Machine learning augmented design of 2D magnet with planar cyclo-tetranitrogen: ambient thermal stability from quantum to mesoscale

Abstract

Synthesizing polynitrogen compounds that remain stable at ambient conditions is particularly challenging because species beyond the N ≡ N triple bond are inherently unstable. In this study, we combine first-principles calculations with a machine-learning potential (MLP) to investigate the ambient stability of planar cyclo-N₄ units embedded in a two-dimensional t-FeN₄ monolayer. Our results show that strong Fe–N coordination inhibits N ≡ N reformation, enabling the square cyclo-N₄ motif to remain dynamically stable and covalently bonded without high-pressure synthesis. Furthermore, this structure exhibits tunable magnetic anisotropy and a Néel temperature above 600 K, indicating potential for room-temperature spintronic applications. The MLP also enables the simulation of systems comprising over 100,000 atoms, including periodic sheets, nanoribbons, nanomatrices and nanosheets, revealing their structural integrity under thermal fluctuations. These results demonstrate that two-dimensional confinement provides a promising route to stabilize exotic nitrogen topologies, linking quantum-mechanical accuracy with mesoscale modelling for future spin-based technologies.