Pushing Forward Simulation Techniques of Ion Transport in Ion Conductors for Energy Materials

Abstract

Simulation techniques are crucial to establish a firm link between phenomena occurring at the atomic scale and macroscopic observations of functional materials. Importantly, extensive sampling of space and time scales is paramount to ensure good convergence of physically relevant quantities to describe ion transport in energy materials. Here, a number of simulation methods to address ion transport in energy materials are discussed, with the pros and cons of each methodology put forward. Emphasis is given to the stochastic nature of results produced by kinetic Monte Carlo, which can adequately account for compositional disorder across multiple sublattices in solids.

Going against the Grain: Atomistic Modeling of Grain Boundaries in Solid Electrolytes for Solid-State Batteries

“…AIMD simulations, where the classical Newtonian equations of motion for a system are solved numerically and the interatomic interactions are defined by first-principles calculations, have proven to be powerful for investigating long-range Li- and Na-ion transport at the GBs of solid electrolytes. Although computationally expensive and typically limited to hundreds of ions and time scales of hundreds of picoseconds, AIMD simulations can provide highly accurate potential energy surfaces of materials and be used to quantify diffusion coefficients directly as a function of temperature, which can then be plotted using the Arrhenius equation to obtain activation energies for ionic conductivity . AIMD calculations can be used to identify and understand the impact of GBs on ion transport compared to bulk materials.…”

Predicting the Na+ ion transport properties of NaSICON materials using density functional theory and Kinetic Monte Carlo

“…The KMC method was used to simulate the macroscopic sodium ion long-range transport in the NaSICONs by evaluating large stochastic ensembles of sodium ion jumps. 55,57,108,110 According to transition state theory, the ionic jump is dened by the energy wells of its stable initial and nal state, which are separated by an energy barrier E mig . E mig is the difference between the saddle point and the ground state of the energy surface along the migration path and corresponds to the unstable intermediate transition state.…”

“…18 Computational methodologies can be utilized to elucidate the microscopic properties, such as the structure and the migration energy prole of ionic pathways, and their inuences on the macroscopic transport behaviour. [54][55][56][57][58][59] To date, however, the focus of computational studies has been mainly on the parent composition Na 1+x Zr 2 Si x P 3−x O 12 , 1-3 which is not sufficient for an adequate understanding of NaSICONs. 53,[60][61][62][63][64][65][66][67][68][69] Therefore, there is a need to investigate various substituted structures over entire compositional ranges using reliable, loweffort computational techniques.…”

Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields

“…AIMD is a powerful tool for studying the dynamics of superionic phases, in part because it does not require parametrization from experimental data. AIMD simulations, however, are computationally costly and are typically limited to simulations of hundreds of atoms over time scales of picoseconds. , While this can be sufficient to characterize the diffusive regime in a superionic phase, this is often inadequate for studying diffusion at lower temperatures, where ion transport is much slower. Empirical potentials can be used to study long-time scale diffusion behavior in poorly conducting low-temperature systems .…”