Machine Learning Assisted High-Throughput Synthesis and 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. We developed 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 are used to train a machine learning model for automated decision making [1]. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties. This paves way for AI-accelerated high-throughput experimental screening for promising responsive soft materials.<br/><br/>[1] Seifermann, M., Reiser, P., Friederich, P. and Levkin, P.A., 2023. High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels. Small Methods, 7(9), p.2300553.