High‐Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels

Author:

Seifermann Maximilian1,Reiser Patrick23ORCID,Friederich Pascal23ORCID,Levkin Pavel A.14ORCID

Affiliation:

1. Institute of Biological and Chemical Systems‐Functional Molecular Systems Karlsruhe Institute of Technology Hermann‐von‐Helmholtz‐Platz 1 76344 Eggenstein‐Leopoldshafen Germany

2. Institute of Nanotechnology Karlsruhe Institute of Technology Hermann‐von‐Helmholtz‐Platz 1 76344 Eggenstein‐Leopoldshafen Germany

3. Institute of Theoretical Informatics Karlsruhe Institute of Technology Am Fasanengarten 5 76131 Karlsruhe Germany

4. Institute of Organic Chemistry Karlsruhe Institute of Technology Fritz‐Haber‐Weg 6 Karlsruhe Germany

Abstract

AbstractDue 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, an experimental workflow for miniaturized combinatorial high‐throughput screening of functional hydrogel libraries is reported. 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. 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.

Publisher

Wiley

Subject

General Materials Science,General Chemistry

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