基于机器学习势函数的理论模拟在本科生教学实验课设计中的应用  

Application of Machine Learning Potential-based Theoretical Simulations in Undergraduate Teaching Laboratory Course Design

作　　者：谢忻恬 马思聪 李晔飞 商城 刘智攀 Xintian Xie;Sicong Ma;Yefei Li;Cheng Shang;Zhipan Liu(Collaborative Innovation Center of Chemistry for Energy Material,Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials,Key Laboratory of Computational Physical Science,Ministry of Education,Department of Chemistry,Fudan University,Shanghai 200433,China;State Key Laboratory of Organometallic Chemistry,Shanghai Institute of Organic Chemistry,Chinese Academy of Sciences,Shanghai 200032,China.)

机构地区：[1]复旦大学化学系,能源材料化学协同创新中心,上海市分子催化和功能材料重点实验室,计算物质科学教育部重点实验室,上海200433 [2]中国科学院上海有机化学研究所,金属有机化学国家重点实验室,上海200032

出　　处：《大学化学》2025年第3期140-147,共8页University Chemistry

基　　金：国家自然科学基金(12188101,22033003,22122301,91945301,91745201);中央高校基本科研基金(20720220011);腾讯基金会科学探索奖。

摘　　要：为了更好地培养新时代化学人才,在本科生教育体系中设置相关模拟上机课程具有重要意义。本文以本课题组开发的模拟方法和软件为例,设计了两个模拟实验,即“H_(2)在Cu(111)表面解离反应势能面的构建”和“沸石分子筛表面酸性表征及模拟”,旨在加深化学与材料学科本科生对理论模拟的理解,感受人工智能技术带来的巨大进步。Integrating theoretical simulation courses into undergraduate education for chemistry and materials science is of great significance for cultivating modern chemistry talents.Using the simulation methods and software developed by our research group,we designed two simulation experiments:"Construction of the Potential Energy Surface for H_(2) Dissociation on the Cu(111)Surface"and"Characterization and Simulation of Acidity on Zeolite Molecular Sieve Surfaces".These experiments aim to deepen the undergraduates’comprehension of theoretical simulations and highlight the transformative advancements driven by artificial intelligence technology.

关 键 词：理论模拟实验 人工智能 势能面搜索 

分 类 号：G64[文化科学—高等教育学] O6[文化科学—教育学]