Point-defect chemistry for ionic conduction in solid electrolytes with isovalent cation mixing

Ogawa, Takafumi; Sato, Kazuyuki; Mori, Kazuhiro; Kobayashi, Shunsuke; Moriwake, Hiroki; Ikuhara, Yuichi; Kuwabara, Akihide

doi:10.1039/d4ta05684a

J. Mater. Chem. A

2024

DOI: 10.1039/d4ta05684a

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Point-defect chemistry for ionic conduction in solid electrolytes with isovalent cation mixing

Takafumi Ogawa,

Kazuyuki Sato,

Kazuhiro Mori

et al.

Abstract: Recent studies on solid electrolytes for electrochemical devices such as batteries and fuel cells have focused on investigating migration of mobile ions in ion-conducting crystalline solids, whereas the temperature dependence...

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Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)_0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)_0.05F_2.95

Hinuma

2024

J. Phys. Chem. B

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Tysonite structure fluorides doped with divalent cations, represented by Ce 0.95 Ca 0.05 F 2.95 , are a class of good F − ion conductors together with fluoritestructured compounds. Computational understanding of the F − conduction process is difficult because of the complicated interactions between three symmetrically distinct F sites and the experimentally observed change in the F diffusion mechanism slightly above room temperature, effectively making first-principles molecular dynamics (FP-MD) simulations, which are often conducted well above the transition temperature, useless when analyzing behavior below the transition point. Neural network potential (NNP) MD simulations showed that the F diffusion coefficient is higher when the divalent dopant cation size is similar to the trivalent cation size. The diffusion behavior of F in different sites changes at roughly 500 K in Ce 0.95 Ca 0.05 F 2.95 because only the F1 site sublattice contributes to F diffusion below this temperature, but the remaining F2 and F3 sublattices become gradually active above this temperature. The paradox of higher diffusion coefficients in CeF 3 -based compounds than similar LaF 3 -based compounds even though the lattice parameters are larger in the latter may be caused by a shallower potential of Ce and F in CeF 3 compared to the LaF 3 counterparts.

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Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)_0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)_0.05F_2.95

Hinuma

2024

J. Phys. Chem. B

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Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understanding

Fujii,

Hyodo,

Shitara

et al. 2024

Science and Technology of Advanced Materials

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This review presents computational and machine learning methodologies developed during a 5-year research project on proton-conducting oxides. The main goal was to develop methodologies that could assist in materials discovery or provide new insights into complex proton-conducting oxides. Through these methodologies, three new proton-conducting oxides, including both perovskite and non-perovskites, have been discovered. In terms of gaining insights, octahedral tilt/distortions and oxygen affinity are found to play a critical role in determining proton diffusivities and conductivities in doped barium zirconates. Replica exchange Monte Carlo approach has enabled to reveal realistic defect configurations, hydration behavior, and their temperature dependence in oxides. Our approach ‘Materials discovery through interpretation’, which integrates new insights or tendencies obtained from computations and experiments to sequential explorations of materials, has also identified perovskites that exhibit proton conductivity exceeding 0.01 S/cm and high chemical stability at 300 C.

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Point-defect chemistry for ionic conduction in solid electrolytes with isovalent cation mixing

Abstract: Recent studies on solid electrolytes for electrochemical devices such as batteries and fuel cells have focused on investigating migration of mobile ions in ion-conducting crystalline solids, whereas the temperature dependence...

Cited by 2 publications

References 88 publications

Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)_0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)_0.05F_2.95

Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)_0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)_0.05F_2.95

Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understanding

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Point-defect chemistry for ionic conduction in solid electrolytes with isovalent cation mixing

Abstract: Recent studies on solid electrolytes for electrochemical devices such as batteries and fuel cells have focused on investigating migration of mobile ions in ion-conducting crystalline solids, whereas the temperature dependence...

Cited by 2 publications

References 88 publications

Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)0.05F2.95

Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)0.05F2.95

Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understanding

Contact Info

Product

Resources

About

Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)_0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)_0.05F_2.95

Neural Network Potential Molecular Dynamics Simulations of (La,Ce,Pr,Nd)_0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)_0.05F_2.95