arXiv subjects

Ryuhei Sato

Publications and source records attributed to Ryuhei Sato.

14 recordsLinked to original sources

arXiv2026

Why Ammoniated Lithium Borohydrides Liquefy and Resolidify?

Ammonia ($\mathrm{NH_3}$) absorption drives $\mathrm{LiBH_4\!\cdot\!xNH_3}$ through a re-entrant ``solid--liquid--solid'' transition: $\mathrm{LiBH_4\!\cdot\!NH_3}$ is a well-defined solid ammoniate, compositions near $\mathrm{LiBH_4\!\cdot\!2NH_3}$ are liquid-like or partially liquefied, whereas $\mathrm{LiBH_4\!\cdot\!3NH_3}$ returns to a more rigid non-liquid ammoniate state. However, the microscopic origin of this unintuitive response remains a long-lasting mystery. Here, we uncover its mechanism. Cross-database analysis identifies borohydrides as a particularly state-diverse and composition-responsive material family. Structure prediction and ab initio molecular simulations reveal that $\mathrm{NH_3}$ progressively replaces $\mathrm{BH_4^-}$ in the Li coordination shell. The liquid-like state emerges not at the highest $\mathrm{NH_3}$ loading but near $x\approx2$, where Li--N and Li--B coordination modes are strongly mixed, coordination memory is weakest, and the sampled Li--N/N$\cdots$B coordination landscape is broadest. Further ammoniation produces Li--N-dominant coordination and slows $\mathrm{BH_4^-}/\mathrm{NH_3}$ contact renewal, with the resulting increase in network persistence and accompanying recovery of a rigid ammoniate state. Pressure--composition isotherm, $^{1}\mathrm{H}$ and $^{11}\mathrm{B}$ nuclear magnetic resonance, and Raman measurements support this non-monotonic state evolution and associated $\mathrm{BH_4^-}/\mathrm{NH_3}$ reorganization. These findings transform ammonia-induced liquefaction from an empirical phase anomaly into a competition between native-network disruption, mixed-coordination frustration, and ligand-built network reconstruction, providing a framework for chemically switching between transport-favouring fluidity and stability-favouring rigidity in hydrogen-rich materials.

cond-mat.mtrl-sci↗

arXiv2026

When Literature Data Mislead Artificial Intelligence in Materials Discovery

Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data. Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes

cs.IR↗

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

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↗

arXiv2026

Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics

The synthesis of the high-$T_c$ superhydride CaH$_6$ has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mechanisms governing such hydrogenation reactions remain poorly understood. Here, we show that machine-learning potential molecular dynamics (MLP-MD) simulations can reproduce and distinguish competing reaction pathways leading to metastable and stable hydrides. By simulating hydrogenation reactions at CaH$_2$/H$_2$ and CaH$_4$/H$_2$ interfaces, we identify two distinct pathways that produce clathrate-type CaH$_6$ and A15-type CaH$_{5.75}$, respectively. CaH$_{5.75}$ lies on the convex hull but requires extensive Ca sublattice rearrangement and therefore forms only at elevated temperatures. In contrast, CaH$_6$ becomes kinetically accessible when CaH$_2$ is used as the precursor. The crystallographic compatibility between the Ca sublattice of CaH$_2$ and the bcc framework of CaH$_6$ enables a martensitic-like topotactic transformation that bypasses the reconstructive pathway leading to CaH$_{5.75}$. These results reveal how precursor structure and thermodynamic stability compete to determine superhydride formation pathways and demonstrate that machine-learning molecular dynamics can directly capture the kinetic selection of metastable phases in reactive materials systems.

cond-mat.mtrl-sci↗

arXiv2025

Tuning Stability of AB3-Type Alloys by Suppressing Magnetism

Hydrogen is a promising clean energy carrier, yet effective and reversible storage remains challenging. AB3-type intermetallic alloys are promising for solid-state hydrogen storage due to intermediate thermodynamic stability and rapid hydrogen uptake. Optimizing stability and gravimetric density is hindered by competing thermodynamic and magnetic effects. Here, we analyze AB3 compounds (A = Ca, Y, Mg; B = Co, Ni) and ternary alloys CaxYyMg1-x-yB3 using first-principles calculations and Monte Carlo simulations. We find a direct correlation between formation energy and total magnetic moment that dictates alloy stability, explaining the trade-off in hydrogen storage. In Co-rich systems with large lattice volumes, formation energy rises with magnetization, showing magnetism as the dominant factor. Mg-rich compositions achieve high gravimetric densities, but strong magnetism destabilizes the system, requiring Y substitution to suppress magnetic moments. Replacing Co with Ni weakens magnetism: YNi3 is nonmagnetic, while CaNi3 and MgNi3 are weakly polarized, allowing thermodynamic stability across compositions. Notably, CaMg2Ni9 combines high theoretical capacity (3.32 wt%) with good reversibility. Mg-rich Ni-based alloys are predicted to offer negative formation energies with the highest gravimetric densities (up to 3.40 wt%). These results show that controlling magnetism via transition-metal substitution is key to overcoming the stability-capacity trade-off in AB3 hydrogen storage materials.

cond-mat.mtrl-sci↗

arXiv2025

Physically Interpretable Descriptors Drive the Materials Design of Metal Hydrides for Hydrogen Storage

Designing metal hydrides for hydrogen storage remains a longstanding challenge due to the vast compositional space and complex structure-property relationships. Herein, for the first time, we present physically interpretable models for predicting two key performance metrics, gravimetric hydrogen density $w$ and equilibrium pressure $P_{\rm eq,RT}$ at room temperature, based on a minimal set of chemically meaningful descriptors. Using a rigorously curated dataset of $5,089$ metal hydride compositions from our recently developed Digital Hydrogen Platform (\it{DigHyd}) based on large-scale data mining from available experimental literature of solid-state hydrogen storage materials, we systematically constructed over $1.6$ million candidate models using combinations of scalar transformations and nonlinear link functions. The final closed-form models, derived from $2$-$3$ descriptors each, achieve predictive accuracies on par with state-of-the-art machine learning methods, while maintaining full physical transparency. Strikingly, descriptor-based design maps generated from these models reveal a fundamental trade-off between $w$ and $P_{\rm eq,RT}$: saline-type hydrides, composed of light electropositive elements, offer high $w$ but low $P_{\rm eq,RT}$, whereas interstitial-type hydrides based on heavier electronegative transition metals show the opposite trend. Notably, Be-based systems, such as Be-Na alloys, emerge as rare candidates that simultaneously satisfy both performance metrics, attributed to the unique combination of light mass and high molar density for Be. Our models indicate that Be-based systems may offer renewed prospects for approaching these benchmarks. These results provide chemically intuitive guidelines for materials design and establish a scalable framework for the rational discovery of materials in complex chemical spaces.

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

douka: A universal platform of data assimilation for materials modeling

A large-scale, general-purpose data assimilation (DA) platform for materials modeling, douka, was developed and applied to nonlinear materials models. The platform demonstrated its effectiveness in estimating physical properties that cannot be directly obtained from observed data. DA was successfully performed using experimental images of oxygen evolution reaction at a water electrolysis electrode, enabling the estimation of oxygen gas injection velocity and bubble contact angle. Furthermore, large-scale ensemble DA was conducted on the supercomputer Fugaku, achieving state estimation with up to 8,192 ensemble members. The results confirmed that runtime scaling for the prediction step follows the weak scaling law, ensuring computational efficiency even with increased ensemble sizes. These findings highlight the potential of douka as a new approach for data-driven materials science, integrating experimental data with numerical simulation.

cond-mat.mtrl-sci↗

arXiv2024

Data-Assimilated Crystal Growth Simulation for Multiple Crystalline Phases

To determine crystal structures from an X-ray diffraction (XRD) pattern containing multiple unknown phases, a data-assimilated crystal growth (DACG) simulation method has been developed. The XRD penalty function selectively stabilizes the structures in the experimental data, promoting their grain growth during simulated annealing. Since the XRD pattern is calculated as the Fourier transform of the pair distribution function, the DACG simulation can be performed without prior determination of the lattice parameters. We applied it to C (graphite and diamond) and SiO$_2$ (low-quartz and low-cristobalite) systems, demonstrating that the DACG simulation successfully reproduced multiple crystal structures.

cond-mat.mtrl-sci↗

arXiv2023

Topological Data Analysis of Ion Migration Mechanism

Topological data analysis based on persistent homology has been applied to the molecular dynamics simulation for the fast ion-conducting phase ($α$-phase) of AgI, to show its effectiveness on the ion-migration mechanism analysis.Time-averaged persistence diagrams of $α$-AgI, which quantitatively records the shape and size of the ring structures in the given atomic configurations, clearly showed the emergence of the four-membered rings formed by two Ag and two I ions at high temperatures. They were identified as common structures during the Ag ion migration. The averaged potential energy change due to the deformation of four-membered ring during Ag migration agrees well with the activation energy calculated from the conductivity Arrhenius plot. The concerted motion of two Ag ions via the four-membered ring was also successfully extracted from molecular dynamics simulations by our approach, providing the new insight into the specific mechanism of the concerted motion.

cond-mat.mtrl-sci↗

arXiv2022

A Noise-Robust Data Assimilation Method for Crystal Structure Prediction Using Powder Diffraction Intensity

Crystal structure prediction for a given chemical composition has long been a challenge in condensed-matter science. We have recently shown that experimental powder X-ray diffraction (XRD) data are helpful in a crystal structure search using simulated annealing, even when they are insufficient for structure determination by themselves (N. Tsujimoto et al., Phys. Rev. Materials 2, 053801 (2018)). In the method, the XRD data are assimilated into the simulation by adding a penalty function to the physical potential energy, where we used a crystallinity-type penalty function defined by the difference between experimental and simulated diffraction angles. To improve the success rate and noise robustness, we introduce a correlation-coefficient-type penalty function adaptable to XRD data with significant experimental noise. We apply the new penalty function to SiO$_2$ coesite and $ε$-Zn(OH)$_2$ to determine its effectiveness in the data assimilation method.

cond-mat.mtrl-sci↗

arXiv2022

Accelerating Simulated Annealing of Glassy Materials with Data Assimilation

The ultra-long relaxation time of glass transition makes it difficult to construct atomic models of amorphous materials by conventional methods. We propose a novel method for building such atomic models using data assimilation method by simulated annealing with an accurately computed interatomic potential augmented by penalty from experimental data. The advantage of this method is that not only can it reproduce experimental data as the structure refinement methods like reverse Monte Carlo but also obtain the reasonable structure in terms of interatomic potential energy. In addition, thanks to the interatomic potential, we do not need high $Q$ range diffraction data, which is necessary to take into account the short-range order. Persistent homology analysis shows that the amorphous ice obtained by the new method is indeed more ordered at intermediate range.

cond-mat.mtrl-sci↗