Work

When machine learning meets multiscale modeling in chemical reactions

Year: 2020

Type: article

Abstract: Due to the intrinsic complexity and nonlinearity of chemical reactions, direct applications of traditional machine learning algorithms may face many difficulties. In this study, through two concrete examples with biological background, we illustrate how the key ideas of multiscale modeling can help to greatly reduce the computational cost of machine learning, 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.

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0015779

Authors: Wuyue Yang, Liangrong Peng, Yi Zhu, Liu Hong

Institutions: Sun Yat-sen University, Minjiang University, Tsinghua University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: 针对蛋白质聚集的抑制剂调控机制的动力学研究, G7061813319