arXiv subjects

Kazuyoshi Yoshimi

Publications and source records attributed to Kazuyoshi Yoshimi.

At least 19 recordsLinked to original sources

arXiv2026

ODAT-SE: A modular and parallel framework for simulation-based inverse problems

Open Data Analysis Tool for Science and Engineering (ODAT-SE) is an open-source Python framework for inverse problems that are solved by repeated forward simulations. It succeeds 2DMAT, which was developed for surface-structure analysis with quantum-beam diffraction and has since been applied to other fields. In version 3, the search algorithms were separated from the application-specific solvers, which became independent packages, and the software was renamed; this paper describes the current version 4.0. The framework connects optimization and sampling algorithms to solvers through a common interface for parameter mapping, constraints, and objective evaluation, so that an existing simulation code can be used with any of the algorithms. Relative to the previously published 2DMAT, it provides two-level MPI parallelism, which distributes candidate evaluations over processes and each evaluation over a parallel solver, checkpoint and continuation facilities, random search, global optimizers from SciPy, tensor-train optimization, and a Bayesian workflow that estimates partition-function ratios, model evidence, effective noise levels, and marginal posterior distributions. We validate the Bayesian workflow against closed-form results for a linear regression, benchmark tensor-train optimization on continuous, binary, and mixed-integer problems, and verify the two-level parallelization by the same optimum for all process decompositions. Taking up the positron-diffraction example of the 2DMAT paper, the workflow identifies the correlated structural solutions and an effective noise scale close to the imposed perturbation, while exposing incomplete convergence of symmetry-related mode weights. Solver packages, templates, and a gallery of examples support the use of the framework as a general platform for inverse-problem analysis.

math.NA↗

arXiv2026

Revisiting spin Hamiltonian parameters in a Kitaev material via Bayesian optimization of magnetization curves

Determining the spin Hamiltonian of a magnetic compound is crucial for understanding its magnetic properties. A standard approach is to derive model parameters from ab initio calculations based on the crystal structure. However, the resulting Hamiltonian can depend sensitively on methodological details of the ab initio procedure. This issue is particularly evident in $α$-RuCl$_3$, a candidate Kitaev material. Here, we present an alternative, data-driven approach to determine the spin Hamiltonian parameters of $α$-RuCl$_3$ by Bayesian optimization of experimental magnetization curves along the $b$- and $c$-axis directions. We optimize five parameters, namely the Kitaev interaction $K$, off-diagonal interactions $Γ$ and $Γ'$, the Heisenberg interaction $J$, and the $c$-axis $g$-factor $g_c$. The parameter set that minimizes the cost function is $(K,Γ,Γ',J,g_c)=(-6.0, 7.5, -0.3, -1.75, 2.3)$, where the exchange couplings are in meV. We find that the cost function is insensitive to the absolute value of the Kitaev coupling $K$. Thus, the magnetization data alone do not determine its energy scale. The cost function also depends only weakly on $Γ'$ and $J$, while the optimization favors a large positive $Γ$. By computing the static spin structure factor, magnetic susceptibility, and specific heat, we show that these quantities favor the large-$Γ$ scenario over the small-$g_c$ scenario and that the parameter set that minimizes the cost function yields good agreement with the experimental structure factor and susceptibility, and qualitative agreement with the specific heat. The combination of Bayesian optimization and accurate low-energy solvers provides an effective approach for determining parameters of spin Hamiltonians. This methodology opens a systematic route to determining spin Hamiltonians in quantum magnets from experimental data.

cond-mat.str-el↗

arXiv2026

Building a research-software catalog with a coding agent: from hackathon prototype to public deployment

Generative AI and coding agents can accelerate research software development, but they also increase the need for efficient software discovery and maintenance. We developed a repository catalog during a three-day hackathon and subsequently examined the engineering required to make it suitable for public deployment, including adversarial review, data-quality checks, browser-level validation, and publication safeguards. We then explored whether the lessons learned from this prototype could be transferred to a much larger, human-curated portal, through a retrieval agent under development for MateriApps that combines curated portal metadata, external documentation, vector search, and local language-model generation. Implementation with coding agents was rapid, but achieving reliable operation required substantial additional engineering: the most consequential problems were not crashes but silent failures that produced plausible yet incomplete or incorrect outputs, arising from incomplete data acquisition, misleading assessments, and retrieval or preprocessing failures. These observations suggest that AI-assisted software portals require explicit validation, monitoring, and repeated review, and that curated metadata and maintained documentation remain essential. The MateriApps work is exploratory and remains under active development, so the observations reported for it are preliminary; a comparable combination of curated metadata, automatically collected documentation, and retrieval-based assistance may nevertheless be useful for extending other research-software portals.

cs.SE↗

arXiv2026

Update of PHYSBO: Improving Usability and Portability of Bayesian Optimization for Physics and Materials Research

Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While many BO frameworks focus on algorithmic efficiency, practical usability and portability are equally critical for sustained use in real research environments. PHYSBO is a Bayesian optimization library designed to address these needs by enabling optimization over user-defined candidate pools and by supporting domain-specific problem settings. This paper presents the major updates introduced in PHYSBO versions 2 and 3, with a focus on improvements in usability, portability, and practical deployment rather than on new optimization algorithms. In PHYSBO version 2, the software license was changed from GPL to MPL to improve compatibility with a wider range of research and software ecosystems. Building on this revision, PHYSBO version 3 introduces a set of implementation-oriented updates aimed at improving usability and portability, without modifying the core optimization algorithms. These updates include improvements in computational performance and scalability, extended support for multi-objective optimization, the introduction of range-based policies for continuous-variable optimization, the removal of environment-dependent components such as tightly coupled Cython modules, and compatibility with NumPy 2. These improvements reduce the technical and organizational burden on users, enabling PHYSBO to be deployed across diverse computing environments and research workflows. By emphasizing portability and ease of integration while maintaining sufficient performance, PHYSBO version 3 is positioned as a sustainable research infrastructure for Bayesian optimization in physics and materials science.

physics.comp-ph↗

arXiv2026

Ambient-Pressure Organic Dirac Electron State in $α$-(BETS)$_2$AuCl$_2$

We report an ambient-pressure Dirac electron (DE) state in a new organic conductor, $α$-(BETS)$_2$AuCl$_2$ (BETS = bis(ethylenedithio)tetraselenafulvalene). This salt exhibits characteristic transport properties, including large positive in-plane and anomalous negative interlayer magnetoresistance. These signatures closely resemble the high-pressure DE states of $α$-(ET)$_2$I$_3$ (ET = bis(ethylenedithio)tetrathiafulvalene). First-principles calculations including spin-orbit coupling identify the electronic state as a quasi-three-dimensional massive Dirac semimetal with residual Fermi pockets. This discovery provides a valuable platform for exploring bulk Dirac fermions without the complexity of high-pressure measurements.

cond-mat.mtrl-sci↗

arXiv2025

Semi-automated estimation of hydrogenic initial states for localized Wannier functions

We present a semi-automated method for obtaining an initial estimate of Wannier functions, designed to facilitate the construction of Wannier functions for describing low-energy effective models of solids, particularly those relevant to strongly correlated electron systems. Our approach automatically determines the hydrogenic projections orbitals and the center of the Wannier functions from information on Bloch wavefunctions at the $Γ$ point. This method is integrated into cif2qewan, enabling seamless generation of input files for Quantum ESPRESSO and Wannier90. We validate our method through applications to both inorganic and organic compounds, such as Si, SrVO$_3$, FeSe, Na$_8$Al$_6$Si$_6$O$_{24}$, and (TMTTF)$_2$PF$_6$. The obtained results demonstrate that our semi-automated projections give a good initial estimate of the Wannier functions. We also show the comparisons with other methods for estimating the initial states of the Wannier functions, such as the Selected Columns of the Density Matrix (SCDM). Our methodology shows an efficient way to construct Wannier functions, paving the way for high-throughput calculations in the study of complex materials.

cond-mat.str-el↗

arXiv2025

Exploring utilization of generative AI for research and education in data-driven materials science

Generative AI has recently had a profound impact on various fields, including daily life, research, and education. To explore its efficient utilization in data-driven materials science, we organized a hackathon -- AIMHack2024 -- in July 2024. In this hackathon, researchers from fields such as materials science, information science, bioinformatics, and condensed matter physics worked together to explore how generative AI can facilitate research and education. Based on the results of the hackathon, this paper presents topics related to (1) conducting AI-assisted software trials, (2) building AI tutors for software, and (3) developing GUI applications for software. While generative AI continues to evolve rapidly, this paper provides an early record of its application in data-driven materials science and highlights strategies for integrating AI into research and education.

cs.CY↗

arXiv2025

Project For Advancement of Software Usability in Materials Science

The Institute for Solid State Physics (ISSP) at The University of Tokyo has been carrying out a software development project named ``the Project for Advancement of Software Usability in Materials Science (PASUMS)". Since the launch of PASUMS, various open-source software programs have been developed/advanced, including ab initio calculations, effective model solvers, and software for machine learning. We also focus on activities that make the software easier to use, such as developing comprehensive computing tools that enable efficient use of supercomputers and interoperability between different software programs. We hope to contribute broadly to developing the computational materials science community through these activities.

cs.SE↗

arXiv2025

Combined X-ray diffraction, electrical resistivity, and $ab$ $initio$ study of (TMTTF)$_2$PF$_6$ under pressure: implications to the unified phase diagram

We present a combined experimental and theoretical study on the quasi-one-dimensional organic conductor (TMTTF)$_2$PF$_6$, and elucidate the variation of its physical properties under pressure. We fully resolve the crystal structure by single crystal x-ray diffraction measurements using a diamond anvil cell up to 8 GPa, and based on the structural data, we perform first-principles density-functional theory calculations and derive the $ab$ $initio$ extended Hubbard-type Hamiltonians. Furthermore, we compare the behavior of the resistivity measured up to 3 GPa using a BeCu clamp-type cell and the ground state properties of the obtained model numerically calculated by the many-variable variational Monte Carlo method. Our main findings are as follows: i) The crystal was rapidly compressed up to about 3 GPa where the volume drops to 80% and gradually varies down to 70% at 8 GPa. The transfer integrals increase following such behavior whereas the screened Coulomb interactions decrease, resulting in a drastic reduction of correlation effect. ii) The degree of dimerization in the intrachain transfer integrals, as the result of the decrease in structural dimerization together with the change in the intermolecular configuration, almost disappears above 4 GPa; the interchain transfer integrals also show characteristic variations under pressure. iii) The results of identifying the characteristic temperatures in the resistivity and the charge and spin orderings in the calculations show an overall agreement: The charge ordering sensitively becomes unstable above 1 GPa, while the spin ordering survives up to higher pressures. These results shed light on the similarities and differences between applying external pressure and substituting the chemical species (chemical pressure).

cond-mat.mtrl-sci↗

arXiv2025

TeNeS-v2: Enhancement for Real-Time and Finite Temperature Simulations of Quantum Many-Body Systems

Quantum many-body systems are challenging targets for computational physics due to their large degrees of freedom. The tensor networks, particularly Tensor Product States (TPS) and Projected Entangled Pair States (PEPS), effectively represent these systems on two-dimensional lattices. However, the technical complexity of TPS/PEPS-based coding is often too much for researchers to handle. To reduce this problem, we developed TeNeS (Tensor Network Solver). This paper introduces TeNeS-v2, which extends TeNeS with real-time and finite temperature simulations, providing deeper insights into quantum many-body systems. We detail the new algorithms, input/output design, and application examples, demonstrating TeNeS-v2's applicability to various quantum spin and Bose models on two-dimensional lattices.

cond-mat.str-el↗

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↗

arXiv2024

Robust analytic continuation using sparse modeling approach imposed by semi-positive definiteness for multi-orbital systems

Analytic continuation (AC) from imaginary-time Green's function to spectral function is essential in the numerical analysis of dynamical properties in quantum many-body systems. However, this process faces a fundamental challenge: it is an ill-posed problem, leading to unstable spectra against the noise in the Green's function. This instability is further complicated in multi-orbital systems with hybridization between spin-orbitals, where off-diagonal Green's functions yield a spectral matrix with off-diagonal elements, necessitating the matrix's semi-positive definiteness to satisfy the causality. We propose an advanced AC method using sparse modeling for multi-orbital systems, which reduces the effect of noise and ensures the matrix's semi-positive definiteness. We demonstrate the effectiveness of this approach by contrasting it with the conventional sparse modeling method, focusing on handling Green's functions with off-diagonal elements, thereby demonstrating our proposed method's enhanced stability and precision.

cond-mat.str-el↗

arXiv2024

Pressure-induced nearly perfect rectangular lattice and superconductivity in an organic molecular crystal (DMET-TTF)$_2$AuBr$_2$

External pressure and associated changes in lattice structures are key to realizing exotic quantum phases such as high-$T_{\rm c}$ superconductivity. While applying external pressure is a standard method to induce novel lattice structures, its impact on organic molecular crystals has been less explored. Here we report a unique structural phase transition in (DMET-TTF)$_2$AuBr$_2$ under pressure. By combining advanced high-pressure techniques and $ab$ $initio$ calculations, we elucidate that (DMET-TTF)$_2$AuBr$_2$ undergoes a transition from a quasi-one-dimensional lattice to a nearly perfect rectangular lattice at 0.9 GPa. This transition leads to the realization of an antiferromagnetic Mott insulator with $T_{\rm N}=66$ K, the highest $T_{\rm N}$ in low-dimensional molecular crystal solids to date. Upon increasing the pressure, the antiferromagnetic ordering is suppressed, and a superconducting phase with $T_{\rm c}=4.8$ K emerges around 6 GPa. Our study reveals the significant impact of external pressure on lattice structures of organic molecular crystals and highlights the intricate relationship between geometrical frustration and superconductivity. Our findings also pave the way for realizing functional organic molecular crystals through changes in lattice structures by pressure.

cond-mat.str-el↗

arXiv2024

Compensated Ferrimagnets with Colossal Spin Splitting in Organic Compounds

The study of the magnetic order has recently been invigorated by the discovery of exotic collinear antiferromagnets with time-reversal symmetry breaking. Examples include altermagnetism and compensated ferrimagnets, which show spin splittings of the electronic band structures even at zero net magnetization, leading to several unique transport phenomena, notably spin-current generation. Altermagnets demonstrate anisotropic spin splitting, such as $d$-wave, in momentum space, whereas compensated ferrimagnets exhibit isotropic spin splitting. However, methods to realize compensated ferrimagnets are limited. Here, we demonstrate a method to realize a fully compensated ferrimagnet with isotropic spin splitting utilizing the dimer structures inherent in organic compounds. Moreover, based on $ab$ $initio$ calculations, we find that this ferrimagnet can be realized in the recently discovered organic compound (EDO-TTF-I)$_2$ClO$_4$. Our findings provide an unprecedented strategy for using the dimer degrees of freedom in organic compounds to realize fully compensated ferrimagnets with colossal spin splitting.

cond-mat.mtrl-sci↗

arXiv2024

Sub-photon accuracy noise reduction of single shot coherent diffraction pattern with atomic model trained autoencoder

Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional noise-reduction processing. This study uses deep learning to validate noise reduction techniques, with autoencoders serving as the learning model. Focusing on the diffraction patterns of nanoparticles, we simulated a large dataset treating the nanoparticles as composed of many independent atoms. Three neural network architectures are investigated: neural network, convolutional neural network and U-net, with U-net showing superior performance in noise reduction and subphoton reproduction. We also extended our models to apply to diffraction patterns of particle shapes different from those in the simulated data. We then applied the U-net model to a coherent diffractive imaging study, wherein a nanoparticle in a microfluidic device is exposed to a single X-ray free-electron laser pulse. After noise reduction, the reconstructed nanoparticle image improved significantly even though the nanoparticle shape was different from the training data, highlighting the importance of transfer learning.

physics.optics↗

arXiv2024

H-wave -- A Python package for the Hartree-Fock approximation and the random phase approximation

H-wave is an open-source software package for performing the Hartree--Fock approximation (HFA) and random phase approximation (RPA) for a wide range of Hamiltonians of interacting fermionic systems. In HFA calculations, H-wave examines the stability of several symmetry-broken phases, such as anti-ferromagnetic and charge-ordered phases, in the given Hamiltonians at zero and finite temperatures. Furthermore, H-wave calculates the dynamical susceptibilities using RPA to examine the instability toward the symmetry-broken phases. By preparing a simple input file for specifying the Hamiltonians, users can perform HFA and RPA for standard Hamiltonians in condensed matter physics, such as the Hubbard model and its extensions. Additionally, users can use a Wannier90-like format to specify fermionic Hamiltonians. A Wannier90 format is implemented in RESPACK to derive ab initio Hamiltonians for solids. HFA and RPA for the ab initio Hamiltonians can be easily performed using H-wave. In this paper, we first explain the basis of HFA and RPA, and the basic usage of H-wave, including download and installation. Thereafter, the input file formats implemented in H-wave, including the Wannier90-like format for specifying the interacting fermionic Hamiltonians, are discussed. Finally, we present several examples of H-wave such as zero-temperature HFA calculations for the extended Hubbard model on a square lattice, finite-temperature HFA calculations for the Hubbard model on a cubic lattice, and RPA in the extended Hubbard model on a square lattice.

cond-mat.str-el↗

arXiv2023

Configuration sampling in multi-component multi-sublattice systems enabled by ab Initio Configuration Sampling Toolkit (abICS)

Simulation of the intermediate levels of disorder found in multi-component multi-sublattice systems in various functional materials is a challenging issue, even for state-of-the-art methodologies based on first-principles calculation. Here, we introduce our open-source package ab Initio Configuration Sampling Toolkit (abICS), which combines high-throughput first-principles calculations, machine learning, and parallel extended ensemble sampling in an active learning setting to enable such simulations. The theoretical background is reviewed in some detail followed by brief notes on usage of the software. In addition, our recent applications of abICS to multi-component ionic systems and their interfaces for energy applications are reviewed as demonstration of the power of this approach.

cond-mat.mtrl-sci↗

arXiv2023

Comprehensive $ab$ $initio$ investigation of the phase diagram of quasi-one-dimensional molecular solids

An $ab$ $initio$ investigation of the family of molecular compounds TM$_2$$X$ is conducted, where TM is either TMTSF or TMTTF and $X$ takes centrosymmetric monovalent anions. By deriving the extended Hubbard-type Hamiltonians from first-principles band calculations and evaluating not only the intermolecular transfer integrals but also the Coulomb parameters, we discuss their material dependence in the unified phase diagram. Furthermore, we apply the many-variable variational Monte Carlo method to accurately determine the symmetry-breaking phase transitions, and show the development of the charge and spin orderings. We show that the material-dependent parameter can be taken as the correlation effect, represented by the value of the screened on-site Coulomb interaction $U$ relative to the intrachain transfer integrals, for the comprehensive understanding of the spin and charge ordering in this system.

cond-mat.str-el↗