To switch or not to switch – a machine learning approach for ferroelectricity

Abstract

The introduced two-dimensional representation of two-parameter signal dependence allows for clear interpretation and classification of the measured signal upon using machine learning methods.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2020
Source ID
10.1039/c9na00731h

Entities

People

  • Andrei L. Kholkin
  • Gabriel Velarde
  • Ivan Kravchenko
  • Lane W Martin
  • Nina Balke
  • Peter Maksymovych
  • Sabine M Neumayer
  • Stephen Jesse

Organizations

  • Army Research Office
  • Aveiro Institute of Materials
  • Center for Nanophase Materials Sciences
  • Fundação para a Ciência e Tecnologia
  • National Science Foundation
  • Oak Ridge National Laboratory
  • United States Army
  • United States Department of Energy
  • University of Aveiro
  • University of California
  • University of California, Berkeley

Tags

Fields of Study

  • Physics

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks