arXiv subjects

Yusuke Hashimoto

Publications and source records attributed to Yusuke Hashimoto.

13 recordsLinked to original sources

arXiv2026

Building a physics-aware AI ecosystem for solid-state hydrogen storage materials

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

cond-mat.mtrl-sci↗

arXiv2026

A unified descriptor framework for hydrogen storage capacity and equilibrium pressure in interstitial hydrides

Hydrogen is a promising energy carrier, yet its practical deployment is limited by the lack of storage materials that simultaneously achieve high storage capacity ($w$) and practical equilibrium pressure at room temperature ($P_{\rm eq,RT}$). Interstitial metal hydrides offer fast kinetics and favorable thermodynamics (high $P_{\rm eq,RT}$) but suffer from intrinsically low w. Here, we establish a physically interpretable, data-driven framework to uncover descriptor-property relationships in interstitial hydrides using a curated database of pressure-composition-temperature measurements (Digital Hydrogen Platform, DigHyd) and white-box symbolic regression. Strikingly, the analysis reveals a clear separation of governing mechanisms, in which $w$ is governed by geometric and lattice conditions, captured by the average atomic radius ($\left\langle r_M \right\rangle$) and average thermal conductivity ($\left\langle\kappa\right\rangle$), with an optimal regime of $r_M \sim 1.47 \r{A}$ and relatively low $\left\langle\kappa\right\rangle$. In contrast, $P_{\rm eq,RT}$ is governed by elastic properties, captured by the average shear modulus ($\left\langle G \right\rangle$) and average Poisson's ratio ($\left\langle \nu \right\rangle$), reflecting the role of lattice rigidity and mechanical compliance. These relationships are translated into compositional optimization pathways that follow the descriptor trends above, enabling the design of candidate materials with enhanced w under practical equilibrium conditions ($P_{\rm eq,RT} \sim 0.1$ MPa). This work establishes a general, interpretable strategy for physics-informed design of energy materials systems.

cond-mat.mtrl-sci↗

arXiv2026

Quantifying chirality of phonons

Recent years have witnessed growing interest in chiral phonons, lattice vibrations carrying angular momentum and exhibiting handedness, as revealed by helicity-dependent optical phenomena. Despite this progress, a quantitative characterization of phonon chirality as a dynamical property has remained elusive. In this work, we propose a theoretical framework to quantify the dynamical chirality of lattice vibrations. We introduce two quantitative measures: momentum-resolved dynamical chirality, which provides a mode- and wave-vector-resolved picture of phonon chirality, and the bulk dynamical chirality, which characterizes the collective behavior of thermally populated chiral phonons. Using first-principles calculations for both chiral and achiral materials, we demonstrate how these quantities capture the handedness and population imbalance of phonon modes and serve as a means to distinguish the enantiomers of chiral crystals.

cond-mat.mtrl-sci↗

arXiv2026

Digital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining

Solid-state hydrogen storage materials are promising candidates for safe and compact hydrogen storage; however, data-driven discovery in this field remains limited by the availability of large-scale, well-curated datasets. Here, we present the Digital Hydrogen Platform (DigHyd: www.dighyd.org), a rigorously curated database comprising $>4,000$ experimental literature sources and $>30,000$ data entries on hydrogen storage materials, constructed through AI-assisted literature mining combined with human-in-the-loop validation. In addition to gravimetric hydrogen storage density ($w$), DigHyd also covers thermodynamic parameters, specifically the enthalpy ($\Delta H$) and entropy ($\Delta S$) changes associated with hydrogenation reactions, primarily defined as $M + \frac{1}{2} {\rm H}_2 \rightleftarrows M{\rm H}$. These parameters were obtained by manually analyzing multi-temperature pressure-composition-temperature (PCT) data using van't Hoff analysis. By focusing on $\Delta H$ and $\Delta S$ rather than fixing equilibrium pressure at a single temperature, DigHyd enables flexible evaluation of equilibrium behavior under application-specific operating conditions. Statistical analyses reveal distinct distributions of thermodynamic parameters across material classes, together with broad compositional variability within representative hydride systems. Furthermore, both physically interpretable symbolic regression and black-box XGBoost models achieve comparable predictive performance for $w$ and equilibrium pressure at room temperature ($P_{\rm eq,RT}$), demonstrating internal consistency and learnable composition-property relationships within the curated dataset. Overall, DigHyd provides a rigorously curated thermodynamic dataset that serves as a reliable basis for data-driven analyses of hydrogen storage materials and supports systematic exploration of structure-property relationships.

cond-mat.mtrl-sci↗

arXiv2025

"DIVE" into Hydrogen Storage Materials Discovery with AI Agents

Data-driven artificial intelligence (AI) approaches are fundamentally transforming the discovery of new materials. Despite the unprecedented availability of materials data in the scientific literature, much of this information remains trapped in unstructured figures and tables, hindering the construction of large language model (LLM)-based AI agent for automated materials design. Here, we present the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow, which systematically reads and organizes experimental data from graphical elements in scientific literatures. We focus on solid-state hydrogen storage materials-a class of materials central to future clean-energy technologies and demonstrate that DIVE markedly improves the accuracy and coverage of data extraction compared to the direct extraction by multimodal models, with gains of 10-15% over commercial models and over 30% relative to open-source models. Building on a curated database of over 30,000 entries from 4,000 publications, we establish a rapid inverse design workflow capable of identifying previously unreported hydrogen storage compositions in two minutes. The proposed AI workflow and agent design are broadly transferable across diverse materials, providing a paradigm for AI-driven materials discovery.

cs.AI↗

arXiv2025

A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery

Materials informatics (MI), emerging from the integration of materials science and data science, is expected to significantly accelerate material development and discovery. The data used in MI are derived from both computational and experimental studies; however, their integration remains challenging. In our previous study, we reported the integration of these datasets by applying a machine learning model that is trained on the experimental dataset to the compositional data stored in the computational database. In this study, we use the obtained datasets to construct materials maps, which visualize the relationships between material properties and structural features, aiming to support experimental researchers. The materials map is constructed using the MatDeepLearn (MDL) framework, which implements materials property prediction using graph-based representations of material structure and deep learning modeling. Through statistical analysis, we find that the MDL framework using the message passing neural network (MPNN) architecture efficiently extracts features reflecting the structural complexity of materials. Moreover, we find that this advantage does not necessarily translate into improved accuracy in the prediction of material properties. We attribute this unexpected outcome to the high learning performance inherent in MPNN, which can contribute to the structuring of data points within the materials map.

cond-mat.mtrl-sci↗

arXiv2022

Composition-tunable magnon-polaron anomalies in spin Seebeck effects in epitaxial Bi$_x$Y$_{3-x}$Fe$_{5}$O$_{12}$ films

We have investigated hybridized magnon-phonon excitation (magnon polarons) in spin Seebeck effects (SSEs) in Bi$_x$Y$_{3-x}$Fe$_{5}$O$_{12}$ (Bi$_x$Y$_{3-x}$IG; $x=0$, $0.5$, and $0.9$) films with Pt contact. We observed sharp peak structures in the magnetic field $H$ dependence of the longitudinal SSE (LSSE) voltages, which appear when the phonon dispersions are tangential to the magnon dispersion curve in Bi$_x$Y$_{3-x}$IG. By increasing the Bi amount $x$, the peak fields in the LSSE shift toward lower $H$ values due to the reduction of the sound velocities in Bi$_x$Y$_{3-x}$IG. We also measured the SSE in a nonlocal configuration and found that magnon-polaron anomalies appear with different signs and intensities. Our result shows composition-tunability of magnon-polaron anomalies and provides a clue to further unravel the physics of magnon-polaron SSEs.

cond-mat.mtrl-sci↗

arXiv2022

Prototype Development and Validation of a Beam-Divergence Control System for Free-Space Laser Communications

Being able to dynamically control the transmitted-beam divergence can bring important advantages in free-space optical communications. Specifically, this technique can help to optimize the overall communications performance when the optimum laser-beam divergence is not fixed or known. This is the case in most realistic space laser communication systems, since the optimum beam divergence depends on multiple factors that can vary with time, such as the link distance, or cannot be accurately known, such as the actual pointing accuracy. A dynamic beam-divergence control allows to optimize the link performance for every platform, scenario, and condition. NICT is currently working towards the development of a series of versatile lasercom terminals that can fit a variety of conditions, for which the adaptive element of the transmitted beam divergence is a key element. This manuscript presents a prototype of a beam-divergence control system designed and developed by NICT and Tamron to evaluate this technique and to be later integrated within the lasercom terminals. The basic design of the prototype is introduced as well as the first validation tests that demonstrate its performance.

eess.SY↗

arXiv2020

Excitation and relaxation dynamics of spin-waves triggered by ultrafast photo-induced demagnetization in a ferrimagnetic insulator

Excitation and propagation dynamics of spin waves in an iron-based garnet film under out-of-plane magnetic field were investigated by time-resolved magneto-optical imaging. The experimental results and the following data analysis by phase-resolved spin-wave tomography reveal the excitation of spin waves triggered by photo-induced demagnetization (PID) along the sample depth direction. Moreover, the fast relaxation of PID accompanied by the spin transfer due to spin-wave emission was observed. Possible scenarios of PID in the garnet film are discussed. Finally, we develop a model for the spin-wave excitation triggered by PID and explain the magnetic-field dependence in the amplitude of the observed spin waves.

cond-mat.mtrl-sci↗

arXiv2019

Snell's law for spin waves at a 90-degree domain wall

We report experimental observation of the refraction and re ection of propagating magnetostatic spin waves crossing a 90-degree domain wall (DW) in terms of time-resolved magneto-optical imaging. Due to the magnetization rotation across the 90-degree DW, the dispersion relation of magnetostatic spin waves rotates by 90 degrees, which results in the change in the propagation dynamics of spin waves in both sides of the DW. We observe the refraction and re ection of magnetosatatc spin waves at the 90-degree DW, and reveal their characteristics, such as negative refraction. The incident-angle dependence of the refraction angle is explained by the wavenumber conservation along the DW, same as the case of Snell's law for light.

cond-mat.other↗

arXiv2018

180-degree phase shift of magnetoelastic waves observed by phase-resolved spin-wave tomography

We have investigated optically-excited magnetoelastic waves by phase-resolved spin-wave tomography (PSWaT). PSWaT reconstructs dispersion relation of spin waves together with their phase information by using time-resolved magneto-optical imaging for spin-wave propagation followed by an analysis based on the convolution theorem and a complex Fourier transform. In PSWaT spectra for a Bi-doped garnet film, we found a 180 degree phase shift of magnetoelastic waves at around the crossing of the dispersion relations of spin and elastic waves. The result is explained by a coupling between spin waves and elastic waves through magnetoelastic interaction. We also propose an efficient way for phase manipulation of magnetoelastic waves by rotating the orientation of magnetization less than 10 degree.

cond-mat.mtrl-sci↗

arXiv2017

Phase-resolved Spin-Wave Tomography

The propagation dynamics of spin waves are represented by their dispersion relations. Recently, we have developed a method, called spin-wave tomography (SWaT), to obtain dispersion relation of spin waves in the long wavelength regime, so-called pure magnetostatic waves. In our previous studies on SWaT, phase information of spin waves was disregarded. In this report, we demonstrate an advanced SWaT analysis, called phase-resolved spin-wave tomography (PSWaT), to realize the direct observation of the amplitude and the phase of spin waves. The PSWaT spectra are obtained by separating the real and the imaginary components of the complex Fourier transform in the SWaT analysis. We demonstrate the PSWaT spectra of spin waves excited by the photo-induced demagnetization in a Bi-doped garnet film, reflecting the characteristic features of the complex dynamical susceptibility affected by magnetostatic coupling in the film.

cond-mat.mtrl-sci↗

arXiv2017

Frequency and wavenumber selective excitation of spin waves through coherent energy transfer from elastic waves

Using spin-wave tomography (SWaT), we have investigated the excitation and the propagation dynamics of optically-excited magnetoelastic waves, i.e. hybridized modes of spin waves and elastic waves, in a garnet film. By using time-resolved SWaT, we reveal the excitation dynamics of magnetoelastic waves through coherent-energy transfer between optically-excited pure-elastic waves and spin waves via magnetoelastic coupling. This process realizes frequency and wavenumber selective excitation of spin waves at the crossing of the dispersion relations of spin waves and elastic waves. Finally, we demonstrate that the excitation mechanism of the optically-excited pure-elastic waves, which are the source of the observed magnetoelastic waves, is dissipative in nature.

cond-mat.mtrl-sci↗