embedding_features.py is to visualize the features from the embedding layer in the CGCNN model.
The local_voltage_plt.py is to visualize the local voltages, which are obtained after the three convolutional layers.
The element_features.csv and OMO_local.csv are the data files that are used in the embedding_features.py and local_voltage_plt.py respectively. The element_features_csv.py and OMO_local_csv.py are the corresponding codes to get the two csv data files.
The other files in this folder are the useful files in the embedding_features.py and the local_voltage_plt.py.
The files in this folder are the main file for the model training for the prediction of Na, K, Mg, Ca, Zn, Y, and Al-ion battery electrods voltages respectively.
Here are the trained models for the voltage predictions of the corresponding metal-ion battery electrodes.
The model_best.pth file is trained on Li-ion battery electrodes dataset. It can be used for the Li-ion battery electrode voltage prediction. It also used to predict the voltages for the Rb and Cs-ion battery electrodes.
The model_best_Na.pth file is trained on Na-ion battery electrodes dataset and also used for the Na-ion battery electrode voltage prediction.
The model_best_K.pth file is trained on K-ion battery electrodes dataset and also used for the K-ion battery electrode voltage prediction.
The model_best_Mg.pth file is trained on Mg-ion battery electrodes dataset and also used for the Mg-ion battery electrode voltage prediction.
The model_best_Ca.pth file is trained on Ca-ion battery electrodes dataset and also used for the Ca-ion battery electrode voltage prediction.
The model_best_Zn.pth file is trained on Zn-ion battery electrodes dataset and also used for the Zn-ion battery electrode voltage prediction.
The model_best_Y.pth file is trained on Y-ion battery electrodes dataset and also used for the Y-ion battery electrode voltage prediction.
The model_best_Al.pth file is trained on Al-ion battery electrodes dataset and also used for the Al-ion battery electrode voltage prediction.
This folder contains the required data for the model training and the corresponding code to get these data files.
Here are the SVR (Supporting Vector Regression), KRR (Kernel Ridge Regression), and RFR (Random Forest Regression) models that are used in our work.
A convenient web tool has been built for the voltage prediction of all the metal-ion battery electrodes. http://batteries.2dmatpedia.org/