Publication date: 15th December 2025
We have been developing computational software towards assisting in the discovery of organic-based materials with targeted structures and properties. Here, we will explore the different ways in which both artificial intelligence (AI) and molecular simulation can assist in the discovery and understanding of a variety of materials for energy devices. This will include exploration of generative AI for the discovery of ionic liquids[1,2], the use of molecular simulation and machine learning forcefields to predict the structure and ion conductivity of polymer membranes for redox flow batteries,[3,4] and the use of optimisation algorithms to accelerate the discovery of new electrode materials.
[1] “Deep learning-enabled discovery of low-melting-point ionic liquids”, G. Ren, A. Mroz, F. Philippi, T. Welton, K. E. Jelfs, ChemRxiv, 2025.
[2] “Expanding the chemical space of ionic liquids using conditional variational autoencoders”, G. Ren, A. Mroz, F. Philippi, T. Welton, K.E. Jelfs, ChemRxiv, 2025.
[3] “Sulfonated poly(ether-ether-ketone) membranes with intrinsic microporosity enable efficient redox flow batteries for energy storage”, T. Wong, Y. Yang, R. Tan, A. Wang, Z. Zhou, Z. Yuan, J. Li, D. Liu, A. Alvarez-Fernandez, C. Ye, M. Sankey, D. Ainsworth, S. Guldin, F. Foglia, N. B. McKeown, K. E. Jelfs,* X. Li,* Q. Song,* Joule (2025), 9 (2), 101795.
[4] “Selective ion transport through hydrated micropores in polymer membranes”, A. Wang, C. Breakwell, F. Foglia, R. Tan, L. Lovell, X. Wei, T. Wong, N. Meng, H. Li, A. Seel, M. Sarter, K. Smith, A. Alvarez‐Fernandez, M. Furedi, S. Guldin, M. M. Britton, N. B. McKeown, K. E. Jelfs & Q. Song, Nature (2024), 635, 353.
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