A Global Fundamental Invariants Neural Networks Potential Energy Surface and Dynamics Study of the H+HCN Reaction

doi:10.1007/s40242-025-5140-7

Chemical Research in Chinese Universities ›› 2025, Vol. 41 ›› Issue (5): 1021-1028.doi: 10.1007/s40242-025-5140-7

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A Global Fundamental Invariants Neural Networks Potential Energy Surface and Dynamics Study of the H+HCN Reaction

CHEN Qun^1,2, LIU Shu^1,2, FU Bina^1,2, ZHANG Donghui^1,2

1. State Key Laboratory of Chemical Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China;
2. University of Chinese Academy of Sciences, Beijing 100049, P. R. China

Received:2025-07-10 Accepted:2025-08-26 Online:2025-10-01 Published:2025-09-26
Contact: ZHANG Donghui, E-mail: zhangdh@dicp.ac.cn;LIU Shu, E-mail: liushu1985@dicp.ac.cn E-mail:zhangdh@dicp.ac.cn;liushu1985@dicp.ac.cn
Supported by:
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Nos. XDB0450202, XDB0970202, XDB0970203), the National Natural Science Foundation of China (Nos. 22288201, 22022306), the Innovation Program for Quantum Science and Technology, China (No. 2021ZD0303305), the Liaoning Revitalization Talents Program, China (No. XLYC2203062), and the Dalian Innovation Support Program, China (No. 2021RD05).

Abstract

Abstract: A global potential energy surface (PES) for the H+HCN reaction has been constructed using the fundamental invariants neural networks (FI-NN) fitting method, based on 38662 ab initio energy points calculated at the UCCSD(T)-F12a/AVTZ theoretical level. The PES not only covers the well-studied abstract channel, but also accurately describes the stationary points and reaction pathways of the exchange channel, achieving an overall root-mean-square error (RMSE) of 2.85 meV. Total integral cross sections of both channels were computed, and the branching ratios of HCN and HNC product isomers were also analyzed, with HNC further distinguished by its formation through three different pathways.

Key words: Potential energy surface (PES), Complex-forming reaction, Integral cross sections (ICS)

CHEN Qun, LIU Shu, FU Bina, ZHANG Donghui. A Global Fundamental Invariants Neural Networks Potential Energy Surface and Dynamics Study of the H+HCN Reaction[J]. Chemical Research in Chinese Universities, 2025, 41(5): 1021-1028.

References

[1] Jacox M. E., J. Phys. Chem., 1987, 91, 6595.
[2] Lambert H. M., Carrington T., Filseth S. V., Sadowski C. M., J. Phys. Chem., 1993, 97, 128.
[3] Pfeiffer J. M., Metz R. B., Thoemke J. D., Woods E., Crim F. F., J. Chem. Phys., 1996, 104, 4490.
[4] Kreher C., Theinl R., Gericke K. H., J. Chem. Phys., 1996, 104, 4481.
[5] Kreher C., Rinnenthal J. L., Gericke K. H., J. Chem. Phys., 1998, 108, 3154.
[6] Bair R. A., Dunning T. H., J. Chem. Phys., 1985, 82, 2280.
[7] Sun Q., Bowman J. M., J. Chem. Phys., 1990, 92, 5201.
[8] Sun Q., Yang D. L., Wang N. S., Bowman J. M., Lin M. C., J. Chem. Phys., 1990, 93, 4730.
[9] Clary D. C., J. Phys. Chem., 1995, 99, 13664.
[10] ter Horst M. A., Schatz G. C., Harding L. B., J. Chem. Phys., 1996, 105, 558.
[11] Talbi D., Ellinger Y., Chem. Phys. Lett., 1996, 263, 385.
[12] Takayanagi T., Schatz G. C., J. Chem. Phys., 1997, 106, 3227.
[13] Bethardy G. A., Wagner A. F., Schatz G. C, ter Horst M. A., J. Chem. Phys., 1997, 106, 6001.
[14] Sumathi R., Nguyen M. T, J. Phys. Chem. A, 1998, 102, 8013.
[15] Manthe U., Matzkies F., Chem. Phys. Lett., 1998, 282, 442.
[16] Bethardy G. A., Northrup F. J., He G., Tokue I., Macdonald R. G., J. Chem. Phys., 1998, 109, 42246.
[17] Zhu W., Zhang J. Z. H., Zhang Y. C., Zhang Y. B., Zhan L. X., Zhang S. L., Zhang D. H., J. Chem. Phys., 1998, 108, 3509.
[18] Ma W. Y., Han K. L., Wang M. L., Zhang J. Z. H., J. Chem. Phys., 2002, 117, 172.
[19] Ju L. P., Han K. L., Zhang J. Z. H., J. Theor. Comput. Chem., 2006, 5, 769.
[20] Hebrard E., Dobrijevic M., Loison J. C., Bergeat A., Hickson K. M., Astron. Astrophys., 2012, 541, A21.
[21] Bhattacharya S., Kirwai A., Panda A. N., Meyer H. D., J. Chem. Sci., 2012, 124, 65.
[22] Jiang B., Guo H., J. Chem. Phys., 2013, 139, 224310.
[23] Wang X., Bowman J. M., J. Chem. Theory. Comput., 2013, 9, 9018.
[24] Correa E., Silva W. B. D., Barreto R. P., Alessandra F. Albernaz., J. Mol. Model., 2017, 23, 169.
[25] Wiens A. E., Andreas V. C., Elliot C. R., Gustavo J. R. A., Jay A., Henry F. S., J. Chem. Phys., 2018, 148, 014305.
[26] Manjusha C. P., Abhijeet R., Ind. Eng. Chem. Res., 2025, 64, 8089.
[27] Fu B., Zhang D. H., Nat. Sci. Rev., 2023, 10, nwad321.
[28] Werner H.-J., Knowles P. J., Celani P., Györffy W., Hesselmann A., Kats D., Knizia G., Köhn A., Korona T., Kreplin D., Lindh A. R., Ma Q., Manby F. R., Mitrushenkov A., Rauhut G., Schütz M., Shamasundar K. R., Adler T. B., Amos R. D., Baker J., Bennie S. J., Bernhardsson A., Berning A., Black J. A., Bygrave P. J., Cimiraglia R., Cooper D. L., Coughtrie D., Deegan M. J. O., Dobbyn A. J., Doll K., Dornbach M., Eckert F., Erfort S., Goll E., Hampel C., Hetzer G., Hill J. G., Hodges M., Hrenar T., Jansen G., Köppl C., Kollmar C., Lee S. J. R., Liu Y., Lloyd A. W., Mata R. A., May A. J., Mussard B., McNicholas S. J., Meyer W., Miller T. F., III Mura M. E., Nicklass A., O'Neill D. P., Palmieri P., Peng D., Peterson K. A. Peterson, Pflüger K., Pitzer R., Polyak I., Pulay P., Reiher M., Richardson J. O., Robinson J. B., Schröder B., Schwilk M., Shiozaki T., Sibaev M., Stoll H., Stone A. J., Tarroni R., Thorsteinsson T., Toulouse J., Wang M., Welborn M., Ziegler B., MOLPRO (Version 2022.3), a Package of Ab initio Programs, 2022.
[29] Chen J., Xu X., Xu X., Zhang D. H., J. Chem. Phys., 2013, 138, 221104.
[30] Zhang D. H., Light J. C., J. Chem. Phys., 1996, 104, 4544.
[31] Liu S., Chen J., Zhang X., Zhang D. H., Chem. Sci., 2023, 14, 7973.
[32] Liu S., Zhang D. H., J. Chem. Phys., 2025, 162, 074307.

A Global Fundamental Invariants Neural Networks Potential Energy Surface and Dynamics Study of the H+HCN Reaction

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