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Abstract Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.

Polymeric nanoparticles were synthesized through Bergman cyclization mediated intramolecular chain collapse. Enediyne moieties were incorporated into linear polymers through copolymerization or post-polymerization modification. The resulted enediyne containing copolymers were then subjected to intramolecular chain collapse under ultra-dilute condition or applying a continuous addition technique. The occurrence of Bergman cyclization was confirmed with IR, NMR and DSC analysis. The intramolecular chain collapse was characterized with GPC, DSC, and AFM. Dramatic decreases of apparent molecular weights of linear copolymers were observed after Bergman cyclization. The structural diversity of enediyne precursors and mild chain collapse condition allow wide monomer selection, which facilitates preparation of a broad range of polymeric nanoparticles. Low-k dielectric thin films were prepared using polymeric nanoparticles through “porogen” approach. The packing tightness of polymeric nanoparticles showed strong influence on film formation behavior with MSSQ matrix. With tightly packed polymeric nanoparticles as sacrificial pore generators, thin films with dielectric constants of as low as 2.1 were obtained. [ABSTRACT FROM AUTHOR]