arXiv subjects

Ichiro Akai

Publications and source records attributed to Ichiro Akai.

10 recordsLinked to original sources

arXiv2026

Bayesian Localization and Uncertainty Quantification of Trace Species in Two-Dimensional SIMS Imaging

To enable accurate localization of trace species on material surfaces, we propose a Bayesian framework for analyzing two-dimensional (2D) secondary ion mass spectrometry (SIMS) imaging data. SIMS is widely used in semiconductor manufacturing, materials science, geology, environmental science, and life sciences because of its high sensitivity and excellent elemental and isotopic specificity. However, precise localization remains challenging because of primary ion beam broadening, overlap between neighboring ion distributions, and limited ion counts. The underlying distribution of trace species is modeled as a superposition of two-dimensional Gaussian peaks. To account for the stochastic nature of low-count measurements, the detected ion counts are assumed to follow a Poisson likelihood within a Bayesian framework. Posterior distributions of the peak parameters are estimated using replica-exchange Monte Carlo (REMC), enabling stable inference together with quantitative uncertainty estimation under low-count conditions. The proposed method is first validated using synthetic datasets with known ground truth and is then applied to SIMS measurements of semiconductor samples containing regularly arranged gold (Au) dots with diameters ranging from 0.4 to 2.0~$\mu$m, using scanning electron microscopy (SEM) images as the reference. Optimization of the measurement conditions reduced the relative localization error for 0.4~$\mu$m dots from 5.1$\%$ to 1.9$\%$. These results demonstrate accurate submicrometer localization with statistically rigorous uncertainty quantification in 2D SIMS imaging.

physics.chem-ph↗

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 and transport processes in lithium-ion battery (LIB) composite electrodes remains challenging because key internal states cannot be measured directly. In this study, we develop a physics-integrated, data-driven analysis pipeline to estimate internal electrochemical states from operando microscopic X-ray absorption fine structure ($\mu$-XAFS) hyperspectral data of LIB cathodes with LiPF$_6$ electrolyte. State-of-charge (SOC) maps are first constructed from Co K-edge spectra. To resolve ambiguities in the two-phase reaction region, a physics-regularized three-layer neural network is introduced, enforcing spatial continuity of SOC and current conservation. The inferred SOC dynamics are then incorporated into a Kirchhoff-based Markov random field framework that integrates Kirchhoff's current and voltage laws, Ohm's law, and a symmetric Butler-Volmer relation to estimate interfacial current, ionic current, electrolyte potential, and effective ionic conductivity. Application to composite electrodes with different initial electrolyte concentrations (0.3, 1, and 2M LiPF$_6$) reveals distinct reaction propagation behaviors governed by electrolyte concentration-dependent conductivity. The inferred electrolyte concentration distributions show qualitative agreement with independent operando X-ray transmission imaging performed on LIB composite cathodes employing a LiAsF$_6$ electrolyte. This framework enables quantitative visualization of otherwise inaccessible internal transport phenomena.

physics.chem-ph↗

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

Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra

We analyzed the X-ray photoemission spectra (XPS) of carbon 1s states in graphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesian spectroscopy. To realize highly accurate spectral decomposition of the XPS spectra, we proposed a framework for discovering physical constraints from the absence of prior quantified physical knowledge, in which we designed the prior probabilities based on the found constraints and the physically required conditions. This suppresses the exchange of peak components during replica exchange Monte Carlo iterations and makes possible to decompose XPS in the case where a reliable structure model or a presumable number of components is not known. As a result, we have successfully decomposed XPS of one monolayer (1ML), two monolayers (2ML), and quasi-freestanding 2ML (qfs-2ML) graphene samples deposited on SiC substrates with the meV order precision of the binding energy, in which the posterior probability distributions of the binding energies were obtained distinguishably between the different components of buffer layer even though they are observed as hump and shoulder structures because of their overlapping.

cond-mat.mtrl-sci↗

arXiv2022

Bayesian Spectral Deconvolution of X-Ray Absorption Near Edge Structure Discriminating High- and Low-Energy Domains

In this paper, we propose a Bayesian spectral deconvolution considering the properties of peaks in different energy domains. Bayesian spectral deconvolution regresses spectral data into the sum of multiple basis functions. Conventional methods use a model that treats all peaks equally. However, in X-ray absorption near edge structure (XANES) spectra, the properties of the peaks differ depending on the energy domain, and the specific energy domain of XANES is essential in condensed matter physics. We propose a model that discriminates between the low- and high-energy domains. We also propose a prior distribution that reflects the physical properties. We compare the conventional and proposed models in terms of computational efficiency, estimation accuracy, and model evidence. We demonstrate that our method effectively estimates the number of transition components in the important energy domain, on which the material scientists focus for mapping the electronic transition analysis by first-principles simulation.

stat.ME↗

arXiv2021

Non-Negative Matrix Factorization for 2D-XAS Images of Lithium Ion Batteries

Lithium-ion secondary batteries have been used in a wide variety of purposes, such as for powering mobile devices and electric vehicles, but their performance should be improved. One of the factors that limits their performance is the non-uniformity of the chemical reaction in the process of charging and discharging. Many attempts have been made to elucidate the mechanism behind this reaction non-uniformity. In this paper, to detect non-uniformity in various physical properties from Co K-edge two-dimensional X-ray absorption spectroscopy (2D-XAS) images of lithium ion batteries, we propose a method that consists of one-sided orthogonal non-negative matrix factorization in combination with removal of the reference signal. The difference between X-ray absorption spectra acquired at different positions in the battery is very small. However, even in such a situation, our method can decompose the 2D-XAS data into different spatial domains and their corresponding absorption spectra. From the spectral decomposition of the obtained absorption spectra, we confirmed a transition-energy shift of the main peak as evidence for a change in the state of charge and also found spectral changes due to orbital hybridization in the decomposed spectral components.

physics.app-ph↗

arXiv2021

Appropriate basis selection based on Bayesian inference for analyzing measured data reflecting photoelectron wave interference

In this study, we applied Bayesian inference for extended X-ray absorption fine structure (EXAFS) to select an appropriate basis from among Fourier, wavelet and advanced Fourier bases, and we extracted a radial distribution function (RDF) and physical parameters from only EXAFS signals using physical prior knowledge, which is to be realized in general in condensed systems. To evaluate our method, the well-known EXAFS spectrum of copper was used for the EXAFS data analysis. We found that the advanced Fourier basis is selected as an appropriate basis for the regression of the EXAFS signal in a quantitative way and that the estimation of the Debye-Waller factor can be robustly realized only by using the advanced Fourier basis. Bayesian inference based on minimal restrictions allows us to not only eliminate some unphysical results but also select an appropriate basis. Generally, FEFF analysis is used for estimating physical parameters such as Deby-Waller and extracting RDF. Bayesian inference enables us to simultaneously select an appropriate basis and optimized physical parameters without FEFF analysis, which results in extracting RDF from only EXAFS signals. These advantages lead to the general usage of Bayesian inference for EXAFS data analysis.

physics.data-an↗

arXiv2021

Sparse Modeling analysis of Extended X-ray Absorption Fine Structure data using two-body expansion

Analysis of extended X-ray absorption fine structure (EXAFS) data by the use of sparse modeling is presented. We consider the two-body term in the n-body expansion of the EXAFS signal to implement the method, together with calculations of amplitudes and phase shifts to distinguish between different back-scattering elements. Within this approach no a priori assumption about the structure is used, other than the elements present inside the material. We apply the method to the experimental EXAFS signal of metals and oxides, for which we were able to extract the radial distribution function peak positions, and the Debye-Waller factor for first neighbors.

physics.data-an↗

arXiv2020

A Phase Prediction Method for Pattern Formation in Time-Dependent Ginzburg-Landau Dynamics for Kinetic Ising Model without a priori Assumptions on Domain Patterns

We propose a phase prediction method for the pattern formation in the uniaxial two-dimensional kinetic Ising model with the dipole-dipole interactions under the time-dependent Ginzburg-Landau dynamics. Taking the effects of the material thickness into account by assuming the uniformness along the magnetization axis, the model corresponds to thin magnetic materials with long-range repulsive interactions. We propose a new theoretical basis to understand the effects of the material parameters on the formation of the magnetic domain patterns in terms of the equation of balance governing the balance between the linear- and nonlinear forces in the equilibrium state. Based on this theoretical basis, we propose a new method to predict the phase in the equilibrium state reached after the time-evolution under the dynamics with a given set of parameters, by approximating the third-order term using the restricted phase-space approximation [R. Anzaki, K. Fukushima, Y. Hidaka, and T. Oka, Ann. Phys. 353, 107 (2015)] for the $ϕ^4$-models. Although the proposed method does not have the perfect concordance with the actual numerical results, it has no arbitrary parameters and functions to tune the prediction. In other words, it is a method with no a priori assumptions on domain patterns.

cond-mat.stat-mech↗

arXiv2018

Bayesian Hamiltonian Selection in X-ray Photoelectron Spectroscopy

Core-level X-ray photoelectron spectroscopy (XPS) is a useful measurement technique for investigating the electronic states of a strongly correlated electron system. Usually, to extract physical information of a target object from a core-level XPS spectrum, we need to set an effective Hamiltonian by physical consideration so as to express complicated electron-to-electron interactions in the transition of core-level XPS, and manually tune the physical parameters of the effective Hamiltonian so as to represent the XPS spectrum. Then, we can extract physical information from the tuned parameters. In this paper, we propose an automated method for analyzing core-level XPS spectra based on the Bayesian model selection framework, which selects the effective Hamiltonian and estimates its parameters automatically. The Bayesian model selection, which often has a large computational cost, was carried out by the exchange Monte Carlo sampling method. By applying our proposed method to the 3$d$ core-level XPS spectra of Ce and La compounds, we confirmed that our proposed method selected an effective Hamiltonian and estimated its parameters appropriately; these results were consistent with conventional knowledge obtained from physical studies. Moreover, using our proposed method, we can also evaluate the uncertainty of its estimation values and clarify why the effective Hamiltonian was selected. Such information is difficult to obtain by the conventional analysis method.

cond-mat.str-el↗