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

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.

Resonant tunneling driven metal-insulator transition in double quantum-well structures of strongly correlated oxide

The metal-insulator transition (MIT), a fascinating phenomenon occurring in some strongly correlated materials, is of central interest in modern condensed-matter physics. Controlling the MIT by external stimuli is a key technological goal for applications in future electronic devices. However, the standard control by means of the field effect, which works extremely well for semiconductor transistors, faces severe difficulties when applied to the MIT. Hence, a radically different approach is needed. Here, we report an MIT induced by resonant tunneling (RT) in double quantum well (QW) structures of strongly correlated oxides. In our structures, two layers of the strongly correlated conductive oxide SrVO3 (SVO) sandwich a barrier layer of the band insulator SrTiO3. The top QW is a marginal Mott-insulating SVO layer, while the bottom QW is a metallic SVO layer. Angle-resolved photoemission spectroscopy experiments reveal that the top QW layer becomes metallized when the thickness of the tunneling barrier layer is reduced. An analysis based on band structure calculations indicates that RT between the quantized states of the double QW induces the MIT. Our work opens avenues for realizing the Mott-transistor based on the wave-function engineering of strongly correlated electrons.

Field-induced metal-to-insulator transition and colossal anisotropic magnetoresistance in a nearly Dirac material EuMnSb2

Realizing applicably appreciated spintronic functionalities basing on the coupling between charge and spin degrees of freedom is still a challenge. For example, the anisotropic magnetoresistance (AMR) effect can be utilized to read out the information stored in magnetic structures. However, the application of AMR in antiferromagnet-based spintronics is usually hindered by the small AMR value. Here, we discover a colossal AMR with its value reaching 1.84 × 106% at 2 K, which stems from the field-induced metal-to-insulator transition (MIT), in a nearly Dirac material EuMnSb2. Density functional theory calculations identify a Dirac-like band around the Y point that depends strongly on the spin–orbit coupling and dominates the electrical transport. The indirect band gap at the Fermi level evolves with magnetic structure of Eu2+ moments, consequently giving rise to the field-induced MIT and the colossal AMR. Our results suggest that the antiferromagnetic topological materials can serve as a fertile ground for spintronics applications.

Overactivated transport in the localized phase of the superconductor-insulator transition

Beyond a critical disorder, two-dimensional (2D) superconductors become insulating. In this Superconductor-Insulator Transition (SIT), the nature of the insulator is still controversial. Here, we present an extensive experimental study on insulating NbxSi1−x close to the SIT, as well as corresponding numerical simulations of the electrical conductivity. At low temperatures, we show that electronic transport is activated and dominated by charging energies. The sample thickness variation results in a large spread of activation temperatures, fine-tuned via disorder. We show numerically and experimentally that this originates from the localization length varying exponentially with thickness. At the lowest temperatures, there is an increase in activation energy related to the temperature at which this overactivated regime is observed. This relation, observed in many 2D systems shows that conduction is dominated by single charges that have to overcome the gap when entering superconducting grains.