Computation-guided discovery of supramolecular materials Kim Jelfs Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, Wood Lane, London, W12 0BZ, United Kingdom We have been developing computational software towards assisting in the discovery of molecular materials with targeted structures and properties. Whilst initially we have focused upon porous molecular materials, we will also address the ways in which our approach is generalisable to other molecular materials and their applications, including as organic semiconductors or for photocatalysis. Intrinsically porous organic molecules have shown promise in separations, catalysis, encapsulation, sensing, and as porous liquids. These molecules are typically synthesised from organic precursors through dynamic covalent chemistry (DCC). If we consider cages synthesised from imine condensation reactions alone, there are approximately 800,000 possible aldehyde and amine precursors, combining these in all the different possible topologies results in over 830 million possible porous organic cages. Therefore, either from a computational or synthetic perspective, it is not possible for us to screen all these possible assemblies.Our evolutionary algorithm automates the assembly of hypothetical molecules from a library of precursors. The software belongs to the class of approaches inspired by Darwin's theory of evolution and the premise of "survival of the fittest".Our approach has already suggested promising targets that have been synthetically realised. Further, we are addressing questions such as which topologies or DCC reactions maximise void size or whether specific chemical functionalities promote targeted applications. We will also discuss ways in which the use of artificial intelligence techniques can assist in the field. We have also examined the application of both supervised machine learning and graph neural networks for the rapid prediction of properties for several different classes of systems. We have also used transfer learning and generative models to predict new supramolecular systems with target properties, even in cases where there is a low volume of data. Finally, we have trained a machine learning model to reproduce the “chemical intuition” of a chemist to guide our predictions to select materials that have a high chance of being synthesisable in the laboratory. References 1. “Into the Unknown: How Computation Can Help Explore Uncharted Material Space” (Perspective), A. M. Mroz, V. Posligua, A. Tarzia, E. H. Wolpert, K. E. Jelfs, J. Am. Chem. Soc. (2022), 144, 41, 18730-18743. 2. “Unlocking the computational design of metal-organic cages”, A. Tarzia, K. E. Jelfs, Chem. Commun. (2022), 58, 3717 - 3730. 3. “Materials Precursor Score: Modelling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cages”, S. Bennett, F. T. Szczypiński, L. Turcani, M. E. Briggs, R. L. Greenaway, K. E. Jelfs, J. Chem. Inf. Model. (2021), 61, 9, 4342–4356. 4. “High-throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design”, A. Tarzia, J. Lewis,* K. E. Jelfs,Angew. Chem. Int. Ed. (2021), 60, 20879–20887. 5. “Stk: An Extendable Python Framework for Automated Molecular and Supramolecular Structure Assembly and Discovery”, L. Turcani, A. Tarzia, F. Szczypiński, K. E. Jelfs, J. Chem. Phys. (2021) 154, 214102.
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