Towards machine learning of molecular ensembles
Speaker: Jana Weber
Time: 10:30 - 11:15
Abstract: Synthetic polymers are a highly demanded class of materials that one finds in many different consumer products. AI-assisted in-silico design of molecules is becoming an increasingly valuable approach to accelerate molecular discovery and development, yet generative AI for synthetic polymers still needs to overcome domain-specific challenges. One challenge is that unlike for small molecules, synthetic polymers are governed by multiple structural levels of information, beyond the atomistic structure of monomers. This causes challenges for data collection and for machine-readable representations alike. Secondly, targeted, or controlled design of new materials requires much (property-) labelled data, which in the field of synthetic polymers is not yet easily accessible. In this talk, I will present our current works on molecular machine learning for copolymers. We build upon the representation of polymers as molecular graph ensembles [1] and work on the two challenges outlined above: learning with limited labelled data and learning beyond the atomistic representation of monomer units [2,3].
References:
[1]: Aldeghi, M., & Coley, C. W. (2022). A graph representation of molecular ensembles for polymer property prediction. Chemical Science, 13(35), 10486-10498.
[2]: Vogel, G., Sortino, P., & Weber, J. M. (2023). Graph-to-String Variational Autoencoder for Synthetic Polymer Design. In AI for Accelerated Materials Design-NeurIPS 2023 Workshop.
[3]: Gao, Q., Dukker, T., Schweidtmann, A. M., & Weber, J. M. (2024). Self-supervised graph neural networks for polymer property prediction. Molecular Systems Design & Engineering.