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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Deep learning based optimization of hydrogels and their funcationalization for applications in ophthalmology
Author(s) Berit Zeller-Plumhoff, Christina Gawlick, Carsten Wickmann, Stefan Oehmcke, André Schulz
On-Site Speaker (Planned) Berit Zeller-Plumhoff
Abstract Scope Hydrogels are promising implant materials for vitreous substitutes in ophthalmology. Their biomechanical functionality depends on their viscoelastic properties, specifically storage modulus G'. To accelerate the design of new hydrogels, we are developing a DL-based model. The present work is divided into two parts: First, we have evaluated different ML-models in an automated framework for the prediction of G' based on the hydrogel type, its copolymers and functionalization. To this end, PolyMetriX was used to generate input features based on the PSMILES string of the respective polymers. We found that TabPFN (v3) returns the lowest median absolute error. Moreover, we found that hydrogels based on hyaluronic acid can be predicted with lower errors than those based on polyethylene glycol. In the second step, we have established a variational autoencoder in combination with the ML-model to generate new hydrogels based on target G' values. The predicted structures are synthesized and tested experimentally.
Proceedings Inclusion? Planned:
Keywords Biomaterials, Machine Learning, Polymers

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