窒素の二次元重合のFeNxにおける機械学習予測

Nitrogen-rich iron nitrides (FeN_x), a representative class of transition metal nitrides, have attracted a considerable amount of interest due to their polymeric nitrogen motifs and outstanding mechanical and energetic properties. However, accurately modeling such transition metal compounds within DFT+U frameworks requires careful U value selection. Moreover, no polymerized nitrogen structures beyond one-dimensional have been reported within the FeN_x system. Here, we develop a machine learning-integrated DFT+U (DFT+U_ML) approach to capture the Hubbard effects and systematically explore the FeN_x (x = 1, 2, 4, 6, 8, or 10) system. By applying this approach, we report for the first time a novel two-dimensional nitrogen-polymerized phase P2₁/c-FeN₄ and resolve the existing controversy regarding the ambient-pressure ground state of FeN. Remaining stable at 0 GPa, the P2₁/c-FeN₄ phase outperforms conventional chain-like FeN_x materials in both energetic and mechanic properties and exhibits potential for application. This study offers new strategies for designing nitrogen-rich energetic materials and guiding high-pressure synthesis.