Automatic Prediction and Characterisation of Complex Chemical Reactions

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Around 90% of all chemical processes use catalysis to control reactivity and selectivity, yet the design of new catalysts too often depends on informed trial-and-error to make progress. This project will directly address the challenge of catalyst design by developing new simulation tools based on network theory, big data and machine learning. We will develop new computational methods for auto-discovery of chemical reactions, enabling us to build microkinetic models to predict the outcomes of real-world laboratory experiments. This multi-scale approach will provide a new direct route towards catalyst design and optimization.