High-throughput synthesis and machine learning assisted design of photodegradable hydrogels

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein we report an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads is used to train a machine learning (ML) model for automated decision making. Through iterative model optimization based on Bayesian Optimization we achieve a substantial improvement in response properties and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in our study. We therefore demonstrate the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties.