Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior in Defect‐Rich PZT Films-Reference-Cited by-同舟云学术

Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior in Defect‐Rich PZT Films

Published:2021-10-22 Issue:12 Volume:5 Page:2100552
ISSN:2366-9608
Container-title:Small Methods
language:en
Short-container-title:Small Methods

Author:

Zhang Fengyuan¹²,Williams Kerisha N.³^ORCID,Edwards David¹²,Naden Aaron B.⁴,Yao Yulian³,Neumayer Sabine M.¹²,Kumar Amit⁵,Rodriguez Brian J.¹²,Bassiri‐Gharb Nazanin³⁶^ORCID

Affiliation:

1. School of Physics University College Dublin Belfield Dublin D04 V1W8 Ireland

2. Conway Institute of Biomolecular and Biomedical Research University College Dublin Belfield Dublin D04 V1W8 Ireland

3. School of Materials Science and Engineering Georgia Institute of Technology Atlanta GA 30332‐0405 USA

4. University of St Andrews School of Chemistry Purdie Building, St Andrews Fife KY16 9ST United Kingdom

5. Centre for Nanostructured Media School of Mathematics and Physics Queen's University Belfast Belfast BT7 1NN United Kingdom

6. G.W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332‐0405 USA

Funder

China Scholarship Council

National Science Foundation

Science Foundation Ireland

Publisher

Wiley

Subject

General Materials Science,General Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/smtd.202100552

Reference45 articles.

1. Big, Deep, and Smart Data in Scanning Probe Microscopy

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3. Imaging mechanism of piezoresponse force microscopy of ferroelectric surfaces

4. Dual-frequency resonance-tracking atomic force microscopy

5. Resonance enhancement in piezoresponse force microscopy: Mapping electromechanical activity, contact stiffness, and Q factor

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