An interaction between two atoms, bonded or non-bonded, consists of interatomic contributions: electrostatic energy, exchange energy and electronic correlation energy. Together with the intra-atomic energy of an atom, these contributions are the basic components of the Interacting Quantum Atom (IQA) energy decomposition scheme. Here, we investigate IQA's proper use in conjunction with an explicit implementation of the B3LYP functional. The recovery of the total molecular energy from the IQA components is emphasised, for the first time. A systematic study of three model systems of biological relevance, N-methylacetamide (NMA), the doubly capped tripeptide GlyGlyGly and an alloxan dimer, shows the stabilization effect of B3LYP on most of the interatomic exchange energies (V^AB _X) compared to their Hartree–Fock values. Diagrams of exchange energies versus interatomic distance show the clustering of interactions, one cluster for each 1,n (n = 1 to 6 where the atoms are separated by n − 1 bonds). The positioning of some V^AB _X values outside their expected cluster marks interesting interactions.

Ab initio quantum chemistry is an independent source of information supplying an ever widening group of experimental chemists. However, bridging the gap between these ab initio data and chemical insight remains a challenge. In particular, there is a need for a bond order index that characterizes novel bonding patterns in a reliable manner, while recovering the familiar effects occurring in well-known bonds. In this article, through a large body of calculations, we show how the delocalization index derived from Quantum Chemical Topology (QCT) serves as such a bond order. This index is defined in a parameter-free, intuitive and consistent manner, and with little qualitative dependency on the level of theory used. The delocalization index is also able to detect the subtler bonding effects that underpin most practical organic and inorganic chemistry. We explore and connect the properties of this index and open the door for its extensive usage in the understanding and discovery of novel chemistry.

To model liquid water correctly and to reproduce its structural, dynamic and thermodynamic properties warrants models that account accurately for electronic polarisation. We have previously demonstrated that polarisation can be represented by fluctuating multipole moments (derived by quantum chemical topology) predicted by multilayer perceptrons (MLPs) in response to the local structure of the cluster. Here we further develop this methodology of modeling polarisation enabling control of the balance between accuracy, in terms of errors in Coulomb energy and computing time. First, the predictive ability and speed of two additional machine learning methods, radial basis function neural networks (RBFNN) and Kriging, are assessed with respect to our previous MLP based polarisable water models, for water dimer, trimer, tetramer, pentamer and hexamer clusters. Compared to MLPs, we find that RBFNNs achieve a 14–26% decrease in median Coulomb energy error, with a factor 2.5–3 slowdown in speed, whilst Kriging achieves a 40–67% decrease in median energy error with a 6.5–8.5 factor slowdown in speed. Then, these compromises between accuracy and speed are improved upon through a simple multi-objective optimisation to identify Pareto-optimal combinations. Compared to the Kriging results, combinations are found that are no less accurate (at the 90th energy error percentile), yet are 58% faster for the dimer, and 26% faster for the pentamer.

Atomistic simulation of chemical systems is currently limited by the elementary description of electrostatics that atomic point-charges offer. Unfortunately, a model of one point-charge for each atom fails to capture the anisotropic nature of electronic features such as lone pairs or π-systems. Higher order electrostatic terms, such as those offered by a multipole moment expansion, naturally recover these important electronic features. The question remains as to why such a description has not yet been widely adopted by popular molecular mechanics force fields. There are two widely-held misconceptions about the more rigorous formalism of multipolar electrostatics: (1) Accuracy: the implementation of multipole moments, compared to point-charges, offers little to no advantage in terms of an accurate representation of a system's energetics, structure and dynamics. (2) Efficiency: atomistic simulation using multipole moments is computationally prohibitive compared to simulation using point-charges. Whilst the second of these may have found some basis when computational power was a limiting factor, the first has no theoretical grounding. In the current work, we disprove the two statements above and systematically demonstrate that multipole moments are not discredited by either. We hope that this perspective will help in catalysing the transition to more realistic electrostatic modelling, to be adopted by popular molecular simulation software.

In this perspective, we explain the strategy behind QCTFF, the current name for a novel atomistic protein force field. The atoms are constructed using Quantum Chemical Topology (QCT). These topological atoms determine how a system's energy is partitioned. We give a brief account of the results hitherto obtained, and a glimpse of unpublished results. Combining this QCT partitioning with the universal quantum expression of energy, leads to four types of fundamental energy contributions. The first of these is intra‐atomic and the remaining three interatomic: (i) atomic self‐energy, (ii) Coulomb energy, (iii) exchange energy, and (iv) correlation energy. All structural and dynamic effects emerge from the interplay of these contributions. The machine learning method kriging captures well how they change in response to a change in nuclear configuration. Second, the Coulomb energy is represented by a converging multipolar series expansion when the nuclei are sufficiently far apart. © 2015 The Authors International Journal of Quantum Chemistry Published by Wiley Periodicals, Inc.