Spectral Denoising for Accelerated Analysis of Correlated Ionic Transport.

Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive, as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displacement covariance to learn a set of diffusion eigenmodes that encode the correlation structure and form a basis for analyzing the ionic trajectories. This allows systematic reduction of the uncertainty and accelerate computations of ionic conductivity in systems with a steady-state correlation structure. We provide mathematical and numerical proofs of the method's robustness and demonstrate it on realistic electrolyte materials.

Gauge Fixing for Heat-Transport Simulations.

Thermal and other transport coefficients were recently shown to be largely independent of the microscopic representation of the energy (current) densities or, more generally, of the relevant conserved densities/currents. In this Article, we show how this gauge invariance, which is intimately related to the intrinsic indeterminacy of the energy of individual atoms in interacting systems, can be exploited to optimize the statistical properties of the current time series from which the transport coefficients are evaluated. To this end, we introduce and exploit a variational principle that relies on the metric properties of the conserved currents, treated as elements of an abstract linear space. Different metrics would result in different variational principles. In particular, we show that a recently proposed data-analysis technique based on the theory of transport in multicomponent systems can be recovered by a suitable choice of this metric.

Enhanced Proton Conductivity in Y-Doped BaZrO3 via Strain Engineering.

The effects of stress-induced lattice distortions (strain) on the conductivity of Y-doped BaZrO3, a high-temperature proton conductor with key technological applications for sustainable electrochemical energy conversion, are studied. Highly ordered epitaxial thin films are grown in different strain states while monitoring the stress generation and evolution in situ. Enhanced proton conductivity due to lower activation energies is discovered under controlled conditions of tensile strain. In particular, a twofold increased conductivity is measured at 200 °C along a 0.7% tensile strained lattice. This is at variance with conclusions coming from force-field simulations or the static calculations of diffusion barriers. Here, extensive first-principles molecular dynamic simulations of proton diffusivity in the proton-trapping regime are therefore performed and found to agree with the experiments. The simulations highlight that compressive strain confines protons in planes parallel to the substrate, while tensile strain boosts diffusivity in the perpendicular direction, with the net result that the overall conductivity is enhanced. It is indeed the presence of the dopant and the proton-trapping effect that makes tensile strain favorable for proton conduction.

Accurate thermal conductivities from optimally short molecular dynamics simulations.

The evaluation of transport coefficients in extended systems, such as thermal conductivity or shear viscosity, is known to require impractically long simulations, thus calling for a paradigm shift that would allow to deploy state-of-the-art quantum simulation methods. We introduce a new method to compute these coefficients from optimally short molecular dynamics simulations, based on the Green-Kubo theory of linear response and the cepstral analysis of time series. Information from the full sample power spectrum of the relevant current for a single and relatively short trajectory is leveraged to evaluate and optimally reduce the noise affecting its zero-frequency value, whose expectation is proportional to the corresponding conductivity. Our method is unbiased and consistent, in that both the resulting bias and statistical error can be made arbitrarily small in the long-time limit. A simple data-analysis protocol is proposed and validated with the calculation of thermal conductivities in the paradigmatic cases of elemental and molecular fluids (liquid Ar and H2O) and of crystalline and glassy solids (MgO and a-SiO2). We find that simulation times of one to a few hundred picoseconds are sufficient in these systems to achieve an accuracy of the order of 10% on the estimated thermal conductivities.