(419c) QM - Camd: Advances in Computer-Aided Molecular Design of Solvents for Reactions

Galindo, A., Imperial College London
Adjiman, C. S., Imperial College London

QM - CAMD:  Advances in Computer-Aided Molecular Design of Solvents for Reactions

Eirini Siougkrou, Amparo Galindo and Claire S. Adjiman

Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK



Many liquid-phase reactions take place in a solvent. The role of the solvent is significant, especially in the control of reaction rates and temperature; a change of solvent can change both the rate and the order of a chemical reaction. Thus, the ability to predict the behaviour of a reaction in a solvent and to choose the most appropriate solvent are crucial challenges. In this work a methodology is developed for the design of optimal solvents for reactions combining Quantum Mechanical (QM) calculations with a Computer Aided Molecular Design (CAMD) formulation. In order to limit the number of QM calculations but also retain accuracy, the kriging1 approach is used. Kriging is a response surface approach originally developed in geostatistics. It has recently attracted a lot of attention in other scientific areas as well as it is an exact extrapolator with a statistical interpretation which makes it stand out from other methods.

In this approach, starting with calculating the rate constant for a small number of solvents using QM, a kriging response surface of the rate constant as a function of seven solvent properties (dielectric constant, acidity, basicity, surface tension, refractive index, halogenicity and aromaticity) is built.  Then the kriging predictor is included in the CAMD formulation as the objective function that needs to be maximised and the optimal solvent based on this predictor is designed.  The rate constant in the current optimal solvent is calculated from QM and, if it does not agree with the kriging result, it is included in the set of solvents used to construct the kriging surface. The kriging surface is rebuilt and the procedure iterates until convergence between QM and kriging is achieved. Conventional transition state theory and the SMD2 solvation model are used for the QM calculations. The proposed methodology is applied successfully to two chemical reactions which exhibit large solvent effects and the results are discussed.


[1]    D. R. Jones, A taxonomy of global optimization methods based on response surfaces, Journal of Global Optimization, 2001, 21, 345-383

[2]   A. V. Marenich, C. J. Cramer, D. G. Truhlar, Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions, Journal of Physical Chemistry B, 2009, 113, 6378-6396