论文标题
当机器学习符合化学反应中的多尺度建模时
When Machine Learning Meets Multiscale Modeling in Chemical Reactions
论文作者
论文摘要
由于化学反应的内在复杂性和非线性，传统机器学习算法的直接应用可能会遇到许多困难。在这项研究中，通过两个具有生物学背景的具体示例，我们说明了多尺度建模的关键思想如何有助于减少机器学习的计算成本，以及机器学习算法如何在时间尺度分离的系统中自动降低模型。我们的研究强调了在化学反应研究过程中，机器学习算法和多尺度建模的整合的必要性和有效性。
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.
