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Date Published: 24/03/2025
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DOI: https://doi.org/10.52714/dthu.14.04S.2025.1544
Issue
Vol. 14 No. 04S (2025): Special Issue of Mekong Delta Natural Science Conference (Vietnamese)
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Mekong Delta Natural Science Conference
How to Cite
Nguyen, H. D., Ho, N. N., Dang, M. T., & Nguyen, T. T. (2025). Application of machine learning model in material simulation using Python on Google Colab. Dong Thap University Journal of Science, 14(04S), 27-35. https://doi.org/10.52714/dthu.14.04S.2025.1544

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Application of machine learning model in material simulation using Python on Google Colab

Hai Dang Nguyen1,2, , Nha Nghi Ho3, Minh Triet Dang4, Thanh Tien Nguyen2
1 Faculty of Basic Sciences, Nam Can Tho University, Vietnam
2 College of Natural Sciences, Can Tho University, Vietnam
3 Huynh Man Dat Gifted High School, Kien Giang, Vietnam
4 School of Education , Can Tho University, Vietnam

Main Article Content

Abstract

This study builds a machine learning model using artificial neural networks to train machine learning-based atomic interaction potentials from data by molecular dynamics simulations. The goal is to predict the formation energy of armchair-edge graphene nanoribbon with a missing carbon atom. The deep learning algorithm is implemented on the Google Colab platform using Python language.

Keywords

Deep learning, Google Colab, machine learning, neural networks, Python

Article Details

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

References

Behler, J. (2016). Perspective: Machine learning potentials for atomistic simulations. The Journal of Chemical Physics, 145(17), 170901. https://doi.org/10.1063/1.4966192
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2
Hafner, J. (2008). Ab‐initio simulations of materials using VASP: Density‐functional theory and beyond. Journal of computational chemistry, 29(13), 2044-2078. https://doi.org/10.1002/jcc.21057
Jinnouchi, R., Karsai, F., & Kresse, G. (2019). On-the-fly machine learning force field generation: Application to melting points. Physical Review B, 100(1), 014105. https://doi.org/10.1103/PhysRevB.100.014105
Nguyen, T. T., Pham, T. B. T., Nguyen, D. K., Le, N. T., & Vo, K. D. (2025). Thermoelectric properties of penta-InP₅: A first-principles and machine learning study. Journal of Applied Physics, 137(8), 084302. https://doi.org/10.1063/5.0251741
Nguyen, T. T, Vo, T. P, & Ahuja, R. (2018). Tuning electronic transport properties of zigzag graphene nanoribbons with silicon doping and phosphorus passivation. AIP Advances, 8(8), 085123. https://doi.org/10.1063/1.5042071
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5, 83. https://doi.org/10.1038/s41524-019-0221-0
Shapeev, A. V. (2016). Moment tensor potentials: A class of systematically improvable interatomic potentials. Multiscale Modeling & Simulation, 14(3), 1153–1173. https://doi.org/10.1137/15M1054183
Smidstrup, S., Markussen, T., Vancraeyveld, P., Wellendorff, J., Schneider, J., Gunst, T., ... & Stokbro, K. (2019). QuantumATK: an integrated platform of electronic and atomic-scale modelling tools. Journal of Physics: Condensed Matter, 32(1), 015901. https://doi.org/10.1088/1361-648X/ab4007
Wang, H., Zhang, L., & Han, J. (2018). DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Computer Physics Communications, 228, 178-184. https://doi.org/10.1016/j.cpc.2018.03.016
Yazyev, O. V., & Louie, S. G. (2010). Electronic transport in polycrystalline graphene. Nature Materials, 9(10), 806–809. https://doi.org/10.1038/nmat2830
Zeng, J., Giese, T. J., Zhang, D., Wang, H., & York, D. M. (2025). DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials. Journal of Chemical Information and Modeling, 65(7), 3154-3160. https://doi.org/10.1021/acs.jcim.4c02441
Zhang, Y., Li, H., & Wang, J. (2022). Various defects in graphene: a review. RSC Advances, 12(24), 15644–15654. https://doi.org/10.1039/D2RA01436J
Zhao, Y., Liu, Z., & Wang, Y. (2022). Effect of Vacancy Defects on the Vibration Frequency of Graphene Nanoribbons. Nanomaterials, 12(5), 764. https://doi.org/10.3390/nano12050764
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., ... & Ong, S. P. (2020). Performance and cost assessment of machine learning interatomic potentials. Journal of Physical Chemistry A, 124(4), 731-745. https://doi.org/10.1021/acs.jpca.9b08723