Front Cover: Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones (Adv. Eng. Mater. 13/2026)

Additive Gaussian Process Regression Silicone for high-resolution 3D printing are often limited in both mechanical and rheological properties. In their Research Article (10.1002/adem.202502575), Roxana Carbonell, Martha Leach, Hongtao Song, and Carolyn Seepersad present a framework for tuning silicones for vat photopolymerization. The framework improves upon conventional Gaussian process models by using additive Gaussian process regression, separating the inputs into component models with chemistry aware groupings. The image depicts separate Gaussian kernels generating smooth predictive trends that converge into a printed silicone octet-truss lattice.