Finite-size effects on the molecular dynamics simulation of fast-ion conductors: A case study of lithium garnet oxide Li7La3Zr2O12

Klenk, Matthew J.; Lai, Wei

doi:10.1016/j.ssi.2016.03.002

Cited by 39 publications

(56 citation statements)

References 69 publications

(84 reference statements)

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“…In LLZWO, we observe a similar behavior to that of the thio-lisicon electrolyte Li 10 GeP 2 S 12 [45], displaying comparable activation energies. In the case of undoped systems, we obtain H ∼ 0.3, a value compatible with other recent simulation results [43,50]. The same value is obtained for the Haven ratio of the implicitly and explicitly doped system.…”

Section: Diffusion Coefficientssupporting

confidence: 91%

Doping in garnet-type electrolytes: Kinetic and thermodynamic effects from molecular dynamics simulations

Mottet

Marcolongo

Laino

et al. 2019

Phys. Rev. Materials

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To shed light on the impact of doping on the conductivity of garnet-type electrolytes, we use molecular dynamics with an ab-initio designed force-field to investigate the complex interplay between the carrier concentration and the kinetic and thermodynamic changes induced by the addition of hypervalent dopants. We particularly focus on the effect of the distribution of the doping agents and find that there is a need to perform a proper average over the frozen disorder introduced by the doping. We observe the competing effect between the decrease in concentration

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Section: Diffusion Coefficientssupporting

confidence: 91%

Doping in garnet-type electrolytes: Kinetic and thermodynamic effects from molecular dynamics simulations

Mottet

Marcolongo

Laino

et al. 2019

Phys. Rev. Materials

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show abstract

“…The abrupt change in the total number of Li clusters and diffusivity in t-LLZO suggests that certain transition occurs at a temperature between 900 K and 1000 K43. In c-LLZO we did not find the splitting of 48 g sites into 96 h sites in c-LLZO since the two sites are closely connected.…”

Section: Resultsmentioning

confidence: 60%

“…The potential parameters, presented in Table S1, were fitted from DFT calculations by Klenk and Lai43, where the lattice parameters, phase transition temperatures, and Li diffusivities are benchmarked against previous experiments57. In order to simulate both the cubic and tetragonal phase of LLZO, and examine the difference in Li diffusion behaviors, we artificially imposed isotropic pressure and biaxial pressure respectively to constraint the lattice shape.…”

Section: Methodsmentioning

confidence: 99%

Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12

Chen

Ciucci

2017

Sci Rep

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Understanding Li diffusion in solid conductors is essential for the next generation Li batteries. Here we show that density-based clustering of the trajectories computed using molecular dynamics simulations helps elucidate the Li diffusion mechanism within the Li7La3Zr2O12 (LLZO) crystal lattice. This unsupervised learning method recognizes lattice sites, is able to give the site type, and can identify Li hopping events. Results show that, while the cubic LLZO has a much higher hopping rate compared to its tetragonal counterpart, most of the Li hops in the cubic LLZO do not contribute to the diffusivity due to the dominance of back-and-forth type jumps. The hopping analysis and local Li configuration statistics give evidence that Li diffusivity in cubic LLZO is limited by the low vacancy concentration. The hopping statistics also shows uncorrelated Poisson-like diffusion for Li in the cubic LLZO, and correlated diffusion for Li in the tetragonal LLZO in the temporal scale. Further analysis of the spatio-temporal correlation using site-to-site mutual information confirms the weak site dependence of Li diffusion in the cubic LLZO as the origin for the uncorrelated diffusion. This work puts forward a perspective on combining machine learning and information theory to interpret results of molecular dynamics simulations.

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“…Further evidence for correlated transport in lithium garnets comes from computational studies. A variety of correlated diffusion processes have been observed in molecular dynamics simulations [41–44], and calculated diffusion coefficients and ionic conductivities show non-Nernst–Einstein behaviour ( H R <1) [45]. These results collectively indicate the existence of significant interactions, either between lithium ions or between these ions and the host lattice.…”

Section: Introductionmentioning

confidence: 96%

Lattice-geometry effects in garnet solid electrolytes: a lattice-gas Monte Carlo simulation study

Morgan

2017

R. Soc. open sci.

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Ionic transport in solid electrolytes can often be approximated as ions performing a sequence of hops between distinct lattice sites. If these hops are uncorrelated, quantitative relationships can be derived that connect microscopic hopping rates to macroscopic transport coefficients; i.e. tracer diffusion coefficients and ionic conductivities. In real materials, hops are uncorrelated only in the dilute limit. At non-dilute concentrations, the relationships between hopping frequency, diffusion coefficient and ionic conductivity deviate from the random walk case, with this deviation quantified by single-particle and collective correlation factors, f and fI, respectively. These factors vary between materials, and depend on the concentration of mobile particles, the nature of the interactions, and the host lattice geometry. Here, we study these correlation effects for the garnet lattice using lattice-gas Monte Carlo simulations. We find that, for non-interacting particles (volume exclusion only), single-particle correlation effects are more significant than for any previously studied three-dimensional lattice. This is attributed to the presence of two-coordinate lattice sites, which causes correlation effects intermediate between typical three-dimensional and one-dimensional lattices. Including nearest-neighbour repulsion and on-site energies produces more complex single-particle correlations and introduces collective correlations. We predict particularly strong correlation effects at xLi=3 (from site energies) and xLi=6 (from nearest-neighbour repulsion), where xLi=9 corresponds to a fully occupied lithium sublattice. Both effects are consequences of ordering of the mobile particles. Using these simulation data, we consider tuning the mobile-ion stoichiometry to maximize the ionic conductivity, and show that the ‘optimal’ composition is highly sensitive to the precise nature and strength of the microscopic interactions. Finally, we discuss the practical implications of these results in the context of lithium garnets and other solid electrolytes.

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Finite-size effects on the molecular dynamics simulation of fast-ion conductors: A case study of lithium garnet oxide Li7La3Zr2O12

Cited by 39 publications

References 69 publications

Doping in garnet-type electrolytes: Kinetic and thermodynamic effects from molecular dynamics simulations

Doping in garnet-type electrolytes: Kinetic and thermodynamic effects from molecular dynamics simulations

Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12

Lattice-geometry effects in garnet solid electrolytes: a lattice-gas Monte Carlo simulation study

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