Seifermann Maximilian, Reiser Patrick, Friederich Pascal, Levkin Pavel A
Institute of Biological and Chemical Systems-Functional Molecular Systems, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
Small Methods. 2023 Sep;7(9):e2300553. doi: 10.1002/smtd.202300553. Epub 2023 Jun 7.
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, 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.
由于化学空间巨大，功能性和响应性软材料的设计面临诸多挑战，但就可能的性能范围而言也提供了广泛的机会。在此，报道了一种用于功能性水凝胶库的小型化组合高通量筛选的实验工作流程。从对900多种不同类型水凝胶垫的光降解过程分析中产生的数据用于训练机器学习模型以进行自动决策。通过基于贝叶斯优化的迭代模型优化，响应性能得到了显著改善，从而扩大了本研究中水凝胶化学空间内可获得的材料性能范围。因此证明了将小型化高通量实验与智能优化算法相结合在成本和时间方面高效优化材料性能的潜力。
