scholarly journals Coupled magnetic–elastic and metal–insulator transition in epitaxially strained SrMnO3/BaMnO3 superlattices

RSC Advances ◽  
2018 ◽  
Vol 8 (63) ◽  
pp. 36407-36411
Author(s):  
Jin-Feng Wang ◽  
Zheng Li ◽  
Zhao-Tong Zhuang ◽  
Yan-Ming Zhang ◽  
Jun-Ting Zhang
Keyword(s):  
Phase Transitions ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Epitaxial Strain

The epitaxial strain can induce interesting physical phase transitions in SrMnO3/BaMnO3 superlattices.

Tuning the metal-insulator transition via epitaxial strain and Co doping in NdNiO3 thin films grown by polymer-assisted deposition

2016 ◽  
Vol 119 (3) ◽  
pp. 035303 ◽  
Author(s):  
Dan Yao ◽  
Lei Shi ◽  
Shiming Zhou ◽  
Haifeng Liu ◽  
Jiyin Zhao ◽  
...  
Keyword(s):  
Thin Films ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Co Doping ◽  
Epitaxial Strain

Epitaxial strain induced metal insulator transition in La0.9Sr0.1MnO3 and La0.88Sr0.1MnO3 thin films

2000 ◽  
Vol 76 (2) ◽  
pp. 155-157 ◽  
Author(s):  
F. S. Razavi ◽  
G. Gross ◽  
H.-U. Habermeier ◽  
O. Lebedev ◽  
S. Amelinckx ◽  
...  
Keyword(s):  
Thin Films ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Epitaxial Strain

scholarly journals Machines for Materials and Materials for Machines: Metal-Insulator Transitions and Artificial Intelligence

2021 ◽  
Vol 9 ◽  
Author(s):  
Jennifer Fowlie ◽  
Alexandru Bogdan Georgescu ◽  
Bernat Mundet ◽  
Javier del Valle ◽  
Philippe Tückmantel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Rare Earth ◽  
Phase Transitions ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Network Analyses ◽  
Correlated Electron ◽  
Thermally Driven

In this perspective, we discuss the current and future impact of artificial intelligence and machine learning for the purposes of better understanding phase transitions, particularly in correlated electron materials. We take as a model system the rare-earth nickelates, famous for their thermally-driven metal-insulator transition, and describe various complementary approaches in which machine learning can contribute to the scientific process. In particular, we focus on electron microscopy as a bottom-up approach and metascale statistical analyses of classes of metal-insulator transition materials as a bottom-down approach. Finally, we outline how this improved understanding will lead to better control of phase transitions and present as an example the implementation of rare-earth nickelates in resistive switching devices. These devices could see a future as part of a neuromorphic computing architecture, providing a more efficient platform for neural network analyses – a key area of machine learning.

Epitaxial strain effects on the metal–insulator transition in V2O3 thin films

2004 ◽  
Vol 129 (4) ◽  
pp. 245-248 ◽  
Author(s):  
Shigeki Yonezawa ◽  
Yuji Muraoka ◽  
Yutaka Ueda ◽  
Zenji Hiroi
Keyword(s):  
Thin Films ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Strain Effects ◽  
Epitaxial Strain
Sign in / Sign up

Export Citation Format

Share Document