Inverse Design of Nanoparticulate Materials.

The unique size- and shape-dependent properties of nanomaterials offer a rich parameter space for tailoring functionalities and applications. Recently, inverse design of such nanoparticulate systems has provided a paradigm shift from empirical trial-and-error approaches toward predictive, model-driven design strategies to achieve desired functionalities. This perspective presents a practical framework for applying inverse design to nanoparticulate materials. We distinguish between two general modeling strategies: knowledge-based design, grounded in a detailed understanding of the underlying physics and chemistry, and data-based design, based on experimental or simulated input-output datasets. Hybrid models bridge these two strategies. Each strategy is further structured into three levels of optimization: (i) process optimization via process functions connecting synthetic parameters with resulting particle properties; (ii) structure optimization via property functions connecting particle properties with macroscopic properties; and (iii) full inverse design via combined property-process relationships. In a tutorial style, we introduce practical steps for model development, calibration, and implementation and discuss design rules to guide the choice of modeling strategy. This perspective thus aims to facilitate the broad adoption of inverse design for nanoparticulate systems, laying the foundation for the development of rigorously optimized, application-specific materials with ideal properties tailored to a given application.