Integrating crystal structure and numerical data for predictive models of lithium-ion battery materials: A modified crystal graph convolutional neural networks approach

Abstract

Machine learning (ML) are currently making significant impact and bringing tremendous opportunities in material science, and has proven to be an effective tool for accelerating the discovery and advancing the development of lithium-ion batteries materials. In this study, we propose a modified version of the crystal convolutional neural network (CGCNN) algorithm, namely mCGCNN. This deep learning model successfully integrates the crystal structure characteristics and the physical/chemical properties of materials, solving the data fusion problem of the current universal models in materials and significantly enhancing the efficiency and accuracy of gravimetric capacity prediction for lithium-ion batteries. In addition, the scale factors α and β are introduced to control the contribution of crystal structure and numerical data of materials to the model, which increases the flexibility and adjustability of the model. A large dataset is extracted from the Materials Project database and analyzed using various ML algorithms, including traditional ML models and deep learning models such as CGCNN and mCGCNN. In comparison to other models, mCGCNN achieves superior performance with MAE of 6.6 mAhg−1 and R2 value of 0.965 in test set. This outperforms the original CGCNN model (MAE: 42.5 mAhg−1, R2: 0.328), XGBoost model (MAE: 23.6 mAhg−1, R2: 0.842), and Random Forest model (MAE: 21.9 mAhg−1, R2: 0.844). Furthermore, the mCGCNN model is also applied to a classification task using the same dataset, achieving an AUC of 0.99 and a total accuracy of 95.5 %. The mCGCNN model not only provides a valuable tool for rapid and accurate screening of high-performance batteries using multi-source material data, but also has significant potential for the discovery of other functional materials, making it widely applicable.