scholarly journals Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds

2021 ◽  
Author(s):  
Alexandru B. Georgescu ◽  
Peiwen Ren ◽  
Aubrey R. Toland ◽  
Shengtong Zhang ◽  
Kyle D. Miller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Machine Learning Model ◽  
Thermally Driven

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.

scholarly journals Thermally driven analog of the Barkhausen effect at the metal-insulator transition in vanadium dioxide

2014 ◽  
Vol 105 (13) ◽  
pp. 131902 ◽  
Author(s):  
Benjamin Huber-Rodriguez ◽  
Siu Yi Kwang ◽  
Will J. Hardy ◽  
Heng Ji ◽  
Chih-Wei Chen ◽  
...  
Keyword(s):  
Vanadium Dioxide ◽  
Insulator Transition ◽  
Barkhausen Effect ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Thermally Driven

Fermi-surface reconstruction close to a pressure-induced metal-insulator transition

2004 ◽  
Vol 114 ◽  
pp. 277-281 ◽  
Author(s):  
J. Wosnitza ◽  
J. Hagel ◽  
O. Stockert ◽  
C. Pfleiderer ◽  
J. A. Schlueter ◽  
...  
Keyword(s):  
Fermi Surface ◽  
Surface Reconstruction ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Fermi Surface Reconstruction

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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