arXiv subjects

Masaichiro Mizumaki

Publications and source records attributed to Masaichiro Mizumaki.

At least 19 recordsLinked to original sources

arXiv2026

A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy

Quantitative understanding of coupled reaction-transport dynamics in lithium-ion battery (LIB) composite electrodes is limited by the inaccessibility of key internal electrochemical states. Here we present a physics-integrated, data-driven framework to reconstruct latent states from operando microscopic X-ray absorption fine structure ($μ$ -XAFS) hyperspectral data of LIB cathodes. Normalized-local-lithium-stoichiometry (NLLS) maps derived from Co K-edge spectra are refined using a physics-regularized neural network that enforces spatial continuity and current conservation to resolve ambiguities in the weak-sensitivity region. The reconstructed NLLS dynamics are embedded in a Kirchhoff-based Markov random field incorporating Kirchhoff's laws, Ohm's law, and Butler-Volmer kinetics to infer interfacial current density, ionic current, electrolyte potential, and effective ionic conductivity. Application to electrodes with differential initial electrolyte concentrations reveals distinct reaction-propagation modes that are consistent with a mechanism involving the non-monotonic concentration dependence of electrolyte conductivity. The inferred electrolyte concentration profiles qualitatively resemble the spatially extended concentration changes observed by independent operando X-ray transmission imaging using a 1 M LiAsF$_6$ electrolyte.

physics.chem-ph↗

arXiv2026

Re-examination of electronic structure of dilute Kondo transition-metal ions substituted into a Heavy Fermion compound

Correlations between the localized and conductive spins/charges have been the central issue of various fascinating quantum phenomena found on itinerant electron systems. Here, the obvious multiplet structures are presented on the Mn 2$p$ to 3$d$ x-ray absorption for a heavy fermion $α$-(Yb,Lu)(Al$_{1-x}$Mn$_x$)B$_4$, indicating that the unoccupied electronic structure of the Mn site is described as the correlated high-spin 2+, even though magnetic measurements show the Mn sites to be nonmagnetic. This apparently paradoxical result demonstrates that a ligand field can effectively appear between localized Mn 3$d$ and surrounding B 2$p$ orbitals, which has been anticipated as a manifestation of a Kondo effect but not been clearly confirmed for most itinerant metals in spectroscopy. By contrast, the Mn 2$p$ photoemission indicates that the occupied Mn$^{2+}$ 3$d$ electrons still exhibit itinerant and nonlocally screened nature also owing to the Kondo-like correlation with the conductive B 2$p$, and heavier Yb 4$f$ and 5$d$ bands below the Fermi energy. The asymmetry on the particle-hole stimulates a reconsideration of the correlation and screening effects in the core-level spectroscopies.

cond-mat.str-el↗

arXiv2026

Development of ultra-high efficiency soft X-ray angle-resolved photoemission spectroscopy equipped with deep prior-based denoising method

Soft X-ray angle resolved photoemission spectroscopy (SX-ARPES) is one of the most powerful spectroscopic techniques to visualize the three-dimensional bulk electronic structure in reciprocal lattice space. Compared with ARPES employing low-energy photon sources, the time burden imposed by a lower photoelectron yield, stemming from the photoionization cross-section, has been a persistent technical challenge. To address this challenge, we have developed a noise removal system by using the deep prior-based method and integrated it into the micro focused SX-ARPES (μSX-ARPES) system at BL25SU in SPring-8. Our implemented system effectively eliminates the grid and spike noise typically present in ARPES data acquired using the voltage Fixed-mode, within about 30 seconds. We demonstrate, through the μSX-ARPES measurements on a single crystal of CeRu2Si2, that data with sufficient statistical accuracy can be obtained in approximately 40 seconds. In addition, we present the potential of high signal-to-noise ratio ARPES measurement, achieving an energy resolution of 51.6 meV at an excitation energy of 708 eV in μSX-ARPES measurements on polycrystalline gold. Our developed system successfully reduces the time burden in SX-ARPES and paves the way for advancements in lower photoelectron yield measurements, such as those requiring higher energy resolution and three-dimensional nonequilibrium measurements.

cond-mat.str-el↗

arXiv2026

Sequential Exchange Monte Carlo: A Sampling Method for Bayesian Data Analysis without Parameter Tuning

Bayesian data analysis is widely used across many disciplines, and representative examples in materials science include spectral analysis and sparse modeling. In such applications, the underlying models often become complex and yield multimodal posterior distributions, making efficient sampling from multimodal distributions essential. Replica exchange Monte Carlo has been commonly employed for this purpose; however, its performance strongly depends on difficult parameter tuning, such as the design of the inverse temperature. In this study, we comparatively investigate sampling algorithms that require fewer tuning parameters for Bayesian data analysis in materials science. Specifically, we compare three approaches: non-reversible parallel tempering (NRPT), sequential Monte Carlo samplers (SMCS), and a newly proposed method, sequential exchange Monte Carlo (SEMC). Our results indicate that NRPT can require computational time for parameter tuning, while SMCS requires careful adjustment of the number of MCMC steps at each temperature level. In contrast, SEMC achieves robust convergence across a range of problem settings without additional tuning, demonstrating its practicality for Bayesian inference.

cs.IT↗

arXiv2025

Deep prior-based denoising for state-of-the-art scientific imaging and metrology

Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose deep prior-based denoising, a robust deep learning model that requires no training data. We demonstrate its effectiveness by removing grid artifacts in angle-resolved photoemission spectroscopy (ARPES), a long-standing and critical data analysis challenge in materials science. Our results demonstrate that deep prior-based denoising yields clearer ARPES images in a fraction of the time required by conventional, experiment-based denoising methods. This ultra-efficient approach to ARPES will enable high-speed, high-resolution three-dimensional band structure mapping in momentum space, thereby dramatically accelerating our understanding of microscopic electronic structures of materials. Beyond ARPES, deep prior-based denoising represents a versatile tool that could become a new standard in any advanced scientific measurement fields where data acquisition is limited.

cond-mat.mtrl-sci↗

arXiv2025

Detection of ferroic octupole ordering in $d$-wave altermagnetic rutile-type compounds

We propose that X-ray absorption and emission magnetic circular dichroism (XAS-MCD and XES-MCD) are promising measurements to directly detect ferroic higher-rank multipoles as candidate order parameters in altermagnets. Using the sum rules for XES-MCD and connecting them to multipole language, we demonstrate that the expectation value of the magnetic octupole moment is finite in the $d$-wave altermagnetic candidate rutile-type compounds TF$_2$ (T=transition metal). We also perform spectral calculations of XAS-MCD and XES-MCD based on an effective model with a full multiplet approach. While the intensity of the XAS-MCD spectra vanishes, the XES-MCD spectra exhibit finite intensity, whose spectrum becomes opposite by inverting the Nèel vector. These results clearly indicate ferroic magnetic octupole order in these compounds.

cond-mat.str-el↗

arXiv2025

Bayesian Inference for Small-Angle Scattering Data II: Core-Shell Samples

Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays, are employed in various scientific fields, including materials science, biochemistry, and polymer physics. During the analysis of SAS data, model parameters that contain information about the sample are estimated by fitting the observational data to a model of sample. Previous research has demonstrated the effectiveness of Bayesian inference in analyzing SAS data using a sphere model. However, compared with the sphere model, the core-shell model, which represents functional nanoparticles, offers higher application potential and greater analytical value. Therefore, in this study, we propose an analytical method for the more complex and practical core-shell model based on Bayesian inference. Through numerical experiments, we evaluated the performance of this method under different conditions, including measurement times, number of data points, and differences in scattering length density. As a result, we clarify the conditions under which accurate estimations are possible.

physics.app-ph↗

arXiv2024

On the relationship between orbital moment anisotropy, magnetocrystalline anisotropy, and Dzyaloshinskii-Moriya interaction in W/Co/Pt trilayers

We have studied the Co layer thickness dependences of magnetocrystalline anisotropy (MCA), Dzyaloshinskii-Moriya interaction (DMI), and orbital moment anisotropy (OMA) in W/Co/Pt trilayers, in order to clarify their correlations with each other. We find that the MCA favors magnetization along the film normal and monotonically increases with decreasing effective magnetic layer thickness ($t_\mathrm{eff}$). The magnitude of the Dzyaloshinskii-Moriya exchange constant ($|D|$) increases with decreasing $t_\mathrm{eff}$ until $t_\mathrm{eff} \sim$1 nm, below which $|D|$ decreases. The MCA and $|D|$ scale with $1/t_\mathrm{eff}$ for $t_\mathrm{eff}$ larger than $\sim$1 nm, indicating an interfacial origin. The increase of MCA with decreasing $t_\mathrm{eff}$ continues below $t_\mathrm{eff}$ $\sim$ 1 nm, but with a slower rate. To clarify the cause of the $t_\mathrm{eff}$ dependences of MCA and DMI, the OMA of Co in W/Co/Pt trilayers is studied using x-ray magnetic circular dichroism (XMCD). We find non-zero OMA when $t_\mathrm{eff}$ is smaller than $\sim$0.8 nm. The OMA increases with decreasing $t_\mathrm{eff}$ more rapidly than what is expected from the MCA, indicating that factors other than OMA contribute to the MCA at small $t_\mathrm{eff}$. The $t_\mathrm{eff}$ dependence of the OMA also suggests that $|D|$ at $t_\mathrm{eff}$ smaller than $\sim$1 nm is not related to the OMA at the interface. We propose that the growth of Co on W results in a strain and/or texture that reduces the interfacial DMI, and, to some extent, MCA at small $t_\mathrm{eff}$.

cond-mat.mtrl-sci↗

arXiv2024

Mesoscopic Bayesian Inference by Solvable Models

The rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a revolution in research methodology. Our group has proposed Bayesian measurement, a framework that applies Bayesian inference to measurement science with broad applicability across various natural sciences. This framework enables the determination of posterior probability distributions of system parameters, model selection, and the integration of multiple measurement datasets. However, applying Bayesian measurement to real data analysis requires a more sophisticated approach than traditional statistical methods like Akaike information criterion (AIC) and Bayesian information criterion (BIC), which are designed for an infinite number of measurements $N$. Therefore, in this paper, we propose an analytical theory that explicitly addresses the case where $N$ is finite in the linear regression model. We introduce $O(1)$ mesoscopic variables for $N$ observation noises. Using this mesoscopic theory, we analyze the three core principles of Bayesian measurement: parameter estimation, model selection, and measurement integration. Furthermore, by introducing these mesoscopic variables, we demonstrate that the difference in free energies, critical for both model selection and measurement integration, can be analytically reduced by two mesoscopic variables of $N$ observation noises. This provides a deeper qualitative understanding of model selection and measurement integration and further provides deeper insights into actual measurements for nonlinear models. Our framework presents a novel approach to understanding Bayesian measurement results.

physics.data-an↗

arXiv2024

Procedure to Reveal the Mechanism of Pattern Formation Process by Topological Data Analysis

Topological data analysis (TDA) is a versatile tool that can be used to extract scientific knowledge from complex pattern formation processes. However, the physics correspondence between the features obtained from TDA and pattern dynamics does not agree one-to-one, and the physical interpretation of the TDA features needs to be set appropriately according to the phenomenon to be analyzed. In this study, we propose an analytical procedure to physically interpret pattern dynamics through TDA and machine learning techniques. The proposed procedure was applied to the process of magnetic domain pattern formation to quantify non-trivial domain pattern classifications and reveal the nature of the underlying dynamics. On the basis of these findings, we also propose a candidate reduction model to understand the nature of magnetic domain formation.

nlin.PS↗

arXiv2024

Mechanism of intermetallic charge transfer and bond disproportionation in BiNiO$_3$ and PbNiO$_3$ revealed by hard x-ray photoemission spectroscopy

Perovskites with Bi or Pb on the A-site host a number of interesting and yet to be understood phenomena such as negative thermal expansion in BiNiO$_3$. We employ hard x-ray photoemission spectroscopy of Ni 2$p$ core-level as well as valence band to probe the electronic structure of BiNiO$_3$ and PbNiO$_3$. The experimental results supported by theoretical calculations using dynamical mean-field theory reveal essentially identical electronic structure of the Ni-O subsystem typical of Ni$^{2+}$ charge-transfer insulators. The two materials are distinguished by filling of the Bi(Pb)-O antibonding states in the vicinity of the Fermi level, which is responsible for the Bi disproportionation in BiNiO$_3$ at ambient pressure and absence of similar behavior in PbNiO$_3$. The present experiments provide evidence for this conclusion by revealing the presence/absence of Bi/Pb $6s$ states at the top of the valence band in the two materials.

cond-mat.str-el↗

arXiv2024

Basis Function Dependence of Estimation Precision for Synchrotron-Radiation-Based Mössbauer Spectroscopy

Mössbauer spectroscopy is a technique employed to investigate the microscopic properties of materials using transitions between energy levels in the nuclei. Conventionally, in synchrotron-radiation-based Mössbauer spectroscopy, the measurement window is decided by the researcher heuristically, although this decision has a significant impact on the shape of the measurement spectra. In this paper, we propose a method for evaluating the precision of the spectral position by introducing Bayesian estimation. The proposed method makes it possible to select the best measurement window by calculating the precision of Mössbauer spectroscopy from the data. Based on the results, the precision of the Mössbauer center shifts improved by more than three times compared with the results achieved with the conventional simple fitting method using the Lorentzian function.

physics.comp-ph↗

arXiv2024

Quantitative Selection of Sample Structures in Small-Angle Scattering Using Bayesian Methods

Small-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting.This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models.We assess the performance of our method through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that our proposed method analysis approach yields highly accurate and interpretable results.We also discuss the ability of our method to analyze a range of mixing ratios and particle size ratios for mixed components, along with its precision in model evaluation by the degree of fitting.Our proposed method effectively facilitates quantitative analysis of nano-scale sample structures in SAS, which has traditionally been challenging, and is expected to significantly contribute to advancements in a wide range of fields.

physics.data-an↗

arXiv2023

Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning

We estimate the spatial distribution of heterogeneous physical parameters involved in the formation of magnetic domain patterns of polycrystalline thin films by using convolutional neural networks. We propose a method to obtain a spatial map of physical parameters by estimating the parameters from patterns within a small subregion window of the full magnetic domain and subsequently shifting this window. To enhance the accuracy of parameter estimation in such subregions, we employ large-scale models utilized for natural image classification and exploit the benefits of pretraining. Using a model with high estimation accuracy on these subregions, we conduct inference on simulation data featuring spatially varying parameters and demonstrate the capability to detect such parameter variations.

cond-mat.mtrl-sci↗

arXiv2023

Bayesian Inference for Small-Angle Scattering Data

In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve processes of manual parameter adjustment by analysts or optimization using gradient methods. These analysis processes tend to involve heuristic approaches and may lead to local solutions.Furthermore, it is difficult to evaluate the reliability of the results obtained by conventional analysis methods. Our method solves these problems by estimating model parameters as probability distributions from SAS data using the framework of the Bayesian inference. We evaluate the performance of our method through numerical experiments using artificial data of representative measurement target models.From the results of the numerical experiments, we show that our method provides not only high accuracy and reliability of estimation, but also perspectives on the transition point of estimability with respect to the measurement time and the lower bound of the angular domain of the measured data.

stat.ME↗

arXiv2023

Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model

In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample fragility and high energy costs. To improve the efficiency of experiments, sequential experimental designs are proposed, in which the subsequent measurement is designed by active learning using the data obtained before the measurement. Conventionally, parametric models are employed in data analysis; when employed for active learning, they are expected to afford a sequential experimental design that improves the accuracy of data analysis. However, due to the complexity of the formulas, a sequential experimental design using general parametric models has not been realized. Therefore, we applied Bayesian inference-based data analysis using the exchange Monte Carlo method to realize a sequential experimental design with general parametric models. In this study, we evaluated the effectiveness of the proposed method by applying it to Bayesian spectral deconvolution and Bayesian Hamiltonian selection in X-ray photoelectron spectroscopy. Using numerical experiments with artificial data, we demonstrated that the proposed method improves the accuracy of model selection and parameter estimation while reducing the measurement time compared with the results achieved without active learning or with active learning using the Gaussian process regression.

cs.LG↗

arXiv2023

Bayesian Inference of Absorption Spectra Based on Binomial Distribution

In this paper, we propose a Bayesian spectral deconvolution method for absorption spectra. In conventional analysis, the noise mechanism of absorption spectral data is never considered appropriately. In that analysis, the least-squares method, which assumes Gaussian noise from the perspective of Bayesian statistics, is frequently used. Since Bayesian inference is possible by introducing an appropriate noise model for the data, we consider the absorption process of a single photon to be a Bernoulli trial and develop a Bayesian spectral deconvolution method based on binomial distribution. We have evaluated our method on artificial data under several conditions by numerical experiments. The results show that our method not only allows us to estimate parameters with high accuracy from absorption spectral data, but also to infer them even from absorption spectral data with large absorption rates where the spectral structure is flattened, which was previously impossible to analyze.

stat.ME↗

arXiv2022

Bayesian Inference on Hamiltonian Selections for Mössbauer Spectroscopy

Mössbauer spectroscopy, which provides knowledge related to electronic states in materials, has been applied to various fields such as condensed matter physics and material sciences. In conventional spectral analyses based on least-square fitting, hyperfine interactions in materials have been determined from the shape of observed spectra. In conventional spectral analyses, it is difficult to discuss the validity of the hyperfine interactions and the estimated values. We propose a spectral analysis method based on Bayesian inference for the selection of hyperfine interactions and the estimation of Mössbauer parameters. An appropriate Hamiltonian has been selected by comparing Bayesian free energy among possible Hamiltonians. We have estimated the Mössbauer parameters and evaluated their estimated values by calculating the posterior distribution of each Mössbauer parameter with confidence intervals. We have also discussed the accuracy of the spectral analyses to elucidate the noise intensity dependence of numerical experiments.

physics.comp-ph↗