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Mekong Delta Natural Science Conference
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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
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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
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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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
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
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