当机器学习遇到化学反应中的多尺度建模
When Machine Learning Meets Multiscale Modeling in Chemical Reactions
摘要：
由于化学反应本身的复杂性和非线性，传统的机器学习算法直接应用于化学反应过程会面临很多困难。本文通过两个生物学背景的具体实例，说明了多尺度建模的核心思想如何帮助机器学习大大降低计算成本，以及机器学习算法如何在时间尺度分离的系统中自动进行模型约简。我们的研究强调了在化学反应研究中集成机器学习算法和多尺度建模的必要性和有效性。
英文摘要：
Due to the intrinsic complexity and nonlinearity of chemical reactions, direct applications of traditional machine learning algorithms may face with many difficulties. In this study, through two concrete examples with biological background, we illustrate how the key ideas of multiscale modeling can help to reduce the computational cost of machine learning a lot, as well as how machine learning algorithms perform model reduction automatically in a time-scale separated system. Our study highlights the necessity and effectiveness of an integration of machine learning algorithms and multiscale modeling during the study of chemical reactions.
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