Hamiltonian learning for spin-spiral moir\'e magnets from electronic magnetotransport

TL;DR

This paper introduces a machine learning-based method to determine the spin-spiral vector in two-dimensional noncollinear magnetic materials using electronic transport measurements, overcoming detection challenges.

Contribution

The authors develop a supervised machine learning approach to extract spin-spiral vectors from conductance data, robust against impurities and noise, enabling direct magnetic structure learning from transport experiments.

Findings

Conductance patterns depend complexly on the spin-spiral vector.

The method accurately retrieves the $ extbf{q}$ vector in noisy and impurity-laden systems.

The approach is robust and applicable to arbitrary spin-spiral magnets.

Abstract

Two-dimensional noncollinear magnetic states, such as spin-spiral magnets, offer an excellent platform for investigating fundamental phenomena, with potential for advancing stray-field-free spintronics. However, detection and characterization of noncollinear magnetic states in two-dimensional systems remain challenging, motivating the development of alternative probing methods. Here, we present a methodology for extracting the spin-spiral $\mathbf{q}$ vector from lateral electronic transport measurements. Our approach leverages the magnetic field and bias dependence of the conductance to train a supervised machine learning algorithm, which enables us to extract the $\mathbf{q}$ vectors of arbitrary spin-spiral magnets. We demonstrate that this methodology is robust to the presence of impurities in the system and noise in the conductance data. Our findings show that the conductance pattern reveals a complex dependence on the $\mathbf{q}$ vector of the spin spiral, providing a new strategy to learn magnetic structures directly from transport experiments.