Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films-Reference-Cited by-同舟云学术

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Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films

Published:2022-10-17 Issue:47 Volume:34 Page:2202814
ISSN:0935-9648
Container-title:Advanced Materials
language:en
Short-container-title:Advanced Materials

Author:

Qin Shuyu¹,Guo Yichen²,Kaliyev Alibek T.¹³,Agar Joshua C.²⁴

Affiliation:

1. Department of Computer Science and Engineering Lehigh University Bethlehem PA 18015 USA

2. Department of Materials Science and Engineering Lehigh University Bethlehem PA 18015 USA

3. College of Business Lehigh University Bethlehem PA 18015 USA

4. Department of Mechanical Engineering and Mechanics Drexel University Philadelphia PA 19104 USA

Funder

Oak Ridge National Laboratory

National Science Foundation

Army Research Laboratory

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.202202814

Reference52 articles.

1. A Strain-Driven Morphotropic Phase Boundary in BiFeO 3

2. Nanoscale Structure and Mechanism for Enhanced Electromechanical Response of Highly Strained BiFeO3 Thin Films

3. Mechanical-force-induced non-local collective ferroelastic switching in epitaxial lead-titanate thin films

4. Kinetic control of tunable multi-state switching in ferroelectric thin films

5. Domain wall motion and its contribution to the dielectric and piezoelectric properties of lead zirconate titanate films

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2. Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials;Materials;2024-05-23

3. Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction;npj Computational Materials;2024-05-18

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