Energetics of Electron Pairs in Electrophilic Aromatic Substitutions

Ángel Martín Pendás,Julia Contreras-García,Miguel Gallegos,Julen Munárriz

doi:10.3390/molecules26020513

Ángel Martín Pendás, Julia Contreras-García + Show 2 more

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https://doi.org/10.3390/molecules26020513

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Abstract

The interacting quantum atoms approach (IQA) as applied to the electron-pair exhaustive partition of real space induced by the electron localization function (ELF) is used to examine candidate energetic descriptors to rationalize substituent effects in simple electrophilic aromatic substitutions. It is first shown that inductive and mesomeric effects can be recognized from the decay mode of the aromatic valence bond basin populations with the distance to the substituent, and that the fluctuation of the population of adjacent bonds holds also regioselectivity information. With this, the kinetic energy of the electrons in these aromatic basins, as well as their mutual exchange-correlation energies are proposed as suitable energetic indices containing relevant information about substituent effects. We suggest that these descriptors could be used to build future reactive force fields.

Highlights

Computational and theoretical chemistry (CTC) has undoubtedly come of age
We start by considering briefly the topology of the Electron Localization Function (ELF) gradient field, which is shown in Figure 1 for some representative examples
We have shown that inductive and mesomeric effects can be grasped from the behavior of the population of the aromatic valence bond basins

Summary

Introduction

Computational and theoretical chemistry (CTC) has undoubtedly come of age. Advances in both electronic structure methods and computer technology allow researchers to perform simulations of realistic systems with outstanding accuracy within affordable times, so that CTC is already playing a crucial role in fields like big pharma industry or materials design [1,2,3,4,5,6]. Machine learning (ML), on the other hand, has fully revolutionized the field [7,8], and problems which were once thought to be out of reach in a foreseeable future, like protein folding, are close to being solved [9] Given this favorable scenario, it should never be forgotten that the simulation of complex systems, or the training of deep neural networks, rely at one point or another on human understanding of an underlying energy model, be it a well parameterized force field (FF) in the first case, or a specific neural network (NN) in the second. Atomistic models [11] already introduced the idea that pairwise additive pair potentials were essentially enough for simulation purposes, with smaller three body corrections that could be added ad hoc, if necessary This mantra was inherited in the first wide purpose force fields and has remained practically untouched to date

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