arXiv subjects

Toyoto Sato

Publications and source records attributed to Toyoto Sato.

5 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 ($\left\langleκ\right\rangle$), with an optimal regime of $r_M \sim 1.47 Å$ and relatively low $\left\langleκ\right\rangle$. 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 ($\left\langle ν\right\rangle$), 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 ($ΔH$) and entropy ($ΔS$) 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 $ΔH$ and $ΔS$ 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

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

"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↗