大学化学2025，Vol.40Issue(9)：69-75,7.DOI:10.12461/PKU.DXHX202411067
高通量计算与机器学习相结合的综合计算化学实验设计与实践
Design and Practice of a Comprehensive Computational Chemistry Experiment Based on High-Throughput Computation and Machine Learning
摘要
Abstract
With the rapid advancements in computational chemistry and artificial intelligence technologies,their integration into chemical education has become increasingly vital.This experimental course is specifically tailored for senior undergraduate and graduate chemistry students,providing a comprehensive platform that merges computational chemistry with machine learning methodologies to explore the bond dissociation energies of organic compounds in depth.The curriculum covers fundamental principles and operational techniques of quantum chemical calculation methods,as well as the construction,training,and validation of machine learning models.Through hands-on experience,students will learn to utilize advanced computational tools and algorithms to predict and analyze chemical bond energies,thereby deepening their understanding of chemical reaction mechanisms.The objective of the course is to equip students with proficient data processing and analysis skills,empowering them to independently apply these skills to research chemical problems,thus establishing a strong foundation for future scientific endeavors or interdisciplinary explorations.关键词
键解离能/计算化学/大数据/机器学习/SMILESKey words
Bond dissociation energy/Computational chemistry/Big data/Machine learning/SMILES分类
社会科学引用本文复制引用
周佳..高通量计算与机器学习相结合的综合计算化学实验设计与实践[J].大学化学,2025,40(9):69-75,7.基金项目
哈尔滨工业大学深圳校区质量工程项目(高等教育教学改革项目)(HITSZERP22009) （高等教育教学改革项目）
哈尔滨工业大学深圳校区思政课程和课程思政专项课题(HITSZIP22017) （HITSZIP22017）
