Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex that it is impractical or impossible to model them by ab initio methods. For this reason there is a need for accurate potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by a number of functions. Some interactions are well understood and can be represented by simple mathematical functions while others are not so well understood and their functional form is represented in a simplistic manner or not even known. In the last 20 years there have been the first examples of a new design ethic, where novel and contemporary methods using machine learning, in particular, artificial neural networks, have been used to find the nature of the underlying functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development of future force fields.

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.

The present investigation demonstrates how configurational entropy stabilises rock-salt type single phase multi-component solid solution oxides. Classical simulations have been used to extensively sample the configurational landscape of such oxides using both random and genetic algorithm sampling strategies. The thermodynamic properties including the enthalpy and free energy of various oxide mixes have been calculated to show the influence of the chemical identity of the oxides on the phase stability. Additionally, a distance analysis between all the cation-cation and cation-anion pairs has been carried out in order to quantify the distortion in the lattice. The correlation between the multiplicity of cations in such systems with consequent enthalpy and configurational entropy has been enumerated and its relation with emergent distortion has been analysed.

It is widely accepted that correctly accounting for polarization within simulations involving water is critical if the structural, dynamic, and thermodynamic properties of such systems are to be accurately reproduced. We propose a novel potential for the water dimer, trimer, tetramer, pentamer, and hexamer that includes polarization explicitly, for use in molecular dynamics simulations. Using thousands of dimer, trimer, tetramer, pentamer, and hexamer clusters sampled from a molecular dynamics simulation lacking polarization, we train (artificial) neural networks (NNs) to predict the atomic multipole moments of a central water molecule. The input of the neural nets consists solely of the coordinates of the water molecules surrounding the central water. The multipole moments are calculated by the atomic partitioning defined by quantum chemical topology (QCT). This method gives a dynamic multipolar representation of the water electron density without explicit polarizabilities. Instead, the required knowledge is stored in the neural net. Furthermore, there is no need to perform iterative calculations to self-consistency during the simulation nor is there a need include damping terms in order to avoid a polarization catastrophe.

Intramolecular polarization is the change to the electron density of a given atom upon variation in the positions of the neighboring atoms. We express the electron density in terms of multipole moments. Using glycine and N-methylacetamide (NMA) as pilot systems, we show that neural networks can capture the change in electron density due to polarization. After training, modestly sized neural networks successfully predict the atomic multipole moments from the nuclear positions of all atoms in the molecule. Accurate electrostatic energies between two atoms can be then obtained via a multipole expansion, inclusive of polarization effects. As a result polarization is successfully modeled at short-range and without an explicit polarizability tensor. This approach puts charge transfer and multipolar polarization on a common footing. The polarization procedure is formulated within the context of quantum chemical topology (QCT). Nonbonded atom-atom interactions in glycine cover an energy range of 948 kJ mol(-1), with an average energy difference between true and predicted energy of 0.2 kJ mol(-1), the largest difference being just under 1 kJ mol(-1). Very similar energy differences are found for NMA, which spans a range of 281 kJ mol(-1). The current proof-of-concept enables the construction of a new protein force field that incorporates electron density fragments that dynamically respond to their fluctuating environment.