arXiv subjects

Emi Minamitani

Publications and source records attributed to Emi Minamitani.

At least 19 recordsLinked to original sources

arXiv2026

Dynamic heterogeneity in sodium silicate melts via machine-learning potential

We present a comprehensive characterisation of dynamic heterogeneity in sodium silicate melts using molecular dynamics simulation with machine-learning potentials. By studying sodium disilicate, tetrasilicate, and hexasilicate melts across a range of temperatures, mean squared displacement and a time-correlation function computed up to the nanosecond timescale provide a detailed account of how spatial mobility disparities emerge in a realistic multicomponent oxide glass. Within these timescales, the self-part of the van Hove function for sodium displays a bimodality, demonstrating that alkali transport is mediated by discrete displacement events consistent with a hopping mechanism. This distinct hopping allows sodium ions to decouple from the sluggish relaxation of the silicate matrix. Furthermore, evaluation of the non-Gaussian parameter reveals that, although all constituent species exhibit dynamic heterogeneity, the non-Gaussian behaviour is most pronounced for oxygen atoms. This trend reflects the intermittency of structural rearrangements, where framework atoms undergo rare and stochastic events compared to the frequent displacements of mobile ions. Our findings elucidate the microscopic mechanism of ion transport and its connection to dynamic heterogeneity in silicate melts, offering a new avenue to study fundamental glassy physics in realistic vitreous materials.

cond-mat.soft↗

arXiv2026

Boson peak in the dynamical structure factor of network- and packing-type glasses

Glasses are structurally disordered solids that host, in addition to crystalline-like phonons, vibrational excitations with no direct phononic counterpart. A long-standing universal signature is the excess vibrational density of states~(vDOS) over the Debye prediction, known as the boson peak~(BP), which has been extensively reported via inelastic neutron and X-ray scattering measurements of the dynamical structure factor $S(q,\omega)$. Here we quantify the vDOS directly from dynamical-structure-factor data and clarify the microscopic origin of the BP. We contrast two routes to extract the vDOS from $S(q,\omega)$: (i) using high-wavenumber $q$ data beyond the Debye wavenumber $q_D$ to access predominantly incoherent scattering and recover the vDOS in a manner analogous to velocity-autocorrelation-based approaches; and (ii) integrating $S(q,\omega)$ over the low-$q$ regime below $q_D$, which enables a decomposition of the vDOS into contributions from distinct wavenumber sectors and thereby provides direct access to the spatial character of vibrational modes. Focusing on the second route, we demonstrate that the BP in the vDOS emerges as the spectral consequence of a dispersionless excitation band in $S(q,\omega)$. Our main results are obtained from molecular-dynamics simulations, and we further show that the same mechanism is captured by an effective-medium theory for random spring networks, providing a unified interpretation that connects the excess vDOS to the wavenumber-resolved structure of vibrational excitations in glasses.

cond-mat.soft↗

arXiv2025

Topological descriptor for interpretable thermal transport prediction in amorphous graphene

Understanding and predicting thermal transport in disordered materials remains a significant challenge due to the absence of periodicity and the complex nature of medium-range structural motifs. In this work, we investigate amorphous graphene and demonstrate that persistent homology, a topological data analysis technique, can serve as a physically interpretable structural descriptor for predicting thermal conductivity. We first show that ridge regression using persistent homology descriptors achieves high prediction accuracy. To gain physical insight into the prediction process, we perform inverse analysis by mapping the regression coefficients back onto the persistence diagrams. This reveals that distorted hexagonal and triangular motifs are strongly correlated with reduced thermal conductivity. A further comparison with the spatial distribution of localized vibrational modes supports the physical interpretation that these motifs suppress thermal transport. Our findings highlight that persistent homology not only enables accurate prediction of physical properties but also uncovers meaningful structure-property relationships in two-dimensional amorphous materials. This approach offers a promising framework for interpretable machine-learning models in materials science.

cond-mat.mtrl-sci↗

arXiv2025

Benchmarking thermostat algorithms in molecular dynamics simulations of a binary Lennard-Jones glass-former model

A systematic comparison was carried out to assess the influence of representative thermostat methods in constant-temperature molecular dynamics simulations. The thermostat schemes considered include the Nos\'e--Hoover thermostat and its chain generalisation, the Bussi velocity rescaling method, and several implementations of the Langevin dynamics. Using a binary Lennard-Jones liquid as a model glass former, we investigated how the sampling of physical observables, such as particle velocities and potential energy, responds to changes in time step across these thermostats. While the Nos\'e--Hoover chain and Bussi thermostats provide reliable temperature control, a pronounced time-step dependence was observed in the potential energy. Amongst the Langevin methods, the Gr{\o}nbech-Jensen--Farago scheme provided the most consistent sampling of both temperature and potential energy. Nonetheless, Langevin dynamics typically incurs approximately twice the computational cost due to the overhead of random number generation, and exhibits a systematic decrease in diffusion coefficients with increasing friction. This study presents a broad comparison of thermostat methods using a binary Lennard-Jones glass-former model, offering practical guidance for the choice of thermostats in classical molecular dynamics simulations. These findings provide useful insights for diverse applications, including glass transition, phase separation, and nucleation.

physics.comp-ph↗

arXiv2025

Boson peak in covalent network glasses: Isostaticity and marginal stability

The boson peak (BP) stands as a key feature in understanding glasses and amorphous materials. It directly underlies their anomalous material properties, including thermal behaviors such as excess specific heat and low thermal conductivity, as well as mechanical characteristics such as nonaffine elasticity and fragile plasticity. Despite its importance, understanding of the BP remains limited in covalent network glasses. The most promising concepts are isostaticity and marginal stability, which have been established in theories of rigidity percolation and the jamming transition. While these concepts, supported by comprehensive data, account for the BP in packing-based glasses, comparable explanations have not yet been demonstrated for covalent network glasses. Here we study silica glass, a prototypical covalent network glass, using molecular dynamics simulations. We show that the BP in silica glass is governed by near-isostatic constraints and marginal stability, supporting their universality across diverse glassy systems. Furthermore, we reveal that these principles manifest as a wavenumber-independent band in the dynamical structure factor, and we demonstrate consistency with inelastic X-ray scattering data on silica glass. Our results provide a unified, experimentally testable framework for deciphering the BP and for refining the interpretation of scattering data in amorphous materials.

cond-mat.dis-nn↗

arXiv2025

Self-stabilized charge states in a double-decker molecular magnet on Pb(111)

Electron charging play key roles in physiochemical processes, whose intrinsic stabilization in single molecules is desirable for tailoring molecular functionality and developing molecular devices, but remains elusive on surfaces. Here, we show molecular charge states can be self-stabilized via intramolecular distortion in single bis(phthalocyaninato)terbium(III) (TbPc2) double-decker molecules, that were grown on Pb(111) substrate. Using scanning tunneling microscopy and spectroscopy, we identify fractions of TbPc2 molecules reduce to 2-fold symmetry, expressing energy-split molecular orbitals and two types of different spin states. Our first principles calculations unveil that the symmetry reduction is induced by charging-triggered Jahn-Teller distortions, which lifts the degenerate orbitals into two 2-fold symmetric orbitals. Single or double occupancy of the lower-energy orbital results in different molecular spin states. Such intramolecular distortion traps the excess electrons stably without explicit involvement of the substrate, in contrast to previously observed molecular charge states. These charged single molecule can be manipulated with the tip individually. This study offers a new avenue for tailoring the charge and spin states of molecules.

cond-mat.mes-hall↗

arXiv2025

Wannier interpolation of spin accumulation coefficient

The spin Hall (SH) effect is widely understood as a phenomoenon in which spin current flows perpendicular to an electric field. In the presence of a spin-orbit coupling, however, spin current is ambiguous, and the SH conductivity depends on the definition of spin current. In this article, we develop an \textit{ab initio} computational scheme for the spin accumulation coefficient, which characterizes the spin accumulation and would be an alternative indicator of the SH effect. The proposed method has been implemented into an open-source software Wannier90 and serves high-precision \textit{ab initio} research on the SH effect.

cond-mat.mes-hall↗

arXiv2024

Persistent homology elucidates hierarchical structures responsible for mechanical properties in covalent amorphous solids

Understanding how atomic-level structures govern the mechanical properties of amorphous materials remains a fundamental challenge in solid-state physics. Under mechanical loading, amorphous materials exhibit simple affine and spatially inhomogeneous nonaffine displacements that contribute to the elastic modulus through the Born (affine) and nonaffine terms, respectively. The differences between soft local structures characterized by small Born terms or large nonaffine displacements have yet to be elucidated. This challenge is particularly complex in covalent amorphous materials such as silicon, where the medium-range order (MRO) plays a crucial role in the network structure. To address these issues, we combined molecular dynamics simulations with persistent homology analysis. Our results reveal that local structures with small Born terms are governed by short-range characteristics, whereas those with large nonaffine displacements exhibit hierarchical structures in which short-range disorder is embedded within the MRO. These hierarchical structures are also strongly correlated with low-energy localized vibrational excitations. Our findings demonstrate that the mechanical responses and dynamic properties of covalent amorphous materials are intrinsically linked to the MRO, providing a new framework for understanding and tailoring their properties.

cond-mat.mtrl-sci↗

arXiv2023

Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li$_3$PO$_4$

Understanding ionic behaviour under external electric fields is crucial to develop electronic and energy-related devices using ion transport. In this study, we propose a neural network (NN) model to predict the Born effective charges of ions along an axis parallel to an applied electric field from atomic structures. The proposed NN model is applied to Li$_3$PO$_4$ as a prototype. The prediction error of the constructed NN model is 0.0376 $e$/atom. In combination with an NN interatomic potential, molecular dynamics (MD) simulations are performed under a uniform electric field of 0.1 V/angstrom, whereby an enhanced mean square displacement of Li along the electric field is obtained, which seems physically reasonable. In addition, the external forces along the direction perpendicular to the electric field, originating from the off-diagonal terms of the Born effective charges, are found to have a nonnegligible effect on Li migration. Finally, additional MD simulations are performed to examine the Li motion in an amorphous structure. The results reveal that Li migration occurs in various areas despite the absence of explicitly introduced defects, which may be attributed to the susceptibility of the Li ions in the local minima to the electric field. We expect that the proposed NN method can be applied to any ionic material, thereby leading to atomic-scale elucidation of ion behaviour under electric fields.

cond-mat.mtrl-sci↗

arXiv2023

Persistent homology-based descriptor for machine-learning potential of amorphous structures

High-accuracy prediction of the physical properties of amorphous materials is challenging in condensed-matter physics. A promising method to achieve this is machine-learning potentials, which is an alternative to computationally demanding ab initio calculations. When applying machine-learning potentials, the construction of descriptors to represent atomic configurations is crucial. These descriptors should be invariant to symmetry operations. Handcrafted representations using a smooth overlap of atomic positions and graph neural networks (GNN) are examples of methods used for constructing symmetry-invariant descriptors. In this study, we propose a novel descriptor based on a persistence diagram (PD), a two-dimensional representation of persistent homology (PH). First, we demonstrated that the normalized two-dimensional histogram obtained from PD could predict the average energy per atom of amorphous carbon (aC) at various densities, even when using a simple model. Second, an analysis of the dimensional reduction results of the descriptor spaces revealed that PH can be used to construct descriptors with characteristics similar to those of a latent space in a GNN. These results indicate that PH is a promising method for constructing descriptors suitable for machine-learning potentials without hyperparameter tuning and deep-learning techniques.

cs.LG↗

arXiv2022

Non-charge-transfer origin of Tc Enhancement in a Surface Superconductor Si(111)-(root7xroot3)-In with Adsorbed Organic Molecules

The effects of adsorption of Zn-phthalocyanine (ZnPc) molecules on the superconductivity of the Si(111)-(root7xroot3)-In surface are studied through transport measurements under ultrahigh vacuum environment. The ZnPc molecules are found to increase the transition temperature Tc by 11% at maximum, which is about 2.7 times the Tc increase previously reported using CuPc. By contrast, angle-resolved photoemission spectroscopy measurements and ab initio calculations show that charge transfer from the In atomic layers to ZnPc is substantially smaller than that to CuPc. This clearly shows that charge transfer should be excluded as the origin of the increase in Tc. The push-back effect induced by physical adsorption of molecules is discussed as a possible mechanism for the Tc enhancement.

cond-mat.supr-con↗

arXiv2022

Using neural network potential to study point defect properties in multiple charge states of GaN with nitrogen vacancy

Investigation of charged defects is necessary to understand the properties of semiconductors. While density functional theory calculations can accurately describe the relevant physical quantities, these calculations increase the computational loads substantially, which often limits the application of this method to large-scale systems. In this study, we propose a new scheme of neural network potential (NNP) to analyze the point defect behavior in multiple charge states. The proposed scheme necessitates only minimal modifications to the conventional scheme. We demonstrated the prediction performance of the proposed NNP using wurzite-GaN with a nitrogen vacancy with charge states of 0, 1+, 2+, and 3+. The proposed scheme accurately trained the total energies and atomic forces for all the charge states. Furthermore, it fairly reproduced the phonon band structures and thermodynamics properties of the defective structures. Based on the results of this study, we expect that the proposed scheme can enable us to study more complicated defective systems and lead to breakthroughs in novel semiconductor applications.

cond-mat.mtrl-sci↗

arXiv2022

Topological descriptor of thermal conductivity in amorphous materials

Quantifying the correlation between the complex structures of amorphous materials and their physical properties has been a long-standing problem in materials science. In amorphous Si, a representative covalent amorphous solid, the presence of a medium-range order (MRO) has been intensively discussed. However, the specific atomic arrangement corresponding to the MRO and its relationship with physical properties, such as thermal conductivity, remain elusive. Here, we solve this problem by combining topological data analysis, machine learning, and molecular dynamics simulations. By using persistent homology, we constructed a topological descriptor that can predict the thermal conductivity. Moreover, from the inverse analysis of the descriptor, we determined the typical ring features that correlated with both the thermal conductivity and MRO. The results provide an avenue for controlling the material characteristics through the topology of nanostructures.

cond-mat.mtrl-sci↗

arXiv2021

Ab initio analysis for initial process of Joule heating in semiconductors

To investigate the initial process of Joule heating in semiconductors microscopically and quantitatively, we developed a theoretical framework for the ab initio evaluation of the carrier energy relaxation in semiconductors under a high electric field using a combination of the two-temperature model and the Boltzmann equation. We employed the method for bulk silicon as a typical example. Consequently, we found a remarkable difference in the energy relaxation processes of the electron and hole carriers. The longitudinal acoustic and optical phonons at the zone boundary contribute to the energy relaxation of electron carriers, whereas they contribute negligibly to that of the hole carriers. In addition, at the band edge, the energy relaxation rate is maximized for the electron carriers, whereas it is suppressed for the hole carriers. These differences stem from the presence/absence of intervalley scattering processes and isotropic/anisotropic band structures in electrons and holes. Our results lay the foundation for controlling the thermal generation in semiconductors by material design.

cond-mat.mtrl-sci↗

arXiv2020

Geometric spin-orbit coupling and chirality-induced spin selectivity

We report a new type of spin-orbit coupling (SOC) called geometric SOC. Starting from the relativistic theory in curved space, we derive an effective nonrelativistic Hamiltonian in a generic curve embedded into flat three dimensions. The geometric SOC is $O(m^{-1})$, in which $m$ is the electron mass, and hence much larger than the conventional SOC of $O(m^{-2})$. The energy scale is estimated to be a hundred meV for a nanoscale helix. We calculate the current-induced spin polarization in a coupled-helix model as a representative of the chirality-induced spin selectivity. We find that it depends on the chirality of the helix and is of the order of $0.01 \hbar$ per ${\rm nm}$ when a charge current of $1~{\rm μA}$ is applied.

cond-mat.mes-hall↗

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↗

arXiv2020

Theoretical prediction of superconductivity in monolayer h-BN doped with alkaline-earth metals (Ca, Sr, Ba)

We investigated the possibility of superconductivity in monolayer hexagonal boron nitride (h-BN) doped using each group-1 (Li, Na, K) and group-2 (Be, Mg, Ca, Sr, Ba) atom via ab-initio calculations. Consequently, we reveal that Sr- and Ba-doped monolayer h-BN and Ca-doped monolayer h-BN with 3.5% tensile strain are energetically stable and become superconductors with Tc values of 5.83 K, 1.53 K, and 12.8 K, respectively, which are considerably higher than those of Ca-, Sr-, and Ba-doped graphene. In addition, the momentum-resolved electron-phonon coupling (EPC) constant shows that the scattering among intrinsic π electrons around the Γ point dominates Tc. The scattering process is mediated by the low-energy vibration of the adsorbate. Moreover, compared with graphene, the stronger adsorbate-substrate interaction and lower symmetry in h-BN are critical for enhancing EPC in doped h-BN.

cond-mat.mtrl-sci↗

arXiv2019

Simulating lattice thermal conductivity in semiconducting materials using high-dimensional neural network potential

We demonstrate that a high-dimensional neural network potential (HDNNP) can predict the lattice thermal conductivity of semiconducting materials with an accuracy comparable to that of density functional theory (DFT) calculation. After a training procedure based on the force, the root mean square error between the forces predicted by the HDNNP and DFT is less than 40 meV/Å. As typical examples, we present the results for Si and GaN bulk crystals. The deviation from the thermal conductivity calculated using DFT is within 1% at 200 to 500 K for Si and within 5.4% at 200 to 1000 K for GaN.

cond-mat.mtrl-sci↗