Computational quantum chemistry is a powerful tool for studying the mechanism and kinetics of complicated multi-step chemical processes such as radical polymerization. Unlike experiment, it allows for the direct calculation of the rate coefficients of the various chemical reactions taking place, without recourse to kinetic model fitting. It also provides useful mechanistic information that can help guide reagent design. However, to obtain accurate results, high-level ab initio procedures are required, and these are too computationally intensive to be applied directly to most practically relevant problems. We have been working toward adapting computational chemistry for these difficult problems through the design of small chemical models capable of mimicking the behaviour of real chemical systems, along with various cost-saving measures such as an efficient algorithm for conformational optimization. With these tools in hand we have demonstrated chemically accurate first principles predictions of rate coefficients in a range of conventional and controlled free-radical polymerization processes, and used these to develop accurate first principles kinetic models. We have also developed accurate protocols for first principles predictions of redox potentials, pKa values, polymer debonding temperatures and many other chemical processes. Our current efforts are focussed on extending our methodology to the study of more complex systems related to enzymes and to electronic devices.
Selected Recent Publications:
- Marenich, A.V., Ho, J., Coote, M.L., Cramer, C.J., & Truhlar, D.G. (2014) Computational Electrochemistry: Prediction of Liquid-Phase Reduction Potentials, Phys. Chem. Chem. Phys., 16, pp. 15068 - 15106.
- Noble, B.B. & Coote, M.L. (2013) First Principles Modelling of Free-Radical Polymerization Kinetics, Int. Rev. Phys. Chem., 32, pp. 467-512
- Ho, J., & Coote, M.L. (2011) First Principles Prediction of Acidities in the Gas and Solution Phase” WIREs Computational Molecular Science 1, pp. 649–660.