Interatomic Potentials for Small Molecule Radical Reactions

Lead Research Organisation: University of Cambridge
Department Name: Chemistry


Year 1: Generic training activities for all first-year student members of the CDT.

Year 2-4: Machine-learned interatomic potentials are emerging tools in atomistic modelling. They bridge the gap between the accurate yet slow quantum-mechanical (QM) methods and computationally cheap, but not satisfyingly accurate classical force fields. Although they have been applied to modelling a range of materials and organic molecules, their use in modelling reactions is yet to be explored.

ML potentials are built on reference databases consisting of three-dimensional structures and energies and forces estimated with a highly accurate QM methods. As a result they are only applicable to structures not too dissimilar to ones the force fields were fitted to. Consequently, a lot of attention is devoted to composing the training data sets. A recent development is to use an imperfect potential to explore the chemical space and improve the potential with collected data. The challenge in our case is to determine how to similarly gather data for small organic molecules and radicals for the relatively constrained problem we are trying to solve.

We will narrow the scope of "modelling reactions of small molecules and radicals" by focusing on hydrogen abstraction reactions of methoxy radical and drug-like molecules, relevant to Cytochromes P450 metabolism. To model reactions, one would need to gather representative data for the equilibrium geometries of reactants and products as well as data sampled from the reaction path between them.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024220/1 31/05/2019 30/11/2027
2276986 Studentship EP/S024220/1 30/09/2019 29/09/2023 Elena Gelzinyte