Abstract Chemical kinetics is a fundamental component of physical chemistry courses,in which the extraction of rate constants and the modeling of kinetic equations represent core challenges in teaching.Taking the acid-catalyzed hydrolysis of sucrose as a case study,this work designs a three-tier modeling framework based on Python:traditional linear fitting,neural network,and symbolic regression.These methods respectively highlight intuitiveness,fitting accuracy,and explainability,enabling students to understand the modeling process from multiple perspectives and progressively deepen their analytical thinking.This teaching case has been integrated into the undergraduate general elective course Fun Machine Learning at Hebei University.Feedback from implementation indicates that students have effectively mastered the basic modeling workflow,gained insights into the characteristics and applicability of different algorithms,and significantly improved their interdisciplinary competence and data literacy.The proposed design establishes an organic integration of theoretical knowledge,experimental data,and modeling techniques,offering a transferable and practical case study for curriculum reform under the framework of “Chemistry+Artificial Intelligence”.
ZHANG Zhao-Sheng, HUO Shu-Ying, ZHANG Hong, SUN Su-Fang, MA Hai-Yun, MA Jing. Machine Learning-Based Teaching Design for Reaction Kinetics Modeling: Case Study of Sucrose Hydrolysis Experiment[J]. Chinese Journal of Chemical Education, 2026, 47(2): 114-122.
