Rise and Fall of Anderson Localization by Lattice Vibrations: A Time-Dependent Machine Learning Approach

TL;DR

This paper employs machine learning to analyze electron-lattice interactions within the Fr"ohlich model, revealing transient localization phenomena that could influence the understanding of strange metals and material design.

Contribution

It introduces a novel time-dependent machine learning approach to classify interaction regimes and identify transient localization in electron-lattice dynamics.

Findings

Identification of transient localization as a key dynamic regime.

Machine learning categorizes different electron-lattice interaction phases.

Insights into the role of lattice vibrations in electron transport.

Abstract

The intricate relationship between electrons and the crystal lattice is a linchpin in condensed matter, traditionally described by the Fr\"ohlich model encompassing the lowest-order lattice-electron coupling. Recently developed quantum acoustics, emphasizing the wave nature of lattice vibrations, has enabled the exploration of previously uncharted territories of electron-lattice interaction not accessible with conventional tools such as perturbation theory. In this context, our agenda here is two-fold. First, we showcase the application of machine learning methods to categorize various interaction regimes within the subtle interplay of electrons and the dynamical lattice landscape. Second, we shed light on a nebulous region of electron dynamics identified by the machine learning approach and then attribute it to transient localization, where strong lattice vibrations result in a momentary Anderson prison for electronic wavepackets, which are later released by the evolution of the lattice. Overall, our research illuminates the spectrum of dynamics within the Fr\"ohlich model, such as transient localization, which has been suggested as a pivotal factor contributing to the mysteries surrounding strange metals. Furthermore, this paves the way for utilizing time-dependent perspectives in machine learning techniques for designing materials with tailored electron-lattice properties.