基于机器学习势函数的理论模拟在本科生教学实验课设计中的应用

doi:10.12461/PKU.DXHX202405164

University Chemistry ›› 2025, Vol. 40 ›› Issue (3): 140-147.doi: 10.12461/PKU.DXHX202405164

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Application of Machine Learning Potential-based Theoretical Simulations in Undergraduate Teaching Laboratory Course Design

Xintian Xie¹, Sicong Ma², Yefei Li¹, Cheng Shang¹, Zhipan Liu^1,2

1 Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Ministry of Education, Department of Chemistry, Fudan University, Shanghai 200433, China;
2 State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China

Received:2024-05-27 Accepted:2024-08-14 Published:2025-03-19
Contact: Zhipan Liu E-mail:zpliu@fudan.edu.cn

Abstract

Abstract: Integrating theoretical simulation courses into undergraduate education for chemistry and materials science is of great significance for cultivating modern chemistry talents. Using the simulation methods and software developed by our research group, we designed two simulation experiments: "Construction of the Potential Energy Surface for H₂ Dissociation on the Cu(111) Surface" and "Characterization and Simulation of Acidity on Zeolite Molecular Sieve Surfaces". These experiments aim to deepen the undergraduates’ comprehension of theoretical simulations and highlight the transformative advancements driven by artificial intelligence technology.

Key words: Experiment of simulation, Artificial intelligence, Potential energy surface exploration

Xintian Xie, Sicong Ma, Yefei Li, Cheng Shang, Zhipan Liu. Application of Machine Learning Potential-based Theoretical Simulations in Undergraduate Teaching Laboratory Course Design[J].University Chemistry, 2025, 40(3): 140-147.

References

[1] Lecun, Y.; Bengio, Y.; Hinton, G. Nature 2015, 521 (7553), 436.
[2] Lawrence S.; Giles C. L.; Tsoi A. C.; Back A. D. IEEE Trans. Neural Netw. 1997, 8 (1), 98.
[3] Krizhevsky, A.; Sutskever, I.; Hinton, G. E. Commun. ACM 2012, 60, 84.
[4] Wang, H.; Zhang, L. F.; Han, J. Q.; E, W. N. Comput. Phys. Commun. 2018, 228, 178.
[5] Dral, P. O. J. Comput. Chem. 2019, 40 (26), 2339.
[6] Huang, S.-D.; Shang, C.; Kang, P.-L.; Zhang, X.-J.; Liu, Z.-P. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2019, 9, e1415.
[7] Behler, J.; Parrinello, M. Phys. Rev. Lett. 2007, 98 (14), 146401.
[8] Behler, J.; Martoňák, R.; Donadio, D.; Parrinello, M. Phys. Rev. Lett. 2008, 100 (18), 185501.
[9] Shang, C.; Huang, S.-D.; Liu, Z.-P. J. Comput. Chem. 2019, 40 (10), 1091.
[10] Huang, S.-D.; Shang, C.; Kang, P.-L.; Liu, Z.-P. Chem. Sci. 2018, 9 (46), 8644.
[11] Kang, P.-L.; Yang, Z.-X.; Shang, C.; Liu, Z.-P. J. Chem. Theory Comput. 2023, 19 (21), 7972.
[12] Shang, C.; Liu, Z.-P. J. Chem. Theory Comput. 2013, 9 (3), 1838.
[13] Zhang, X.-J.; Liu, Z.-P. Phys. Chem. Chem. Phys. 2015, 17 (4), 2757.
[14] Shang, C.; Liu, Z.-P. J. Chem. Theory Comput. 2010, 6 (4), 1136.
[15] Zhang, X.-J.; Shang, C.; Liu, Z.-P. J. Chem. Theory Comput. 2013, 9 (12), 5745.
[16] Kresse, G.; Furthmüller, J. Comput. Mater. Sci. 1996, 6 (1), 15.
[17] Soler, J. M.; Artacho, E.; Gale, J. D.; Garcia, A.; Junquera, J.; Ordejon, P.; Sanchez-Portal, D. J. Phys.: Condens. Matter 2002, 14, 2745.
[18] Plimpton, S. J. Comput. Phys. 1995, 117 (1), 1.
[19] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; et al. Gaussian 09, Revision D.01; Gaussian Inc.: Wallingford, CT, USA, 2009.
[20] Hutter, J.; Iannuzzi, M.; Schiffmann, F.; Vandevondele, J. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2014, 4 (1), 15.
[21] Database of Zeolite Structures. http://www.iza-structure.org/databases/ (accessed Feb. 12, 2025)
[22] Ma, S.; Liu, Z.-P. Chem. Sci. 2022, 13 (18), 5055.

Application of Machine Learning Potential-based Theoretical Simulations in Undergraduate Teaching Laboratory Course Design

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