Accelerating catalyst design using reaction-path data mining

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.

Building on recent work in the PI's group, this project introduces the concepts of path-constrained molecular dynamics (PCMD) and reaction-path data mining (RDM) as strategies for investigating the chemistry of complex catalytic systems. Specific impacts which will emerge during the project lifetime (3 yrs) are:

(i) A new tool for Computational Chemists: RDM is a completely unique approach within the global chemistry community; as such, this project will add a significant new strategy to the computational chemist's toolbox of methods for providing mechanistic insights and designing new functional molecules, nanoparticles and heterogeneous systems. RDM will be popularised by disseminating high-quality outputs throughout the project, thereby inviting new collaborations, as well as through a website which will catalogue and visualise chemical reaction-path datasets generated in our simulations.

(ii) Knowledge transfer to/from project partner and collaborators: Major UK-based chemical companies, notably Johnson Matthey (JM; project partner) and Syngenta, have expressed an interest in exploring application of PCMD/RDM in rationalisation of catalytic mechanisms and design of new catalysts and synthetic routes; by offering new insights into efficiency, reactivity and selectivity, PCMD/RDM has potential to accelerate development of new catalysts, saving time and money. Knowledge transfer to/from JM will be achieved by regular monitoring meetings throughout the project. In addition, we will invite industrial research scientists from UK-based industry to 'catalyst catapult' and Creativity@Home workshops proposed as part of this project. Furthermore, regular interaction with Alan Turing Institute (ATI) and Warwick Data Science Institute (WDSI), including engagement in WDSI/ATI activities (e.g. seminars, software development events), will strengthen the scientific objectives of this proposal; in addition, by providing an application domain which expands current WDSI/ATI remits, this project has potential to help expand the interdisciplinary impact of these hubs.

(iii) Collaboration with industrial and academic catalysis community: This project will be used as a springboard to engage with current UK catalysis research, including UK Catalysis Hub and Dial-a-Molecule consortia, leading to new collaborations and expanding the breadth of research domains in these groups. This can be achieved by inviting representatives from these consortia to the scoping workshops proposed in this project, as well as PI and PDRA engagement within these communities.

(iv) People and skills: The appointed PDRA (as well as any additional researchers appointed through, for example, CDTs) will receive exceptional training in state-of-the-art computational chemistry; in addition, outstanding training in software development will be provided by interaction with the Software Sustainability Institute and Warwick's Research Software Engineers.

Potential long-term impacts (>3 yrs) include:

(i) Societal impact via emerging chemical systems for air quality and exhaust emissions control: JM is a world-leader in this field, such that new catalytic developments and research directions emerging from this project could ultimately translate into major impacts on air-quality, societal health, and sustainability if translated into application technologies by JM.

(ii) Molecular design consultancy: A longer-term ambition is to develop a Warwick-based consultancy which exploits methodological developments such as PCMD/RDM, as well as local high-performance computing power, to design new functional molecules, nanoparticles and solid surfaces to meet specific industrially-driven requirements. It is hoped that collaborations and networks, as well as high-quality outputs, generated in this project will act as a portfolio to encourage engagement with this molecular design consultancy, offering potential long-term impact for this project.