Incorporating Computational Chemistry into Introductory Courses with a No-cost, Installation-free Molecular Dynamics Activity in Python
Abstract
Molecular simulations have become indispensable in applications ranging from drug discovery to materials design and battery technology, yet students are typically not exposed to these techniques until upper-level physical chemistry courses towards the end of their undergraduate education. This late exposure to computational chemistry can limit opportunities for students to develop proficiency or participate in faculty-led computational chemistry research. Although Python-based molecular dynamics (MD) activities are increasingly incorporated into physical chemistry laboratories, they require students to simultaneously learn challenging chemistry concepts and computer programming, creating a substantial cognitive load. This work introduces computational chemistry earlier in the curriculum by integrating Python-controlled and Pythonanalyzed MD simulations into instruction on the ideal gas law. Designed for introductory chemistry, the activity is appropriate for first-year undergraduate and advanced high school students. Students completing this activity first learn foundational Python concepts before applying them to ideal gas law calculations and molecular simulations using the LAMMPS molecular dynamics package. The scaffolded activity progresses from basic Python programming to gas-law calculations and finally to analyzing Lennard-Jones simulations, allowing students to manipulate simulation parameters and compare real and ideal gas behavior while exploring the assumptions underlying the ideal gas model. The effectiveness of the activity was evaluated using anonymous preand post-activity surveys. All instructional materials are provided as publicly available Google Colab notebooks, enabling students to perform molecular simulations entirely through a web browser without software installation or specialized hardware. Because the simulations are run on Google’s freely available cloud computing infrastructure, the activities are freely accessible and can be completed on low-cost devices such as Google Chromebooks. Comparison of pre-and post-activity survey responses suggests that introducing computational chemistry through an accessible, scaffolded activity can increase students’ confidence in gas-law concepts, reduce barriers to computational chemistry, and stimulate interest in computational methods.
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BibTeX
@misc{huynh2026incorporating,
title = {{Incorporating Computational Chemistry into Introductory Courses with a No-cost, Installation-free Molecular Dynamics Activity in Python}},
author = {Minh Hoang Huynh and Bailee Rusconi and Srinitha Sridharan and Abhinav Iyer and Gianmarc Grazioli},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008365/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008365/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Incorporating Computational Chemistry into Introductory Courses with a No-cost, Installation-free Molecular Dynamics Activity in Python
AU - Minh Hoang Huynh
AU - Bailee Rusconi
AU - Srinitha Sridharan
AU - Abhinav Iyer
AU - Gianmarc Grazioli
PY - 2026
DA - 2026/09/06
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008365/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008365/v1
AB - Molecular simulations have become indispensable in applications ranging from drug discovery to materials design and battery technology, yet students are typically not exposed to these techniques until upper-level physical chemistry courses towards the end of their undergraduate education. This late exposure to computational chemistry can limit opportunities for students to develop proficiency or participate in faculty-led computational chemistry research. Although Python-based molecular dynamics (MD) activities are increasingly incorporated into physical chemistry laboratories, they require students to simultaneously learn challenging chemistry concepts and computer programming, creating a substantial cognitive load. This work introduces computational chemistry earlier in the curriculum by integrating Python-controlled and Pythonanalyzed MD simulations into instruction on the ideal gas law. Designed for introductory chemistry, the activity is appropriate for first-year undergraduate and advanced high school students. Students completing this activity first learn foundational Python concepts before applying them to ideal gas law calculations and molecular simulations using the LAMMPS molecular dynamics package. The scaffolded activity progresses from basic Python programming to gas-law calculations and finally to analyzing Lennard-Jones simulations, allowing students to manipulate simulation parameters and compare real and ideal gas behavior while exploring the assumptions underlying the ideal gas model. The effectiveness of the activity was evaluated using anonymous preand post-activity surveys. All instructional materials are provided as publicly available Google Colab notebooks, enabling students to perform molecular simulations entirely through a web browser without software installation or specialized hardware. Because the simulations are run on Google’s freely available cloud computing infrastructure, the activities are freely accessible and can be completed on low-cost devices such as Google Chromebooks. Comparison of pre-and post-activity survey responses suggests that introducing computational chemistry through an accessible, scaffolded activity can increase students’ confidence in gas-law concepts, reduce barriers to computational chemistry, and stimulate interest in computational methods.
ER -