Automated Data Analysis (D2.1)

The goal of Project D2.1 is to build data-driven, self-driving high-throughput labs where machine-learning models continuously analyze experimental data, propose new organic and inorganic formulations, and iteratively optimize polymers and hydrogel-based inks for laser 3D printing. By learning explainable structure–processing–property–function relationships, these models will move from fast optimization in constrained design spaces to generalizable surrogates that can extrapolate to unseen materials, guide targeted, autonomous chemical synthesis, and accelerate the discovery of tailored materials for specific applications.