Cloud-Based Teaching Tool of AlCu Band Gap Simulations Using GPAW: A Python-Driven Approach for Undergraduate Student
DOI:
https://doi.org/10.21009/1.11105Keywords:
Educational Teaching Tool, Material Simulation, AlCu, Density Functional Theory, GPAWAbstract
This work introduces a computational teaching module that leverages Python, Google Colab, and the GPAW package to simulate the electronic band structure of AlCu materials. While powerful, traditional Density Functional Theory (DFT) tools like Quantum ESPRESSO or VASP often present steep learning curves and software installation challenges. By contrast, GPAW operating within Python and its seamless integration with Google Colab provides a user-friendly, platform-independent environment for students to explore quantum simulations without local setup requirements. The simulation workflow is highly efficient, with key processes such as structure creation taking only 7 milliseconds, structural relaxation requiring 51.2 seconds, and band structure calculations completing in just 40 seconds. In this educational framework, students model AlCu and its doped variants, visualize band structures, and analyze changes in the electronic properties induced by doping. The approach supports active learning and reinforces core solid-state physics, quantum mechanics, and computational materials science topics. Sample notebooks, learning outcomes, and classroom integration strategies are presented, aiming to democratize access to DFT education through open-source, cloud-based tools.
References
Bravenec, A.D. & Ward, K.D. (2023). Interactive Python Notebooks for Physical Chemistry. Journal of Chemical Education, 100(2), pp. 933–940. doi: https://pubs.acs.org/doi/10.1021/acs.jchemed.2c00665
Bylaska, E.J. et al. (2024). Electronic Structure Simulations in The Cloud Computing Environment. Journal of Chemical Physics, 161( 15), p. 150902.
Cheng, R., Barik, T., Leung, A., Hohman, F., & Nichols, J. (2024). BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks. arXiv, doi: https://doi.org/10.48550/arXiv.2404.07387
Dejam, S. (2023). ZnO, Cu-doped ZnO, Al-doped ZnO and Cu-Al doped ZnO thin films: Advanced micro-morphology, crystalline structures and optical properties. Materials Science in Semiconductor Processing, 126, p. 105748. doi: https://doi.org/10.1016/j.rinp.2023.106209
Di Felice, R. et al. (2023). A Perspective on Sustainable Computational Chemistry Software Development and Integration. Journal of Chemical Theory and Computation, 19(20), pp. 7056–7076. doi: https://doi.org /10.1021/acs.jctc.3c00419
Elmansi, H., Mostafa, E., El-Sayed, M., & Qoura, A. (2019). Using a Computer-based Scaffolding Strategy to Enhance EFL Preparatory Stage Students' Reading Skills and Self-Regulation. Journal of Research in Curriculum, Instruction and Educational Technology, 5(1), pp. 111–134. Available at: https://www.researchgate.net/publication/338019866
Gholam, A. (2019). Inquiry-Based Learning: Student Teachers’ Challenges and Perceptions. Journal of Inquiry & Action in Education, 10(2), pp. 112–133. Available at: https://files.eric.ed.gov/fulltext/EJ1241559.pdf
Gu, J., Bai, J., Zhu, Y., Qin, Y., Gu, H., Zhai, Y. & Ma, P. (2016). First-principles study of the influence of doping elements on phase stability, crystal and electronic structure of Al₂Cu (θ) phase. Computational Materials Science, 111, pp. 328-333. doi: https://doi.org/10.1016/j.commatsci.2015.09.049
Hrubeš, J., Jaroš, A., Nemirovich, T., Teplá, M. & Petrželová, S. (2024). Integrating Computational Chemistry into Secondary School Lessons. Journal of Chemical Education, 101(6), pp. 2343–2353. doi: https://doi.org/10.1021/acs.jchemed.3c00908
Hüser, F., Olsen, T., & Thygesen, K. S. (2013). Quasiparticle GW calculations for solids, molecules, and 2D materials. Journal of Physics: Condensed Matter, 25(35), p. 353202. doi: https://doi.org/10.1103/PhysRevB.87.235132
Ju, F. et al. (2024). Acceleration without Disruption: DFT Software as a Service. Journal of Chemical Theory and Computation, 20(24), pp. 10838–10851. Available at: https://pubs.acs.org/doi/10.1021/acs.jctc.4c00940
Koksalan, S. & Ogan-Bekiroglu, F. (2024). Examination of Effects of Embedding Formative Assessment in Inquiry-Based Teaching on Conceptual Learning. Science Insights Education Frontiers, 20(2), pp. 3223–3246. Available at: https://files.eric.ed.gov/fulltext/EJ1416927.pdf
Lehtola, S. & Karttunen, A. J. (2022). Free and Open Source Software for Computational Chemistry Education. WIREs Computational Molecular Science, 12(5), p. e1610. Available at: https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1610
Li, J., Huang, Y., Zhang, X., & Zhang, L. (2024). Influence of Mg Doping on the Structure and Mechanical Properties of Al₂Cu Precipitated Phase by First-Principles Calculations. Materials, 17(1), p. 93. doi: https://www.mdpi.com/1996-1944/17/1/93
Ma, Z., Ren, F., Ming, X., Long, Y., & Volinsky, A.A. (2019). Cu-Doped ZnO Electronic Structure and Optical Properties Studied by First-Principles Calculations and Experiments. Materials, 12(1), p. 196. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC6337601/
Magers, B., Chávez, V.H., & Peyton, B.G. (2021). PSI4EDUCATION: Free and Open-Source Programming Activities for Chemical Education with Free and Open-Source Software. in Teaching Programming across the Chemistry Curriculum, American Chemical Society, pp. 107–122. doi: https://pubs.acs.org/doi/abs/10.1021/bk-2021-1387.ch008
Mortensen, J.J. et al. (2024). GPAW: An open Python package for electronic structure calculations. Journal of Chemical Physics, 160(9). doi: https://doi.org/10.1063/5.0182685
Sabolsky, E.M., Mena, J.A., Mendoza-Estrada, V., González-Hernández, R., Sabolsky, K., & Sierras, K. (2025). Doping Effects on Multivalence States, Electronic Structure, and Optical Band Gap in LaCrO₃ under Varied Atmospheres: An Integrated Experimental and Density Functional Theory Study. ACS Applied Electronic Materials, 7(6). doi: https://doi.org/10.1021/acsaelm.4c02359
Vallejo, W., Uribe, C.D., & Fajardo, C. (2022). Google Colab and Virtual Simulations: Practical e-Learning Tools to Introduce Python Programming and Density Functional Theory. ACS Omega, 7(8). doi: https://pubs.acs.org/doi/10.1021/acsomega.2c00362
Wang, Y., Tang, W., Liu, J., & Zhang, L. (2015). Stress-induced anomalous shift of optical band gap in Ga-doped ZnO thin films: Experimental and first-principles study. Applied Physics Letters, 106(16), p. 162101. doi: https://doi.org/10.1063/1.4918933
Wei, Z., Chen, J., Xue, J., Qu, N., Liu, Y., Sun, L., Xiao, Y., Wu, B., Zhu, J., & Tang, H. (2024). Investigation of the Influence of Alloy Atomic Doping on the Properties of Cu-Sn Alloys Based on First Principles. Metals, 14(5), p. 552. doi: https://doi.org/10.3390/met14050552
Zhang, W. & Zhou, Y. (2025). Research on Differentiated Teaching Model of Python Programming Based on Learning Data Analysis. Journal of Computer and Technology Education Research, 1(3). doi: https://doi.org/10.70767/jcter.v1i3.405
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anggara Budi Susila, Haris Suhendar, Riser Fahdiran

This work is licensed under a Creative Commons Attribution 4.0 International License.



