Constant chemical potential-quantum mechanical-molecular dynamics simulations of the graphene-electrolyte double layer.

We present the coupling of two frameworks-the pseudo-open boundary simulation method known as constant potential molecular dynamics simulations (CμMD), combined with quantum mechanics/molecular dynamics (QMMD) calculations-to describe the properties of graphene electrodes in contact with electrolytes. The resulting CμQMMD model was then applied to three ionic solutions (LiCl, NaCl, and KCl in water) at bulk solution concentrations ranging from 0.5 M to 6 M in contact with a charged graphene electrode.

Cleaving Method for Molecular Crystals and Its Application to Calculation of the Surface Free Energy of Crystalline β-d-Mannitol at Room Temperature.

Calculation of the surface free energy (SFE) is an important application of the thermodynamic integration (TI) methodology, which was mainly employed for atomic crystals (such as Lennard-Jones or metals). In this work, we present the calculation of the SFE of a molecular crystal using the cleaving technique which we implemented in the LAMMPS molecular dynamics package. We apply this methodology to a crystal of β-d-mannitol at room temperature and report the results for two types of force fields belonging to the GROMOS family: all atoms and united atoms.

Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks.

We present a general procedure to introduce electronic polarization into classical Molecular Dynamics (MD) force fields using a Neural Network (NN) model. We apply this framework to the simulation of a solid-liquid interface where the polarization of the surface is essential to correctly capture the main features of the system. By introducing a multi-input, multi-output NN and treating the surface polarization as a discrete classification problem, we are able to obtain very good accuracy in terms of quality of predictions.

Shuttleworth equation: A molecular simulations perspective.

Even though the study of interfacial phenomena can be traced back to Laplace and was given a solid thermodynamic foundation by Gibbs, it appears that some concepts and relations among them are still causing some confusion and debates in the literature, particularly for interfaces involving solids. In particular, the definitions of the concepts of interfacial tension, free energy, and stress and the relationships between them sometimes lack clarity, and some authors even question their validity. So far, the debates about these relationships, in particular the Shuttleworth equation, have taken place within the framework of classical thermodynamics.

Systematic derivation of hybrid coarse-grained models.

Molecular dynamics represents a key enabling technology for applications ranging from biology to the development of new materials. However, many real-world applications remain inaccessible to fully resolved simulations due to their unsustainable computational costs and must therefore rely on semiempirical coarse-grained models. Significant efforts have been devoted in the last decade towards improving the predictivity of these coarse-grained models and providing a rigorous justification of their use, through a combination of theoretical studies and data-driven approaches.

The accuracy of ab initio calculations without ab initio calculations for charged systems: Kriging predictions of atomistic properties for ions in aqueous solutions.

Using the machine learning method kriging, we predict the energies of atoms in ion-water clusters, consisting of either Cl or Na surrounded by a number of water molecules (i.e., without NaCl interaction).

Geometry Optimization with Machine Trained Topological Atoms.

The geometry optimization of a water molecule with a novel type of energy function called FFLUX is presented, which bypasses the traditional bonded potentials. Instead, topologically-partitioned atomic energies are trained by the machine learning method kriging to predict their IQA atomic energies for a previously unseen molecular geometry. Proof-of-concept that FFLUX's architecture is suitable for geometry optimization is rigorously demonstrated.

FEREBUS: Highly parallelized engine for kriging training.

FFLUX is a novel force field based on quantum topological atoms, combining multipolar electrostatics with IQA intraatomic and interatomic energy terms. The program FEREBUS calculates the hyperparameters of models produced by the machine learning method kriging. Calculation of kriging hyperparameters (θ and p) requires the optimization of the concentrated log-likelihood L̂(θ,p).

Kriging atomic properties with a variable number of inputs.

A new force field called FFLUX uses the machine learning technique kriging to capture the link between the properties (energies and multipole moments) of topological atoms (i.e., output) and the coordinates of the surrounding atoms (i.

Incorporation of local structure into kriging models for the prediction of atomistic properties in the water decamer.

Machine learning algorithms have been demonstrated to predict atomistic properties approaching the accuracy of quantum chemical calculations at significantly less computational cost. Difficulties arise, however, when attempting to apply these techniques to large systems, or systems possessing excessive conformational freedom. In this article, the machine learning method kriging is applied to predict both the intra-atomic and interatomic energies, as well as the electrostatic multipole moments, of the atoms of a water molecule at the center of a 10 water molecule (decamer) cluster.

Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.

The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e.

Solvent structuring and its effect on the polymer structure and processability: the case of water-acetone poly-ε-caprolactone mixtures.

One of the most common processes to produce polymer nanoparticles is the solvent-displacement method, in which the polymer is dissolved in a "good" solvent and the solution is then mixed with an "anti-solvent". The polymer processability is therefore determined by its structural and transport properties in solutions of the pure solvents and at the intermediate compositions. In this work, we focus on poly-ε-caprolactone (PCL) which is a biocompatible polymer that finds widespread application in the pharmaceutical and biomedical fields, performing full atomistic molecular dynamics simulations of one PCL chain of different molecular weight in a solution of pure acetone (good solvent), of pure water (antisolvent), and their mixtures.

A multiple time step scheme for multiresolved models of macromolecules.

In hybrid particle models where coarse-grained beads and atoms are used simultaneously, two clearly separate time scales are mixed. If such models are used in molecular dynamics simulations, a multiple time step (MTS) scheme can therefore be used. In this manuscript, we propose a simple MTS algorithm which approximates for a specific number of integration steps the slow coarse-grained bead-bead interactions with a Taylor series approximation while the atom-atom ones are integrated every time step.

Mixing atoms and coarse-grained beads in modelling polymer melts.

We present a simple hybrid model for macromolecules where the single molecules are modelled with both atoms and coarse-grained beads. We apply our approach to two different polymer melts, polystyrene and polyethylene, for which the coarse-grained potential has been developed using the iterative Boltzmann inversion procedure. Our results show that it is possible to couple the two potentials without modifying them and that the mixed model preserves the local and the global structure of the melts in each of the case presented.