The charge transport properties of hybrid halide perovskites are investigated with a combination of density functional theory including van der Waals interaction and the Boltzmann theory for diffusive transport in the relaxation time approximation. We find the mobility of electrons to be in the range 5–10 cm2V−1s−1 and that for holes within 1–5 cm2V−1s−1, where the variations depend on the crystal structure investigated and the level of doping. Such results, in good agreement with recent experiments, set the relaxation time to about 1 ps, which is the time-scale for the molecular rotation at room temperature. For the room temperature tetragonal phase we explore two possible orientations of the organic cations and find that the mobility has a significant asymmetry depending on the direction of the current with respect to the molecular axis. This is due mostly to the way the PbI3 octahedral symmetry is broken. Interestingly we find that substituting I with Cl has minor effects on the mobilities. Our analysis suggests that the carrier mobility is probably not a key factor in determining the high solar-harvesting efficiency of this class of materials.

We present a detailed description of the kinetic activation-relaxation technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe, and structural relaxation in a-Si (amorphous silicon).

Solid-electrolyte interphase (SEI) films with controllable properties are highly desirable for improving battery performance. In this paper, a combined experimental and theoretical approach is used to study SEI films formed on hard carbon in Li- and Na-ion batteries. It is shown that a stable SEI layer can be designed by precycling an electrode in a desired Li- or Na-based electrolyte, and that ionic transport can be kinetically controlled. Selective Li- and Na-based SEI membranes are produced using Li- or Na-based electrolytes, respectively. The Na-based SEI allows easy transport of Li ions, while the Li-based SEI shuts off Na-ion transport. Na-ion storage can be manipulated by tuning the SEI layer with film-forming electrolyte additives, or by preforming an SEI layer on the electrode surface. The Na specific capacity can be controlled to < 25 mAh g ; ≈ 1/10 of the normal capacity (250 mAh g ). Unusual selective/preferential transport of Li ions is demonstrated by preforming an SEI layer on the electrode surface and corroborated with a mixed electrolyte. This work may provide new guidance for preparing good ion-selective conductors using electrochemical approaches.

Many materials science phenomena, such as growth and self-organisation, are dominated by activated diffusion processes and occur on timescales that are well beyond the reach of standardmolecular dynamics simulations. Kinetic Monte Carlo (KMC) schemes make it possible to overcome this limitation and achieve experimental timescales. However, most KMC approaches proceed by discretizing the problem in space in order to identify, from the outset, a fixed set of barriers that are used throughout the simulations, limiting the range of problems that can be addressed. Here, we propose a more flexible approach -the kinetic activation-relaxation technique (k-ART) -which lifts these constraints. Our method is based on an off-lattice, self-learning, on-the-fly identification and evaluation of activation barriers using ART and a topological description of events. The validity and power of the method are demonstrated through the study of vacancy diffusion in crystalline silicon.Many problems in condensed matter and materials science involve stochastic processes associated with the diffusion of atoms over barriers that are high with respect to temperature and therefore inherently slow under "normal" conditions. Because the associated rates are small, these processes may be considered independent; neglecting the thermal motion of atoms, it is thus possible to deal with them using the kinetic Monte Carlo (KMC) algorithm, a stochastic approach proposed by Bortz et al. [1,2,3,4] and based on transition state theory, whereby the evolution of a system is determined by a set of pre-specified diffusion mechanisms, i.e., whose energy barriers are known beforehand. In KMC simulations, the timescale is determined by the fastest activated processes and, in practice, timescales of ms or longer can be reached -much longer than accessible in traditional molecular-dynamics (MD) simulations.While KMC has been extensively and successfully used over the past 20 years, it suffers from a number of drawbacks. In particular, the systems investigated must be discretized and mapped onto a fixed lattice in order to define the various diffusion mechanisms that need to be considered at a given moment [3]. Once all processes on the lattice have been identified (and their barriers evaluated) a priori, the simulations simply consist in operating a diffusion event picked at random, updating the list of possible moves in the new configuration, and iterating this procedure long enough to cover the relevant physical timescales. This approach works very well for simple problems (e.g., surface diffusion, metal-on-metal growth) but fails when the systems undergo significant lattice deformations or when long-range elastic effects are important. There have been numerous efforts to lift these limitations, most solutions falling into one of two categories: introduction of continuum approximations for the long-range strain deformations, and on-the-fly evaluation of the energy barriers. The first category retains the lattice formulation but adds long-range contributions -...