Ferrogen: Generative Pipeline for Guided Search of Novel Ferroelectric Material for Logic and Memory

Discovery of novel materials for physical memory is essential for superior memory systems enabling AI hardware scaling. Among them, ferroelectrics hold promise for highly scalable DRAM. Yet their discovery remains driven largely by human intuition and exhaustive database screening. We present Ferrogen, a generative pipeline for the targeted discovery of novel ferroelectric materials for memory by fine-tuning the diffusion-based crystal structure generator Mattergen on ferroelectric-relevant properties. To enable property-conditioned generation, we construct a ML-labeled dataset using a suite of fast machine learning estimators, including a novel two-stage ensemble polarization predictor, dramatically reducing reliance on high-throughput Density Field Theory (DFT) during training and screening. The base Mattergen model is fine-tuned via lightweight adapter layers conditioned on polarization, switching energy, and metal-probability embeddings, with classifier-free guidance enabling targeted generation of candidates with high polarization, low but finite switching barriers, and insulating character. Generated candidates are screened by the same ML estimators and validated through rigorous DFT calculations, including structural relaxation, band gap verification, and Berry phase polarization computation. Against the ML screens, Ferrogen achieves a roughly $80 \times$ improvement in search efficiency over exhaustive database search. The screened candidates are then finally validated using DFT and made publicly available as a database of theoretically confirmed novel ferroelectrics. The pipeline is readily extensible to other functional material classes, establishing generative models as a powerful paradigm for accelerating electronic materials discovery for high performance memory.