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

Frontiers in Physics

Alexandru Bogdan Georgescu¹  Jennifer Fowlie²  Javier del Valle²  Philippe Tückmantel²  Bernat Mundet³ 

[1] Evanston, IL, United States;Geneva, Switzerland;Geneva, Switzerland;Lausanne, Switzerland;

关键词: machine learning;    rare-earth nickelates;    artificial intelligence;    resistive switching;    neuromorphic computing;    metal-insulator transitions;    scanning transmission elctron microscopy;   

DOI  :  10.3389/fphy.2021.725853

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY