Citation: Guokui Liu, Li Li, Yifan Yang, Yaoyao Wei, Qiying Xia. 科教融合导向下的本科化学实验设计——基于机器学习预测分子偶极矩的探索[J]. University Chemistry, ;2026, 41(10): 370-378. doi: 10.12461/PKU.DXHX202510100 shu

科教融合导向下的本科化学实验设计——基于机器学习预测分子偶极矩的探索

Guokui Liu ¹ ,
Li Li ² ,
Yifan Yang ¹ ,
Yaoyao Wei ^1,, ,
Qiying Xia ^1,,

1.
School of Chemistry and Chemical Engineering, Linyi University, Linyi 276000, Shandong Province, China;
2.
College of Chemistry, Chemical Engineering and Resource Utilization, Northeast Forestry University, Harbin 150040, Heilongjiang Province, China

Corresponding author: Yaoyao Wei, Qiying Xia,
Received Date: 28 October 2025
Accepted Date: 26 November 2025

针对机器学习的飞速发展及其在各学科教学科研领域的重要应用，本文将机器学习融入物理化学中一个经典的实验“偶极矩的测定”。实验以正丁醇为模型分子，学生不仅要完成量子化学计算中的结构优化、势能面扫描和偶极矩计算，而且要进一步利用生成的量子化学数据集训练机器学习模型，实现对正丁醇偶极矩的快速准确预测。通过本实验，可以有效衔接本科教学与科研前沿，为学生普及机器学习这一强大工具，培养学生面向未来、跨学科的研究能力。
- Machine learning,
- Dipole moment,
- Computational chemistry,
- XGBoost
1. [1]
2. [2]
3. [3]
4. [4]
5. [5]
6. [6]
7. [7]
8. [8]
9. [9]
10. [10]
11. [11]
12. [12]
13. [13]
14. [14]
15. [15]
16. [16]
  M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, et. al., Gaussian 09, Revision D.01; Gaussian, Inc.: Wallingford CT, USA, 2013.
17. [17]
  R. Dennington, T. Keith, J. Millam, GaussView, Version 5; Semichem Inc.: Shawnee Mission, KS, USA, 2009.
18. [18]
  Anaconda, Inc. (2025). Anaconda Distribution (Version 25.7.0) [Computer software]. Anaconda, Inc. https://www.anaconda.com/
19. [19]
  R.D. LNelson Jr., D.R. Lide Jr., A.A. Maryott, Selected Values of Electric Dipole Moments for Molecules in The Gas Phase, Government Printing Office: Washington, DC, USA, 1967.
20. [20]
  M.N. Vo, M. Call, C. Kowall, J.K. Johnson, Ind. Eng. Chem. Res. 2019, 58(41), 19263.
1. [1]
  Jia Zhou . Design and Practice of a Comprehensive Computational Chemistry Experiment Based on High-Throughput Computation and Machine Learning. University Chemistry, 2025, 40(9): 69-75. doi: 10.12461/PKU.DXHX202411067
2. [2]
  Liuxie Liu , Jing He , Jiali Du , Shuang Mao , Qianggen Li . Extension of Computational Chemical-Assisted Dipole Moment Measurement Experiment. University Chemistry, 2025, 40(3): 363-370. doi: 10.12461/PKU.DXHX202407108
3. [3]
  Ruming Yuan , Laiying Zhang , Xiaoming Xu , Pingping Wu , Gang Fu , Bin Ren . 从微观结构到宏观极性：实验与计算协同解析偶极矩. University Chemistry, 2026, 41(9): 293-300. doi: 10.12461/PKU.DXHX202508054
4. [4]
  Juan Yang . From qualitative to quantitative taking dipole moment and polarizability as examples: a case of general chemistry in the Chemistry “101 Plan”. University Chemistry, 2026, 41(6): 18-23. doi: 10.12461/PKU.DXHX202605042
5. [5]
  Jia Zhou , Huaying Zhong . Experimental Design of Computational Materials Science Combined with Machine Learning. University Chemistry, 2025, 40(3): 171-177. doi: 10.12461/PKU.DXHX202406004
6. [6]
  Jia Zhou . Constructing Potential Energy Surface of Water Molecule by Quantum Chemistry and Machine Learning: Introduction to a Comprehensive Computational Chemistry Experiment. University Chemistry, 2024, 39(3): 351-358. doi: 10.3866/PKU.DXHX202309060
7. [7]
  Jingjie Rao , Wenwen Cai , Jiahui Zhao , Xu Yang , Ziyan Yan , Tianjin Zhang , Hang Zhang . Digital Exploration of Analytical Chemistry Experiments in the Context of Machine Learning and Big Data: A Case Study on Water Hardness Measurement. University Chemistry, 2026, 41(1): 276-288. doi: 10.12461/PKU.DXHX202504104
8. [8]
  Chao Zheng , Chengxi Sun , Runfeng Chen . Theoretical calculations of triplet state lifetimes and phosphorescence radiative rates in organic long-persistent luminescence molecules: Introducing a comprehensive computational chemistry experiment. University Chemistry, 2026, 41(6): 374-384. doi: 10.12461/PKU.DXHX202507082
9. [9]
  Jiali CHEN , Guoxiang ZHAO , Yayu YAN , Wanting XIA , Qiaohong LI , Jian ZHANG . Machine learning exploring the adsorption of electronic gases on zeolite molecular sieves. Chinese Journal of Inorganic Chemistry, 2025, 41(1): 155-164. doi: 10.11862/CJIC.20240408
10. [10]
  Xinghai Li , Zhisen Wu , Lijing Zhang , Shengyang Tao . Machine Learning Enables the Prediction of Amide Bond Synthesis Based on Small Datasets. Acta Physico-Chimica Sinica, 2025, 41(2): 100010-0. doi: 10.3866/PKU.WHXB202309041
11. [11]
  Hao Wu , Fengqi Li , Xinwei Shi , Haifeng Bian , Qing Zhou , Shunshun Jia , Yujie Ma , Jian Gu , Jingzi Zhang , Shuijian He , Xiangkang Meng . Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction. Acta Physico-Chimica Sinica, 2026, 42(8): 100227-0. doi: 10.1016/j.actphy.2025.100227
12. [12]
  Zuoyong Li , Haoxiang Tu , Mingwei Ding , Meijun Liu , Ting Yang . Innovative Teaching Reform Study on the Synthesis of Silver Nanoparticles Based on Machine Learning and Microfluidic Technology. University Chemistry, 2026, 41(1): 64-75. doi: 10.12461/PKU.DXHX202505088
13. [13]
  Lingyu Chang , Yanfang Lang , Yuyan Zhu , Jie Wang , Ying Guo , Die Wang , Peng Ding , Yueming Zhou , Zhixiang Gong , Shujuan Liu . Machine Learning-Optimized Microcolumn Ion Exchange Chromatography for Trace Arsenic Determination. University Chemistry, 2026, 41(1): 76-84. doi: 10.12461/PKU.DXHX202506023
14. [14]
  Heng Zhang , Ying Ma , Shiling Yuan . Machine Learning-based Prediction of Antifouling Performance in Polymer Materials: An Integrated Molecular Simulation Experiment. University Chemistry, 2026, 41(1): 346-353. doi: 10.12461/PKU.DXHX202506015
15. [15]
  Ying Liang , Yuheng Deng , Shilv Yu , Jiahao Cheng , Jiawei Song , Jun Yao , Yichen Yang , Wanlei Zhang , Wenjing Zhou , Xin Zhang , Wenjian Shen , Guijie Liang , Bin Li , Yong Peng , Run Hu , Wangnan Li . Machine learning-guided antireflection coatings architectures and interface modification for synergistically optimizing efficient and stable perovskite solar cells. Acta Physico-Chimica Sinica, 2025, 41(9): 100098-0. doi: 10.1016/j.actphy.2025.100098
16. [16]
  Weigang Zhu , Jianfeng Wang , Qiang Qi , Jing Li , Zhicheng Zhang , Xi Yu . Curriculum Development for Cheminformatics and AI-Driven Chemistry Theory toward an Intelligent Era. University Chemistry, 2025, 40(9): 34-42. doi: 10.12461/PKU.DXHX202412002
17. [17]
  Yueming Zhou , Xin Qiu , Xin Zhou , Xiaotian Wan , Mofan Zhang , Feng Li , Xinxin Shao , Peng Ding , Xizhen Liang . Intelligent Visualization of Potassium Dichromate Reflux Method for Determination of Chemical Oxygen Demand. University Chemistry, 2026, 41(1): 85-94. doi: 10.12461/PKU.DXHX202506021
18. [18]
  Songmei Ma , Ying Zhang , Gang Liu , Wenlong Xu . Comprehensive Experiment Teaching Exploration and Practice in Polymeric Materials Integrating Research-Driven Learning, Creativity-Enhanced Competency, and Science-Education Synergy: A Case Study of Machine Learning-Assisted Intelligent Handwriting Recognition System. University Chemistry, 2026, 41(1): 289-297. doi: 10.12461/PKU.DXHX202509083
19. [19]
  Ruming Yuan , Laiying Zhang , Gang Fu , Bin Ren . Refining and implementing a computational chemistry module in the chemistry “101 Plan” for chemistry measurement experiments. University Chemistry, 2026, 41(6): 204-210. doi: 10.12461/PKU.DXHX202503098
20. [20]
  Yanan Jiang , Yuchen Ma . Brief Discussion on the Electronic Exchange Interaction in Quantum Chemistry Computations. University Chemistry, 2025, 40(3): 10-15. doi: 10.12461/PKU.DXHX202402058

Get Citation

PDF

XML

Metrics

PDF Downloads(0)
Abstract views(147)
HTML views(10)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142