Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Silicones are valued for durability, elasticity, low reactivity, and availability of biocompatible formulations, making them suitable for a wide range of applications. Additive manufacturing (AM), or 3D‐printing, enables production of complex, customized parts, but the limitations of AM‐compatible silicones restrict application‐based property tuning. This work introduces a material design framework for predictive formulation of UV‐curable polydimethylsiloxane (PDMS), a type of silicone, suitable for vat photopolymerization (VPP) and other UV‐based 3D‐printing processes. Additive Gaussian process regression models trained on custom PDMS formulations predict ultimate tensile strength and elongation at break of the cured polymer along with viscosity of the precursor resin. These properties serve as application and manufacturing driven screening criteria for computational formulation design. The PDMS formulations contain diverse combinations and quantities of siloxane base polymers, crosslinkers, and reinforcing fillers, while satisfying polymer network design constraints. Model structure is chemistry‐aware, using polymer network interactions to inform variable definitions and additive model components. The resulting models provide predictions of formulations of interest and support exploration of a diverse property range for UV‐cured and VPP PDMS.