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Citation: Jia Zhou. Design and Practice of a Comprehensive Computational Chemistry Experiment Based on High-Throughput Computation and Machine Learning[J]. University Chemistry, ;2025, 40(9): 69-75. doi: 10.12461/PKU.DXHX202411067 shu

Design and Practice of a Comprehensive Computational Chemistry Experiment Based on High-Throughput Computation and Machine Learning

Jia Zhou ^,

State Key Laboratory of Urban Water Resource and Environment, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong Province, China

Corresponding author: Jia Zhou, jiazhou@hit.edu.cn
Received Date: 19 November 2024
Accepted Date: 2 January 2025

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.
- Bond dissociation energy,
- Computational chemistry,
- Big data,
- Machine learning,
- SMILES
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