Predictive multiscale free energy simulations of hybrid transition metal catalysts

Catalysis is a key area of fundamental science which underpins a high proportion of manufacturing industry. Developments in catalytic science and technology will also be essential in achieving energy and environmental sustainability. Progress in catalytic science requires a detailed understanding of processes at the molecular level, in which computation now plays a vital role. When used in conjunction with experiment, computational modelling is able to characterise structures, properties and processes including active site structures, reaction mechanisms and increasingly reaction rates and product distributions. However, despite the power of computational catalysis, currently available methods have limitations in both accuracy and their ability to model the reaction environment. Also, it is practically difficult to model hybrid catalysts, which combine elements of different types of catalyst (e.g. unnatural metal centres incorporated in natural enzymes). Advances in technique are essential if the goal of catalysis by design is to be achieved.

A powerful, practical approach to modelling catalytic processes is provided by Quantum Mechanical/Molecular Mechanical (QM/MM) methods, in which the reaction and surroundings are described using an accurate quantum mechanical approach, with the surrounding environment modelled by more approximate classical forcefields. QM/MM has been widely and successfully employed in modelling enzymatic reactions (recognised in the 2013 Nobel prize for Chemistry) but has an equally important role in other areas of catalytic science.

The flagship ChemShell code, developed by the STFC team in collaboration with UCL, Bristol and other groups around the world, is a highly flexible and adaptable open source QM/MM software package which allows a range of codes and techniques to be used in the QM and MM regions (www.chemshell.org). The software has been widely and successfully used in modelling enzymatic reactions and catalytic processes in zeolites and on oxide surfaces. It will provide the ideal platform for the developments we are proposing which will take computational catalysis to the next level. These will include the use of high level QM techniques to achieve chemical accuracy, accurate modelling of solvent effects, calculation of spectroscopic signatures allowing direct interaction with experiment, and dynamical approaches for free energy simulations. Crucially, we will bring together methods from different spheres of computational catalysis to enable modelling of hybrid catalytic systems. We will develop flexible and rigorous methods that meet the twin challenges of high-level QM treatment for accuracy with the ability to sample dynamics of the reacting system. Together these methods will allow accurate and predictive modelling of catalytic reactions under realistic conditions. The project will also anticipate the software developments needed to exploit the next generation of exascale high performance computing.

We will apply these new techniques to model the catalytic behaviour of a range of engineered heterogeneous, homogeneous and biomolecular catalysts, currently under study in the UK Catalysis Hub. The Hub supports experimental and computational applications across the whole UK catalysis community. This project will provide method development and software engineering that is not covered by the Hub, and thus will complement EPSRC investment in the Hub. Specific systems include methanol synthesis using homogeneous ruthenium complexes, Cu-based artificial enzymes for enantioselective Friedel-Crafts reactions, fluorophosphite-modified rhodium systems for hydroformylation catalysis of alkenes, and non-canonical substitutions in non-heme iron enzymes for C-H functionalisations. These highly topical and potentially industrially relevant systems will allow us both to test and exploit the new software, which promises a step change in our ability to model catalytic systems and reactions.