Curriculum Construction of "Soft Matter and Molecular Simulation" Integrating Computational Physics and Artificial Intelligence in Graduate Education

Mei Feng; Tingting Sun; Yan Wang

doi:10.47297/wspiedWSP2516-250029.20250912

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Curriculum Construction of "Soft Matter and Molecular Simulation" Integrating Computational Physics and Artificial Intelligence in Graduate Education

Volume 9, Issue 12

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Curriculum Construction of "Soft Matter and Molecular Simulation" Integrating Computational Physics and Artificial Intelligence in Graduate Education
Curriculum Construction of "Soft Matter and Molecular Simulation" Integrating Computational Physics and Artificial Intelligence in Graduate Education
1.Department of Physics, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, 310023, China
2.Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Hangzhou, Zhejiang, 310008, China
Vol. 9, Issue 12, Pages: 130-133(2025)
Published：30 December 2025
DOI：10.47297/wspiedWSP2516-250029.20250912
Accepted：

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Feng Mei,Sun Tingting,Wang Yan.Curriculum Construction of "Soft Matter and Molecular Simulation" Integrating Computational Physics and Artificial Intelligence in Graduate Education[J].Journal of International Education and Development,2025,09(12):130-133.
DOI：

Feng Mei,Sun Tingting,Wang Yan.Curriculum Construction of "Soft Matter and Molecular Simulation" Integrating Computational Physics and Artificial Intelligence in Graduate Education[J].Journal of International Education and Development,2025,09(12):130-133. DOI： 10.47297/wspiedWSP2516-250029.20250912.

摘要

Abstract

Molecular simulation plays an increasingly important role in the study of soft matter and biomolecular systems

driven in part by the growing use of computational physics and artificial intelligence (AI). In response to the need for interdisciplinary training at the graduate level

a new course entitled Soft Matter and Molecular Simulation was developed and offered to physics graduate students at our university. The course is organized around statistical physics and soft matter theory

and systematically introduces molecular dynamics simulations

free energy perturbation methods

and selected AI-assisted approaches for molecular modeling and protein-related studies. A progressive teaching framework is employed to link theoretical instruction with hands-on computational practice and research-oriented examples. Based on classroom implementation and student learning activities

the course has been found to support the development of fundamental modeling skills and to help students gain a clearer understanding of interdisciplinary research workflows. The curriculum design and teaching practices described in this paper also reflect a broader attempt to align graduate physics education with emerging computational and intelligent research paradigms.

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