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.
Mei et al. (Wed,) studied this question.
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