Prediction of electrostatic properties for molecules is of vital importance in numerous research disciplines. In biochemistry, the electrostatic potential is a dominant factor determining the preference for functional states in biomolecules such as ligand-binding or protein-protein interactions. In material science, the function of nanoporous crystals such as zeolites and metal-organic frameworks for gas storage and separation applications rely on their ability to absorb polar molecules. In electrochemistry, the function of electrochemical cells relies on the diffusion of ions and the double-layer formation at the electrode surface. Computational modeling of these systems thereby requires an accurate description of the electrostatic interaction between the different components of these complex system. Although the ubiquitous role that long-ranged electric fields play in catalysis has been recognized, it is seldom used as a primary design parameter in the discovery of new catalytic materials. I will present results on how electric fields have been used to computationally optimize biocatalytic performance of a synthetic enzyme, and how they could be used as a unifying descriptor for catalytic design across a range of homogeneous and heterogeneous catalysts. While focusing on electrostatic environmental effects may open new routes toward the rational optimization of efficient catalysts, much more predictive capacity is required of theoretical methods to have a transformative impact in their computational design – and thus experimental relevance – when using electric field alignments in the reactive centres of complex catalytic systems. I will discuss some of these methodological advances from accurate many-body force fields under non-reactive approximations in classical molecular dynamics, to reactive force fields to describe chemical reactions where charge flow is an essential process.

Coarse graining electrons and computational optimization of electric fields for better catalysis design

ACS Fall 2021

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