Research Update: Ca doping effect on the Li-ion conductivity in NASICON-type solid electrolyte LiZr2(PO4)3: A first-principles molecular dynamics study
Abstract:In this work, we used a density functional theory-based molecular dynamics simulation to investigate the Ca content-dependent Li-ion conductivity of NASICON-type Li1+2xCaxZr2-x(PO4)3 (LCZP) solid electrolytes (0.063 ≤ x ≤ 0.375) which exhibit a Li-excess chemical composition. The LCZP systems show a higher room temperature Li-ion conductivity and a lower activation energy than pristine LiZr2(PO4)3 (LZP), and the tendencies of those properties agree with the experimental results. In addition, the Li-ion conduct… Show more
“…Therefore, low temperature conductivities are extrapolated from high temperature data as reported in our previous FPMD calculations. 10,11,37 The Arrhenius plots for the NASICON-type and the b-iron sulfate-type structures is shown in Fig. 4, and the migration energies of Mg were evaluated by a simple linear tting scheme.…”
Section: Resultsmentioning
confidence: 99%
“…Above FPMD studies have been performed in our previous studies, showing reasonable accordance between the experimental and the computational results. 10,11,37 We evaluated the thermodynamic stability of the MZP polymorphs by rst principles DFT calculations. Temperature-and/ or pressure-dependent phase stability was also evaluated using DFT-based phonon calculations or cell-volume constraint calculations, respectively.…”
“…Therefore, low temperature conductivities are extrapolated from high temperature data as reported in our previous FPMD calculations. 10,11,37 The Arrhenius plots for the NASICON-type and the b-iron sulfate-type structures is shown in Fig. 4, and the migration energies of Mg were evaluated by a simple linear tting scheme.…”
Section: Resultsmentioning
confidence: 99%
“…Above FPMD studies have been performed in our previous studies, showing reasonable accordance between the experimental and the computational results. 10,11,37 We evaluated the thermodynamic stability of the MZP polymorphs by rst principles DFT calculations. Temperature-and/ or pressure-dependent phase stability was also evaluated using DFT-based phonon calculations or cell-volume constraint calculations, respectively.…”
“…92 Ionic conductivity may be increased by introducing low-valence ions, such as Ca 2+ and Y 3+ , at the Zr ion sites of LZP and inducing a state of excess Li. 33,41,42,49,72 However, only single-element doping has been used in most studies to date, with few examples wherein two elements have been doped simultaneously (i.e. co-doping).…”
Section: Composition Optimisation Using the Bayesian Methodsmentioning
All-solid-state Li-ion batteries are of considerable interest as safer alternatives to Li-ion batteries containing flammable organic electrolytes. To date, however, achieving sufficient charging and discharging rates, in addition to capacity,...
“…We also evaluate the Li migration energies for all 400 compounds (Dataset 1) by using fast force‐field (FF) calculations. As mentioned before, first‐principles DFT approaches can precisely evaluate the Li migration energies for various inorganic solid‐state compounds by nudged elastic band (NEB) and/or first‐principles molecular dynamics (FPMD) techniques . However, these techniques have large computational demands, and the exhaustive evaluation of materials properties is thus technically difficult.…”
Interest in all‐solid‐state Li‐ion batteries (LIBs) using non‐flammable Li‐conducting ceramics as solid electrolytes has increased, as safe and robust batteries are urgently desired as power sources for (hybrid) electric vehicles. However, the low Li‐ion conductivities of ceramics have hindered all‐solid‐state LIB commercialization; many researchers have attempted to develop fast Li‐ion conductors. We introduce two efficient high‐throughput computational approaches for materials exploration: (i) exhaustive search and (ii) informatics‐aided prediction. For demonstration, ∼400 Li‐ and Zn‐containing oxide (Li−Zn−X−O) compounds of varied crystal structures are extracted from Materials Project datasets. We calculate the migration energies for Li‐ion conduction and the phase stabilities (decomposition energies) of these materials by simulation and apply Bayesian optimization to determine the material with the highest ionic conductivity. The results show much greater efficiency than a random search algorithm.
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