Closed-loop discovery of materials and catalysts with automation and machine learning

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There is an increasing drive to use new technologies, robotics, and automation in chemical research. Automated platforms can allow thousands of experiments to be carried out within a very short time. Characterisation data of the products of these reactions can also be rapidly collected using kit equipped with high-throughput autosamplers, including key analytical techniques such as mass spectrometry and NMR spectroscopy. However, the bottleneck in the discovery process then becomes the analysis of this huge amount of data - interpretation and assignment of this data is still largely carried out by a human, and the analysis of NMR spectra is very time consuming. This can lead to platforms lying idle for large periods of time while a human is analysing the outcome and determining which products were made. This project will focus on the development of software that automates the analysis of the NMR data collected during high-throughput screening workflows. This will include processing the raw data, peak picking, determining multiplets, and comparing these to chemical shifts calculated by computational chemistry simulations, finally allowing either confirmation of what products are present in a mixture or structural assignment. Combining these automated platforms with the developed software, including artificial intelligence algorithms, holds the promise of "closed loop" discovery where an initial set of reactions are analysed by software, and then based on the outcome, the algorithm decides on the next set of experiments to try and then run on the platform, for example to optimise the properties of a material or the selectivity of a catalyst. This will involve trialing a variety of optimisation approaches, such as using evolutionary algorithms or Bayesian optimisation. The approach developed will have wide applicability and will be tested against a variety of problems, including screening for the selectivity of a wide range of catalysts and for the identification of the successful synthesis of promising molecular materials. The project fits in the Digitisation/New Tools/Rapid Analysis theme from the Sandpits.