Accelerating catalyst design using reaction-path data mining

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Catalysis underpins the £3,500B/year global chemical industry, enabling new routes to synthesising new antibiotics, removing air pollution from the air we breathe, or turning industrial waste into useful products such as plastics. In short, without catalysis, many of the products, drugs, fuels and materials we take for granted would simply not exist.Unfortunately, the design of new catalysts with targeted properties remains an enormous challenge to industry and academia. The key reason is complexity; contemporary heterogeneous and nanoparticle catalysts can exhibit a mind-boggling range of reaction sites and pathways, and catalyst activity can depend (often in an ill-defined manner) on a wide range of features such as structure, composition, support interactions, temperature, pressure, reactant phase constituents, and by-product poisoning. This enormous chemical complexity is a direct barrier to the traditional trial-and-error synthetic approaches to catalyst design used the world over.But, what if we could teach computers to automatically design new, better, catalytic species instead? This would have a transformative impact on catalysis research, both in academia and industry; using computers to accurately predict the optimal catalyst for a reaction would cut down time wasted in trial-and-error synthesis, accelerate catalyst discovery and improve sustainability. However, automated computational design of catalysts has proven elusive to date; again, the same issue of chemical complexity which dogs experimental catalyst design similarly hinders computational methods.This project aims to change this situation, pushing us towards development of a "black box" strategy for computational catalyst design. Specifically, we will begin to address this challenge using path-constrained molecular dynamics (PCMD), a new computational approach developed recently by the PI. PCMD is a connectivity-driven sampling strategy which enables rapid generation of reaction paths connecting large numbers of different chemical species; combined with quantum-chemical calculations of reaction rates and kinetic modelling, PCMD underpins a hierarchical strategy which can predict trends in rate laws, selectivities and product yields arising as a result of changes to catalyst features. To the best of our knowledge, PCMD was the first automated "black box" strategy shown capable of predicting the emergent mechanism and rate law of complex catalytic transformations such as alkene hydroformylation. In the first industrial application of PCMD, we will seek to generate new insights into the reactive chemistry of nanoparticle and heterogeneous catalytic systems for exhaust emissions control. In collaboration with Johnson Matthey, a world-leader in emissions control technologies, we will use PCMD to develop a 'roadmap' of reaction mechanisms, thermodynamics and kinetics of key exhaust gas reactions on nanoparticle and heterogeneous catalysts, specifically carbon monoxide oxidation and nitrogen oxide reduction on metallic nanoparticles and in Cu-promoted zeolites. In addition, building new collaborations with Warwick Data Science Institute and The Alan Turing Institute, we will apply 'big data' statistical analyses of the (potentially enormous) reaction-path datasets generation by PCMD; this leads to the new concept of reaction-path data mining (RDM), which will transform reaction-path datasets into tangible insights and descriptors of catalyst function. Overall, our PCMD/RDM strategy represents a new direction for computational catalysis; by dramatically accelerating the development and application of this strategy, this project will be a critical milestone towards our ultimate long-term goal, namely the "black box" computational design of new catalysts, molecular and other functional chemical systems.