arXiv subjects

Yasunobu Ando

Publications and source records attributed to Yasunobu Ando.

7 recordsLinked to original sources

arXiv2026

Probing Anharmonic and Heterogeneous Carrier Dynamics Across Sublattice Melting in a Minimal Model Superionic Conductor

Despite decades of research, the microscopic origin of sublattice melting and fast ion transport in superionic conductors remains elusive. Here, we introduce a chemically neutral minimal binary model consisting of a rigid host lattice stabilized by short-range steric repulsion and a soft carrier sublattice interacting via long-range Wigner-type forces. This contrast naturally produces distinct melting temperatures and an intermediate sublattice-melting phase in which carriers become fluidlike while the host remains crystalline. Molecular dynamics simulations identify three dynamical regimes-crystalline, sublattice-melt, and fully molten-marked by sharp changes in diffusivity, structural correlations, and dynamical heterogeneity. Near sublattice melting, carrier motion is strongly anharmonic and spatially heterogeneous, beyond mean-field hopping descriptions. By tuning the density, we demonstrate that sublattice melting can be continuously controlled, establishing a direct link between lattice softness, anharmonicity, and collective ion transport. Comparison with conventional long-range Coulombic models confirms that our minimal model reproduces the key dynamical signatures of superionicity, providing a unified microscopic foundation for designing mechanically robust superionic conductors.

cond-mat.soft↗

arXiv2026

Asymmetric simple exclusion process with tree-like network branches

The asymmetric simple exclusion process (ASEP) is a fundamental stochastic model describing asymmetric many-particle diffusion with hard-core interactions on a one-dimensional lattice, and has been widely applied in the study of nonequilibrium transport phenomena. Motivated by the modeling of proton transport along oxygen networks in proton-conducting solid oxides, we extend the ASEP to a model defined on a one-dimensional backbone lattice with tree-like network branches. We derive the exact stationary distribution of this network ASEP and investigate its transport properties. By considering two representative network geometries for which physical quantities can be expressed in terms of certain hypergeometric series, we elucidate how the network geometry influences transport properties.

cond-mat.stat-mech↗

arXiv2024

Surface structure of the 3x3-Si phase on Al(111), studied by the multiple usages of positron diffraction and core-level photoemission spectroscopy

The structure of an Al(111)3x3-Si surface was examined by combining data from positron diffraction and core-level photoemission spectroscopy. Analysis of the diffraction rocking curves indicated that the overlayer had a flat honeycomb lattice structure. Simulations of Si core-level spectra calculated via the first-principles indicated that one of the Si atoms in the unit cell was replaced by an Al atom. The surface superstructure was thus a two-dimensional layer of Al-embedded silicene on Al(111).

cond-mat.mtrl-sci↗

arXiv2022

Electronic topological transition of 2D boron by the ion exchange reaction

We systematically investigated electronic evolutions of non-symmorphic borophene with chemical environments that were realized by the ion exchange method. Electronic structures can be characterized by the topological $Z_2$ invariant. Spectroscopic experiments and DFT calculations unveiled that a sheet of hydrogenated borophene (borophane) is the Dirac nodal loop semimetal ($Z_2=-1$), while a layered crystal of YCrB$_4$ is an insulator ($Z_2=1$). The results demonstrate the electronic topological transition by replacement of the counter atoms on the non-symmorphic borophene layer.

cond-mat.mtrl-sci↗

arXiv2020

Phase stability of Au-Li binary systems studied using neural network potential

The miscibility of Au and Li exhibits a potential application as an adhesion layer and electrode material in secondary batteries. Here, to explore alloying properties, we constructed a neural network potential (NNP) of Au-Li binary systems based on density functional theory (DFT) calculations. To accelerate construction of NNPs, we proposed an efficient and inexpensive method of structural dataset generation. The predictions by the constructed NNP on lattice parameters and phonon properties agree well with those obtained by DFT calculations. We also investigated the mixing energy of Au$_{1-x}$Li$_{x}$ with fine composition grids, showing excellent agreement with DFT verifications. We found the existence of various compositions with structures on and slightly above the convex hull, which can explain the lack of consensus on the Au-Li stable phases in previous studies. Moreover, we newly found Au$_{0.469}$Li$_{0.531}$ as a stable phase, which has never been reported elsewhere. Finally, we examined the alloying process starting from the phase separated structure to the complete mixing phase. We found that when multiple adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface started from the dissolved region. This paper demonstrates the applicability of NNPs toward miscible phases and provides the understanding of the alloying mechanism.

cond-mat.mtrl-sci↗

arXiv2018

Construction of accurate machine learning force fields for copper and silicon dioxide

Recently, the machine learning force field has emerged as a powerful atomic simulation approach for its high accuracy and low computational cost. However, its applications in the multi-component materials are relatively less. In this study, the ML force fields are constructed for both elemental material (Cu) and binary material (SiO2). The atomic environments are described by the structural fingerprint that takes the bond angle into account, and then, different ML techniques, including linear regression, neural network and mixture model method, are used to learn the structure-force relationship. We found that the use of angular structural fingerprint and mixture model method significantly improves the accuracy of ML force fields. In addition, we discussed the effective structural fingerprints auto-selection method based on LASSO and the genetic algorithm. The atomic simulations carried out with ML force fields are in excellent agreement with ab initio calculations.

cond-mat.mtrl-sci↗

arXiv2017

Scanning tunneling spectroscopy of superconductivity on surfaces of LiTi2O4(111) thin films

Unique superconductivity at surfaces/interfaces, as exemplified by LaAlO3/SrTiO3 interfaces, and the high transition temperature in ultrathin FeSe films, have triggered intense debates on how superconductivity is affected in atomic and electronic reconstructions. The surface of superconducting cubic spinel oxide LiTi2O4 is another interesting system because its inherent surface electronic and atomic reconstructions add complexity to superconducting properties. Investigations of such surfaces are hampered by the lack of single crystals or high-quality thin films. Here, using low-temperature scanning tunneling microscopy, we report an unexpected small superconducting energy gap and a long coherence length on the surface of LiTi2O4 (111) epitaxial thin films. Furthermore, we find that a pseudogap opening at the Fermi energy modifies the surface superconductivity. Our results open an avenue, exploring anomalous superconductivity on the surface of cubic transition-metal oxides where the electronic states are spontaneously modulated with involving rich many-body interactions.

cond-mat.supr-con↗