Mechanics in Engineering ›› 2020, Vol. 42 ›› Issue (1): 13-16.DOI: 10.6052/1000-0879-19-412

• Applied Research • Previous Articles     Next Articles

DESIGNING ARTIFICIAL BOUNDARY CONDITIONS FOR ATOMIC CHAINS BY MACHINE LEARNING 1)

ZHANG Qian*, QIAO Dan, TANG Shaoqiang*,2)()   

  1. * Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100871, China
    † Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100871, China
  • Received:2019-11-11 Online:2020-02-20 Published:2020-03-13
  • Contact: TANG Shaoqiang

Abstract:

In this paper, we adopt machine learning techniques to design artificial boundary conditions for one-dimensional linear atomic chain. Training a feedforward neural network with a small amount of numerical solutions, we obtain artificial boundary conditions. This approach requires little prior information, and programming and computation are fast. Numerical examples illustrate a relatively small reflection.

Key words: molecular dynamics simulation, artificial boundary conditions, machine learning, feedforward neural network

CLC Number: