机器学习在计算化学教学中的创新应用——钙钛矿材料高通量筛选研究

Innovative Application of Machine Learning in Computational Chemistry Education:High-Throughput Screening Study on Perovskite Materials

本教学案例结合XGBoost和LightGBM两种先进的机器学习算法,探索其在钙钛矿材料热稳定性和带隙高通量筛选中的应用,旨在将计算化学与数据科学的交叉融合引入化学教学中。通过实际案例分析,不仅加深了学生对计算化学基础理论的理解,也提升了他们运用机器学习技术解决化学问题的能力,促进了跨学科的思维和技能培养。此外,通过SHAP分析增强了模型预测的可解释性,为学生提供了理解和掌握前沿科技的宝贵机会,为其科研和职业发展打下坚实基础。

This teaching case explores the application of advanced machine learning algorithms,XGBoost and LightGBM,in the high-throughput screening of perovskite materials for thermal stability and bandgap,aiming to integrate the intersection of computational chemistry and data science into chemical education.Through practical case analysis,this article not only deepens students'understanding of the basic theories of computational chemistry but also enhances their ability to apply machine learning technologies to solve chemical problems,promoting interdisciplinary thinking and skill cultivation.Additionally,SHAP analysis is utilized to enhance the interpretability of model predictions,providing students with valuable opportunities to understand and master cutting-edge technologies,and laying a solid foundation for their research and professional development.