Excited state reaction kinetics regression based on sequence-to-sequence learning

Bai Tianzi; Huai Ying; Liu Tingting; Jia Shuqin; Duo Liping

doi:10.11884/HPLPB202638.250298

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Article Navigation > High Power Laser and Particle Beams > 2026 > Accepted Manuscript

Bai Tianzi, Huai Ying, Liu Tingting, et al. Excited state reaction kinetics regression based on sequence-to-sequence learning[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250298

Citation:

Bai Tianzi, Huai Ying, Liu Tingting, et al. Excited state reaction kinetics regression based on sequence-to-sequence learning[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250298

Bai Tianzi, Huai Ying, Liu Tingting, et al. Excited state reaction kinetics regression based on sequence-to-sequence learning[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250298

Citation:

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Excited state reaction kinetics regression based on sequence-to-sequence learning

doi: 10.11884/HPLPB202638.250298

Bai Tianzi^{1, 2
,},
Huai Ying^{1, 3
,
,},
Liu Tingting¹,
Jia Shuqin^{1, 3},
Duo Liping^{1, 3}

1.
Key Laboratory of Chemical Lasers, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China

Funds: supported by National Key R&D Program of China(No. 2024YFB4006600); Research Foundation (232-CXCY-A01-09-05-01); Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0970204)

More Information

Author Bio:
Bai Tianzi, btzan@dicp.ac.cn
Corresponding author: Huai Ying, huaiying@dicp.ac.cn。
Received Date: 2025-09-01
Accepted Date: 2025-12-17
Rev Recd Date: 2025-12-16

Available Online: 2026-01-15

Abstract

Abstract

Background
The reaction kinetics in lasers often involves a lots of excited state species. The mutual effects and numerical stiffness arising from the excited state species pose significant challenges in numerical simulations of lasers. The development of artificial intelligence has made Neural Networks (NNs) a promising approach to address the computational intensity and instability in Excited State Reaction Kinetics (ESRK).
Purpose
However, the complexity of ESRK poses challenges for NN training. These reactions involve numerous species and mutual effects, resulting in a high-dimensional variable space. This demands that the NN possess the capability to establish complex mapping relationships. Moreover, the significant change in state before and after the reaction leads to a broad variable space coverage, which amplifies the demand for NN's accuracy.
Methods
To address the aforementioned challenges, this study introduces the successful sequence-to-sequence learning from large language learning into ESRK to enhance prediction accuracy in complex, high-dimensional regression. Additionally, a statistical regularization method is proposed to improve the diversity of the outputs. NNs with different architectures were trained using randomly sampled data, and their capabilities were compared and analyzed.
Results
The proposed method is validated using a vibrational reaction mechanism for hydrogen fluoride, which involves 16 species and 137 reactions. The results demonstrate that the sequential model achieves lower training loss and relative error during training. Furthermore, experiments with different hyperparameters reveal that variation in the random seed can significantly impact model performance.
Conclusions
In this work, the introduction of the sequential model successfully reduced the parameter count of the conventional wide model without compromising accuracy. However, due to the intrinsic complexity of ESRK, there remains considerable room for improvement in NN-based regression tasks for this domain.
- excited state,
- reaction kinetics,
- sequence-to-sequence learning,
- complexity

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References(25)

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