Automating reaction condition prediction for supramolecular organic assemblies

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Supramolecular synthesis is a powerful strategy for assembling complex molecules, but doing this by targeted design is challenging. Whilst there is some success in targeting specific molecules using chemical intuition, knowledge of how other synthetic variables such as solvent, temperature, and concentration, control the reaction outcome is limited, and arguably left to chance. We would like to move towards a data-driven approach to predicting the reaction outcomes but are hampered by the lack of high quality data in a sufficiently high quantity. Here, we propose to use high-throughput automation to carry out large numbers of reaction screens using both commercially available and simple synthetically viable precursors. This will fill the gap in data by generating thousands of data points across different precursor combinations for dynamic covalent chemistry, solvent choice, temperature choice, and reaction concentration, all of which will be recorded in an electronic lab notebook. With the available data, we will use supervised machine learning approaches to attempt to first predict the best synthetic conditions for a successful, "clean" reaction, and then to predict the reaction outcome (i.e. molecular mass). We will also apply atomistic modelling where possible using available software within the Jelfs group, to both determine which hits are the most promising to scale up and to investigate the physical reasons behind observed design rules.