Property-Guided Diffusion for Inverse Design of Crystalline Materials

Abstract

Diffusion-based generative models with property guidance have emerged as a promising paradigm for inverse materials design by enabling the generation of crystalline materials with user-specified target properties. However, despite recent advances, the effectiveness of property guidance, its influence on crystallographic symmetry, and the physical viability of generated materials remain poorly understood. To address these questions, we develop a property-guided framework based on the lightweight diffusion model DiffCrysGen using parameter-efficient adapter fine-tuning and classifier-free guidance (CFG). The resulting framework enables efficient multi-property crystal generation while preserving the knowledge learned during unconditional pre-training. Using formation energy together with saturation magnetization and Vickers hardness as representative inverse-design tasks, we systematically investigate the influence of CFG across a broad range of guidance strengths. Increasing the guidance scale progressively steers the generated property distributions toward the prescribed targets while reducing the fraction of lowest-symmetry ($P1$) structures and increasing the proportion of higher-symmetry structures. To evaluate physical viability, generated structures are geometrically prescreened and subsequently validated using a machine-learning interatomic potential (MLIP)-based workflow comprising structural relaxation and thermodynamic, dynamical, and property-specific analyses. The framework identifies thermodynamically and dynamically stable magnetic and mechanically hard materials with overall success rates of 12.3\% and 3.9\%, respectively. These results establish property-guided DiffCrysGen as an efficient framework for inverse materials design while providing new insights into the role of classifier-free guidance in crystal generation.