摘要
Lithium manganese silicate(Li-Mn-Si-O)cathodes are key components of lithium-ion batteries,and their physical and mechanical properties are strongly influenced by their underlying crystal structures.In this study,a range of machine learning(ML)algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory(DFT)data obtained from the Materials Project database.The dataset comprised 211 compositions characterized by key descriptors,including formation energy,energy above the hull,bandgap,atomic site number,density,and unit cell volume.These features were utilized to classify the materials into monoclinic(0)and triclinic(1)crystal systems.A comprehensive comparison of various classification algorithms including Decision Tree,Random Forest,XGBoost,Support VectorMachine,k-Nearest Neighbor,Stochastic Gradient Descent,Gaussian Naive Bayes,Gaussian Process,and Artificial Neural Network(ANN)was conducted.Among these,the optimized ANN architecture(6–14-14-14-1)exhibited the highest predictive performance,achieving an accuracy of 95.3%,aMatthews correlation coefficient(MCC)of 0.894,and an F-score of 0.963,demonstrating excellent consistency with DFT-predicted crystal structures.Meanwhile,RandomForest and Gaussian Processmodels also exhibited reliable and consistent predictive capability,indicating their potential as complementary approaches,particularly when data are limited or computational efficiency is required.This comparative framework provides valuable insights into model selection for crystal system classification in complex cathode materials.
基金
supported by the Learning&Academic Research Institution for Master’s,PhD students,and Postdocs LAMP Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(No.RS-2023-00301974)
This work was also supported by the Glocal University 30 Project fund of Gyeongsang National University in 2025.
