Qin Haobo, Zhang Yanchao, Guo Zhaofeng, Wang Shuhuan, Zhao Dingguo, Xue Yuekai
Department of Resources and Environmental Engineering, Hebei Vocational University of Technology and Engineering, Xingtai 054000, China.
College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063000, China.
Materials (Basel). 2024 Dec 19;17(24):6217. doi: 10.3390/ma17246217.
The bandgap is a critical factor influencing the energy density of batteries and a key physical quantity that determines the semiconducting behavior of materials. To further improve the prediction accuracy of the bandgap in silicon oxide lithium-ion battery materials, a boosting machine learning model was established to predict the material's bandgap. The optimal model, AdaBoost, was selected, and the SHapley Additive exPlanations (SHAP) method was used to quantitatively analyze the importance of different input features in relation to the model's prediction accuracy. It was found that AdaBoost performed exceptionally well in terms of prediction accuracy, ranking as the best among five predictive models. Using the SHAP method to interpret the AdaBoost model, it was discovered that there is a significant positive correlation between the energy of the conduction band minimum (cbm) of silicon oxides and the bandgap, with the bandgap size showing an increasing trend as the cbm rises. Additionally, the study revealed a strong negative correlation between the Fermi level of silicon oxides and the bandgap, with the bandgap expanding as the Fermi level decreases. This research demonstrates that boosting-type machine learning models perform superiorly in predicting the bandgap of silicon oxide materials.
带隙是影响电池能量密度的关键因素，也是决定材料半导体行为的关键物理量。为了进一步提高氧化硅锂离子电池材料带隙的预测精度，建立了一个增强机器学习模型来预测材料的带隙。选择了最优模型AdaBoost，并使用SHapley加法解释（SHAP）方法定量分析不同输入特征对模型预测精度的重要性。结果发现，AdaBoost在预测精度方面表现出色，在五个预测模型中排名最佳。使用SHAP方法解释AdaBoost模型时发现，氧化硅导带最小值（cbm）的能量与带隙之间存在显著正相关，随着cbm升高，带隙尺寸呈增加趋势。此外，研究还揭示了氧化硅费米能级与带隙之间存在很强的负相关，随着费米能级降低，带隙扩大。这项研究表明，增强型机器学习模型在预测氧化硅材料的带隙方面表现优异。
