Theoretical upper limit of dislocation density in slightly-ductile single-crystal ceramics

Soft Matter Electrolytes: Mechanism of Ionic Conduction Compared to Liquid or Solid Electrolytes

Soft matter electrolytes could solve the safety problem of widely used liquid electrolytes in Li-ion batteries which are burnable upon heating. Simultaneously, they could solve the problem of poor contact between electrodes and solid electrolytes. However, the ionic conductivity of soft matter electrolytes is relatively low when mechanical properties are relatively good. In the present review, mechanisms of ionic conduction in soft matter electrolytes are discussed in order to achieve higher ionic conductivity with sufficient mechanical properties where soft matter electrolytes are defined as polymer electrolytes and polymeric or inorganic gel electrolytes. They could also be defined by Young's modulus from about 105 Pa to 109 Pa. Many soft matter electrolytes exhibit VFT (Vogel-Fulcher-Tammann) type temperature dependence of ionic conductivity. VFT behavior is explained by the free volume model or the configurational entropy model, which is discussed in detail. Mostly, the amorphous phase of polymer is a better ionic conductor compared to the crystalline phase. There are, however, some experimental and theoretical reports that the crystalline phase is a better ionic conductor. Some methods to increase the ionic conductivity of polymer electrolytes are discussed, such as cavitation under tensile deformation and the microporous structure of polymer electrolytes, which could be explained by the conduction mechanism of soft matter electrolytes.

Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

In the present review, the merits and demerits of machine learning (ML) in materials science are discussed, compared with first principles calculations (PDE (partial differential equations) model) and physical or phenomenological ODE (ordinary differential equations) model calculations. ML is basically a fitting procedure of pre-existing (experimental) data as a function of various factors called descriptors. If excellent descriptors can be selected and the training data contain negligible error, the predictive power of a ML model is relatively high. However, it is currently very difficult for a ML model to predict experimental results beyond the parameter space of the training experimental data. For example, it is pointed out that all-dislocation-ceramics, which could be a new type of solid electrolyte filled with appropriate dislocations for high ionic conductivity without dendrite formation, could not be predicted by ML. The merits and demerits of first principles calculations and physical or phenomenological ODE model calculations are also discussed with some examples of the flexoelectric effect, dielectric constant, and ionic conductivity in solid electrolytes.

Possibility of High Ionic Conductivity and High Fracture Toughness in All-Dislocation-Ceramics

Based on the results of numerical calculations as well as those of some related experiments which are reviewed in the present paper, it is suggested that solid electrolytes filled with appropriate dislocations, which is called all-dislocation-ceramics, are expected to have considerably higher ionic conductivity and higher fracture toughness than those of normal solid electrolytes. Higher ionic conductivity is due to the huge ionic conductivity along dislocations where the formation energy of vacancies is considerably lower than that in the bulk solid. Furthermore, in all-dislocation- ceramics, dendrite formation could be avoided. Higher fracture toughness is due to enhanced emissions of dislocations from a crack tip by pre-existing dislocations, which causes shielding of a crack tip, energy dissipation due to plastic deformation and heating, and crack-tip blunting. All-dislocation-ceramics may be useful for all-solid-state batteries.