Intercalation-mediated activation and enhancement of ferroelectricity in transition metal dichalcogenide heterobilayers

Abstract

Ferroelectricity (FE) in two-dimensional (2D) materials holds considerable promise for ultrathin, low-power memory and logic applications, but its advancement remains limited by the scarcity of high-performance candidates and the generally weak out-of-plane polarization (OOP). Here, we demonstrate that metal intercalation provides a general and experimentally feasible strategy to activate and enhance sliding FE in transition metal dichalcogenide (TMD) heterobilayers. Through high-throughput calculations on 5264 pristine and intercalated configurations, systematically derived from accessible TMD monolayers and nine representative non-magnetic metal intercalants (Cu, Ag, Au, Pt, Zn, Cd, Hg, Ga, and In), we identify 234 intercalated systems with switchable OOP, including 190 that exceed the experimentally reported value for the MoS₂/WS₂ system, with some achieving OOP values up to 37× higher. This represents a 13-fold increase over pristine counterparts. Notably, Pt intercalation exhibits the most pronounced effect, delivering the strongest enhancement of OOP. To enable efficient exploration of this vast configuration space, we further introduce a crystal equivariant graph neural network that accurately predicts OOP directly from atomic structures (R² = 0.98), including both its magnitude and reversible directionality, thereby bypassing the need for computationally intensive DFT calculations. Together, these results elucidate the mechanistic role of interfacial intercalation in tuning symmetry breaking and interlayer coupling, and establish a scalable, machine learning-accelerated framework for the discovery of next-generation 2D sliding ferroelectrics with enhanced functional performance and broad technological relevance.