Machine-learning-identified two-dimensional van der Waals multiferroics for four-state nonvolatile memory

TL;DR

This study uses machine learning and first-principles calculations to identify 2D van der Waals multiferroics, particularly AuCrP$_2$S$_6$, for four-state nonvolatile memory with coupled ferroelectric and magnetic properties.

Contribution

Combines machine learning with first-principles methods to discover new 2D multiferroics suitable for multi-state memory devices.

Findings

Identified AuCrP$_2$S$_6$ as a promising multiferroic candidate.

Demonstrated dual-channel non-destructive readout via BPVE.

Showed coupling of polarization and magnetic order enables four-state memory.

Abstract

Two-dimensional (2D) van der Waals (vdW) multiferroics offer an attractive platform for four-state nonvolatile memory by combining switchable ferroelectric polarization and magnetization within a single material system. However, their development is hindered by the scarcity of synthesizable candidates and the lack of non-destructive readout schemes. Here, we combine machine-learning screening with first-principles calculations to explore the 2D vdW ABC$_2$X$_6$ family and identify a set of high-confidence multiferroic candidates. Among them, AuCrP$_2$S$_6$ monolayer emerges as a representative system with a ferromagnetic ground state, a sizable out-of-plane polarization of 7.46 pC/m, and a moderate ferroelectric switching barrier of $\sim$130 meV/f.u. Moreover, the nonlinear optical response mediated by the bulk photovoltaic effect (BPVE) in AuCrP$_2$S$_6$ provides a dual-channel probe of the ferroic orders, in which the polarization direction governs the photocurrent sign while the magnetic order selects the spin channel via robust exchange splitting. This intrinsic coupling enables the non-destructive readout of four logic states within a single atomic layer, thereby providing a practical blueprint for next-generation multistate optoelectronic memory.