Exploring non-equilibrium molecular dynamics of mobile protons in the solid acid CsH2PO4 at the micrometer and microsecond scale.

We explicitly compute the non-equilibrium molecular dynamics of protons in the solid acid CsH2PO4 on the micrometer length scale via a multiscale Markov model: The molecular dynamics/matrix propagation (MDM) method. Within the MDM approach, the proton dynamics information of an entire molecular dynamics simulation can be condensed into a single M × M matrix (M is the number of oxygen atoms in the simulated system). Due to this drastic reduction in the complexity, we demonstrate how to increase the length and time scales in order to enable the simulation of inhomogeneities of CsH2PO4 systems at the nanometer scale. We incorporate explicit correlation of protonation dynamics with the protonation state of the neighboring proton sites and illustrate that this modification conserves the Markov character of the MDM method. We show that atomistic features such as the mean square displacement and the diffusion coefficient of the protons can be computed quantitatively from the matrix representation. Furthermore, we demonstrate the application potential of the scheme by computing the explicit dynamics of a non-equilibrium process in an 8 µm CsH2PO4 system during 5 ms.

Dynamical matrix propagator scheme for large-scale proton dynamics simulations.

We derive a matrix formalism for the simulation of long range proton dynamics for extended systems and timescales. On the basis of an ab initio molecular dynamics simulation, we construct a Markov chain, which allows us to store the entire proton dynamics in an M × M transition matrix (where M is the number of oxygen atoms). In this article, we start from common topology features of the hydrogen bond network of good proton conductors and utilize them as constituent constraints of our dynamic model. We present a thorough mathematical derivation of our approach and verify its uniqueness and correct asymptotic behavior. We propagate the proton distribution by means of transition matrices, which contain kinetic data from both ultra-short (sub-ps) and intermediate (ps) timescales. This concept allows us to keep the most relevant features from the microscopic level while effectively reaching larger time and length scales. We demonstrate the applicability of the transition matrices for the description of proton conduction trends in proton exchange membrane materials.

Proton mobility in aqueous systems: combining ab initio accuracy with millisecond timescales.

We present a multiscale simulation of proton transport in liquid water, combining ab initio molecular dynamics simulations with force-field ensemble averaging and kinetic Monte-Carlo simulations. This unique Ansatz allows for ab initio accuracy incorporating the femtosecond dielectric relaxation dynamics of the aqueous hydrogen bonding network, and bridges the time-scale gap towards the explicit simulation of millisecond diffusion dynamics.

A Coupled Molecular Dynamics/Kinetic Monte Carlo Approach for Protonation Dynamics in Extended Systems.

We propose a multiscale simulation scheme that combines first-principles Molecular Dynamics (MD) and kinetic Monte Carlo (kMC) simulations to describe ion transport processes. On the one hand, the molecular dynamics trajectory provides an accurate atomistic structure and its temporal evolution, and on the other hand, the Monte Carlo part models the long-time motion of the acidic protons. Our hybrid approach defines a coupling scheme between the MD and kMC simulations that allows the kMC topology to adapt continuously to the propagating atomistic microstructure of the system. On the example of a fuel cell membrane material, we validate our model by comparing its results with those of the pure MD simulation. We show that the hybrid scheme with an evolving topology results in a better description of proton diffusion than a conventional approach with a static kMC transfer rate matrix. Furthermore, we show that our approach can incorporate additional dynamical features such as the coupling of the rotation of a side group in the molecular building blocks. In the present implementation, we focus on ion conduction, but it is straightforward to generalize our approach to other transport phenomena such as electronic conduction or spin diffusion.

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.