General Summary of ARF Research Project
Dr Natalie Fey
(since Oct. 2007)
Catalysts are often the unsung heroes of synthetic chemistry. Whilst we all know that a catalyst can be used to speed up a reaction, it is often not appreciated that the choice of catalyst can also control the selectivity of the reaction, thereby influencing its products. The key to catalysis is control. Consider a chemical reaction – it has a range of input variables, both chemical (such as the metal and ligands used in an organometallic catalyst) and environmental (such as temperature and pressure). If a reaction can give two products, A and B, then the right catalyst can be used to control the reaction so that the desired levels of A and B are produced. This is particularly important if we are only interested in one of the products, for example B, because otherwise we might have to separate A from B and could end up having to throw away any amounts of A produced.
Until now, chemists have usually identified suitable catalysts empirically – performing the same reaction many times, each time varying one of the inputs, until a satisfactory result is achieved. This process often has to be repeated for each new reaction, as the screening results may not be transferable. This is an inefficient and expensive process, which takes up significant time and resources with no guarantee that the result will indeed present the optimum solution.
The development of increasingly powerful yet affordable computers has revolutionised the study of organometallic catalysis. As Wendy Cornell, a director in the molecular systems group at Merck puts it, “we are able not only to do longer and bigger calculations, but we also have the opportunity to re-evaluate our basic approach to a scientific problem.” (Quoted in Chemical and Engineering News, September 27th 2004, pp. 35-40.)
This research project seizes this exciting opportunity by developing a faster, more accurate and more efficient computational approach to the study of organometallic catalysis. Firstly, a robust computational methodology for the study of catalytic cycles with real complexes will be developed. This is in contrast to most computational research currently undertaken, which relies on simplified, and therefore perhaps less accurate, models of the catalysts. Secondly, this methodology will be tested in the study of several important reactions, rhodium-catalysed hydroformylation and the family of palladium-catalysed cross-coupling reactions. By varying the ligands used in these reactions, the key steps of the catalytic cycles (i.e. where the modification of ligands has the greatest effect) will be identified. Thirdly, these key steps will be studied in greater detail, culminating in the construction of statistical models for predicting the outcome/selectivity of the reaction. Extension of the knowledge bases is straightforward, allowing both known and novel complexes to be considered computationally, even if they have not yet been synthesised.
The development of this approach will profoundly change the study of organometallic catalysis. For the first time, researchers will be able to work backwards from a desired outcome to determine the best combination of inputs. Rather than settling for input variables that produce a result that is ‘good enough’, researchers will be able to identify the optimum combination of inputs to achieve the desired outcome. This is an advancement of organometallic chemistry that is as important as it is exciting. It will make the study of homogeneous catalysis more accurate, more efficient and less wasteful, focusing the attention of researchers on the best candidates and the final knowledge outcome. This will be of inestimable benefit to all areas of chemical research, offering greener routes to desirable chemicals important in drug design, polymers and plastics as well as agricultural fertilisers.
The proposed research will change the way new catalytic systems are developed by establishing a quantitative basis for catalyst modification, as well as developing a large-scale, systematic knowledge framework of descriptors for theoretical screening. This will be of significant and positive benefit to industrial and academic researchers, as well as providing a positive pedagogic outcome for teachers and students of organometallic chemistry. It will serve to streamline the development of new catalysts, thereby improving the efficiency and reducing the financial and environmental cost of screening experiments. In addition, this work will provide an improved approach to the investigation of realistically sized, experimentally relevant molecules, which will extend the frontiers of this application beyond the study of organometallic catalysis. This will be of great interest to researchers in all areas of computational chemistry.
Last updated on 21 January 2008.