用神经微分方程补全化学反应模型的未知动力学机制
Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
- 将神经微分方程融入化学反应网络建模，自动学习浓度变化规律
- 揭示传统经验模型遗漏的动力学特征，提升反应路径预测准确性
- 适合从事反应机理研究或新反应设计的科研人员参考
在化学反应网络理论中，常使用常微分方程描述化学物质浓度随时间的变化。由于这些方程的函数形式基于对反应网络的经验假设，可能不完整。本文通过将动态建模与深度学习结合，采用神经常微分方程（Neural ODEs）来揭示反应网络中的隐藏规律。该方法不仅能够识别现有经验模型的缺陷，还可为未来反应网络的设计提供依据。
原文摘要 · Abstract (English)
In chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration. As the functional form of these ordinary differential equations systems is derived from an empirical model of the reaction network, it may be incomplete. Our approach aims to elucidate these hidden insights in the reaction network by combining dynamic modelling with deep learning in the form of neural ordinary differential equations. Our contributions not only help to identify the shortcomings of existing empirical models but also assist the design of future reaction networks.
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