Many-body mobility edges in one dimension revealed by efficient and interpretable feature-based learning with Kolmogorov-Arnold Networks

TL;DR

This paper introduces a feature-based machine learning approach using Kolmogorov-Arnold Networks to accurately identify many-body localization transitions and mobility edges in disordered one-dimensional fermionic systems.

Contribution

The study presents a novel, efficient, and interpretable machine learning framework that uses physically motivated features to detect many-body localization transitions, outperforming traditional high-dimensional neural networks in training efficiency.

Findings

Achieves over 99.9% validation accuracy in classifying localized and delocalized states.

Reveals a clear many-body mobility edge across the energy spectrum.

Provides consistent estimates of critical disorder strength.

Abstract

We study the many-body localization (MBL) transition in interacting fermionic systems on disordered one-dimensional lattices using a physics-informed machine-learning framework. Instead of feeding full many-body wave functions into the model, we construct a compact feature representation based on four physically motivated observables: the inverse participation ratio, the Shannon entropy, the many-body hybridization parameter, and the mean level-spacing ratio. These quantities capture complementary aspects of localization, entanglement, and spectral correlations, and are used to train a Kolmogorov--Arnold Network (KAN) classifier on eigenstates deep in the weak and strong disorder regimes. The resulting KAN achieves a validation accuracy exceeding $99.9\%$, comparable to that of convolutional neural networks trained directly on high-dimensional wave-function data, while requiring substantially reduced input dimensionality and significantly shorter training time. Applying the trained classifier across the full energy spectrum yields energy-resolved phase diagrams that reveal a clear many-body mobility edge and provide a consistent estimate of the critical disorder strength. The approach is inherently extensible: additional physically relevant observables can be incorporated into the feature space in a systematic manner without altering the overall architecture. Our results demonstrate that feature-based learning with KAN provides an efficient, scalable, and interpretable methodology for identifying many-body localization transitions, offering a practical alternative to raw-data-based neural network approaches.