强激光与粒子束2026，Vol.38Issue(4)：149-157,9.DOI:10.11884/HPLPB202638.250298
基于序列学习的激发态反应动力学回归
Excited state reaction kinetics regression based on sequence-to-sequence learning
摘要
Abstract
[Background]The reaction kinetics in lasers often involves a lot 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 introduced 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 was 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.关键词
激发态/反应动力学/序列学习/复杂性Key words
excited state/reaction kinetics/sequence-to-sequence learning/complexity分类
信息技术与安全科学引用本文复制引用
白天滋,怀英,刘婷婷,贾淑芹,多丽萍..基于序列学习的激发态反应动力学回归[J].强激光与粒子束,2026,38(4):149-157,9.基金项目
supported by National Key R&D Program of China(2024YFB4006600) （2024YFB4006600）
Research Foundation(232-CXCY-A01-09-05-01) （232-CXCY-A01-09-05-01）
Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0970204) （XDB0970204）
