Diffusion–Model–Driven Discovery of Ferroelectrics for Photocurrent Applications

Ferroelectric materials are vital for next-generation memory and photovoltaic technologies, yet their discovery is limited to the few known prototypes. Here, we present a design framework that integrates diffusion model–based crystal generation with multi-fidelity screening to the discovery of ferroelectrics. Using MatterGen, we generated 12,800 candidate structures and applied a pipeline combining diverse machine-learning tools and density functional theory calculations. This process revealed two promising candidates, Ca3P2 and LiCdP, both insulating and switchable. The polarization value of Ca3P2 is 8.9 μC/cm^2 while that of LiCdP reaches as high as 144.1 μC/cm^2 which is comparable to Sc-doped AlN, one of the highest-polarization ferroelectrics reported to date. In addition, HSE06 calculations yield band gaps of 1.58 and 1.13 eV for Ca3P2 and LiCdP, respectively, suggesting strong potential for photocurrent applications. These findings establish new promising candidates for the ferroelectric family and demonstrate the power of generative models to uncover novel functional materials.