Statistical mechanics of constant-potential molecular simulations yields a new fluctuation–dissipation relation for the differential capacitance.

Electrochemistry is central to many applications, ranging from biology to energy science. Studies now involve a wide range of techniques, both experimental and theoretical. Modeling and simulations methods, such as density functional theory or molecular dynamics, provide key information on the structural and dynamic properties of the systems. Of particular importance are polarization effects of the electrode/electrolyte interface, which are difficult to simulate accurately. Here, we show how these electrostatic interactions are taken into account in the framework of the Ewald summation method. We discuss, in particular, the formal setup for calculations that enforce periodic boundary conditions in two directions, a geometry that more closely reflects the characteristics of typical electrolyte/electrode systems and presents some differences with respect to the more common case of periodic boundary conditions in three dimensions. These formal developments are implemented and tested in MetalWalls, a molecular dynamics software that captures the polarization of the electrolyte and allows the simulation of electrodes maintained at a constant potential. We also discuss the technical aspects involved in the calculation of two sets of coupled degrees of freedom, namely the induced dipoles and the electrode charges. We validate the implementation, first on simple systems, then on the well-known interface between graphite electrodes and a room-temperature ionic liquid. We finally illustrate the capabilities of MetalWalls by studying the adsorption of a complex functionalized electrolyte on a graphite electrode.

Classical molecular dynamics simulations have recently become a standard tool for the study of electrochemical systems. State-of-the-art approaches represent the electrodes as perfect conductors, modeling their responses to the charge distribution of electrolytes via the so-called fluctuating charge model. These fluctuating charges are additional degrees of freedom that, in a Born–Oppenheimer spirit, adapt instantaneously to changes in the environment to keep each electrode at a constant potential. Here, we show that this model can be treated in the framework of constrained molecular dynamics, leading to a symplectic and time-reversible algorithm for the evolution of all the degrees of freedom of the system. The computational cost and the accuracy of the new method are similar to current alternative implementations of the model. The advantage lies in the accuracy and long term stability guaranteed by the formal properties of the algorithm and in the possibility to systematically introduce additional kinematic conditions of arbitrary number and form. We illustrate the performance of the constrained dynamics approach by enforcing the electroneutrality of the electrodes in a simple capacitor consisting of two graphite electrodes separated by a slab of liquid water.

Understanding the dynamics of complex molecular processes is often linked to the study of infrequent transitions between long-lived stable states. The standard approach to the sampling of such rare events is to generate an ensemble of transition paths using a random walk in trajectory space. This, however, comes with the drawback of strong correlations between subsequently sampled paths and with an intrinsic difficulty in parallelizing the sampling process. We propose a transition path sampling scheme based on neural-network generated configurations. These are obtained employing normalizing flows, a neural network class able to generate statistically independent samples from a given distribution. With this approach, not only are correlations between visited paths removed, but the sampling process becomes easily parallelizable. Moreover, by conditioning the normalizing flow, the sampling of configurations can be steered towards regions of interest. We show that this approach enables the resolution of both the thermodynamics and kinetics of the transition region for systems that can be sampled using exact-likelihood generative models.

The time-reversal properties of charged systems in a constant external magnetic field are reconsidered in this paper. We show that the evolution equations of the system are invariant under a new symmetry operation that implies a new signature property for time-correlation functions under time reversal. We then show how these findings can be combined with a previously identified symmetry to determine, for example, null components of the correlation functions of velocities and currents and of the associated transport coefficients. These theoretical predictions are illustrated by molecular dynamics simulations of superionic AgI.