arXiv subjects

Shin-ichi Orimo

Publications and source records attributed to Shin-ichi Orimo.

16 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

Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models

Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slow experimental iteration. Unlike catalysis, where surface reactivity dominates, SEs require simultaneous optimization of bulk ion transport, defect chemistry, mechanical integrity, and interfacial stability. Here, we outline a framework for autonomous SE discovery enabled by large artificial intelligence (AI) models, including machine learning interatomic potentials (MLIPs) and large language models (LLMs). We discuss the evolution from static materials databases to dynamic, self-updating knowledge systems, the role of MLIPs in bridging density functional theory (DFT) and long-timescale ion migration, and the emergence of LLMs as engines for literature mining, hypothesis generation, and scientific reasoning. We further describe a closed-loop architecture integrating AI-driven candidate design, multiscale simulation, uncertainty-aware selection, and experimental validation. Such systems shift SE research from intuition-guided exploration to data-informed, self-improving cycles. We conclude by highlighting challenges in data standardization, interfacial complexity, and reproducibility, and we propose design principles for building autonomous laboratories for solid-state battery materials.

cond-mat.mtrl-sci↗

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↗

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↗

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↗

arXiv2023

Colossal reversible barocaloric effects in a plastic crystal mediated by lattice vibrations and ion diffusion

Solid-state methods for cooling and heating promise a more sustainable alternative to current compression cycles of greenhouse gases and inefficient fuel-burning heaters. Barocaloric effects (BCE) driven by hydrostatic pressure ($p$) are especially encouraging in terms of large adiabatic temperature changes ($|ΔT| \sim 10$ K) and colossal isothermal entropy changes ($|ΔS| \sim 100$ JK$^{-1}$kg$^{-1}$). However, BCE typically require large pressure shifts due to irreversibility issues, and sizeable $|ΔT|$ and $|ΔS|$ seldom are realized in a same material. Here, we demonstrate the existence of colossal and reversible BCE in LiCB$_{11}$H$_{12}$, a well-known solid electrolyte, near its order-disorder phase transition at $\approx 380$ K. Specifically, for $Δp \approx 0.23$ $(0.10)$ GPa we measured $|ΔS_{\rm rev}| = 280$ $(200)$ JK$^{-1}$kg$^{-1}$ and $|ΔT_{\rm rev}| = 32$ $(10)$ K, which individually rival with state-of-the-art barocaloric shifts obtained under similar pressure conditions. Furthermore, over a wide temperature range, pressure shifts of the order of $0.1$ GPa yield huge reversible barocaloric strengths of $\approx 2$ JK$^{-1}$kg$^{-1}$MPa$^{-1}$. Molecular dynamics simulations were carried out to quantify the role of lattice vibrations, molecular reorientations and ion diffusion on the disclosed colossal BCE. Interestingly, lattice vibrations were found to contribute the most to $|ΔS|$ while the diffusion of lithium ions, despite adding up only slightly to the accompanying entropy change, was crucial in enabling the molecular order-disorder phase transition. Our work expands the knowledge on plastic crystals and should motivate the investigation of BCE in a variety of solid electrolytes displaying ion diffusion and concomitant molecular orientational disorder.

cond-mat.mtrl-sci↗

arXiv2020

Colossal barocaloric effects in the complex hydride Li$_{2}$B$_{12}$H$_{12}$

Traditional refrigeration technologies based on compression cycles of greenhouse gases pose serious threats to the environment and cannot be downscaled to electronic device dimensions. Solid-state cooling exploits the thermal response of caloric materials to external fields and represents a promising alternative to current refrigeration methods. However, most of the caloric materials known to date present relatively small adiabatic temperature changes ($|ΔT| \sim 1$ K) and/or limiting irreversibility issues resulting from significant phase-transition hysteresis. Here, we predict the existence of colossal barocaloric effects (isothermal entropy changes of $|ΔS| \sim 100$ JK$^{-1}$kg$^{-1}$) in the energy material Li$_{2}$B$_{12}$H$_{12}$ by means of molecular dynamics simulations. Specifically, we estimate $|ΔS| = 387$ JK$^{-1}$kg$^{-1}$ and $|ΔT| = 26$ K for an applied pressure of $P = 0.4$ GPa at $T = 475$ K. The disclosed colossal barocaloric effects are originated by an order-disorder phase transformation that exhibits a fair degree of reversibility and involves coexisting Li$^{+}$ diffusion and (BH)$_{12}^{-2}$ reorientational motion at high temperatures.

cond-mat.mtrl-sci↗

arXiv2020

Pseudo-ternary LiBH4-LiCl-P2S5 system as structurally disordered bulk electrolyte for all-solid-state lithium batteries

The properties of the mixed system LiBH4 LiCl P2S5 are studied with respect to all-solid-state batteries. The studied material undergoes an amorphization upon heating above 601C, accompanied with increased Li+ conductivity beneficial for battery electrolyte applications. The measured ionic conductivity is 10-3 Scm-1 at room temperature with an activation energy of 0.40(2) eV after amorphization. Structural analysis and characterization of the material suggest that BH4 groups and PS4 may belong to the same molecular structure, where Cl ions interplay to accommodate the structural unit. Thanks to its conductivity, ductility and electrochemical stability (up to 5 V, Au vs. Li+/Li), this new electrolyte is successfully tested in battery cells operated with a cathode material (layered TiS2, theo. capacity 239 mAh g-1) and Li anode resulting in 93% capacity retention (10 cycles) and notable cycling stability under the current density 12 mA g-1 (0.05C-rate) at 501C. Further advanced characterisation by means of operando synchrotron X-ray diffraction in transmission mode contributes explicitly to a better understanding of the (de)lithiation processes of solid-state battery electrodes operated at moderate temperatures.

cond-mat.mtrl-sci↗

arXiv2020

Materials for hydrogen-based energy storage: Past, recent progress and future outlook

Magnesium hydride owns the largest share of publications on solid materials for hydrogen storage. The Magnesium group of international experts contributing to IEA Task 32 Hydrogen Based Energy Storage recently published two review papers presenting the activities of the group focused on magnesium hydride based materials and on Mg based compounds for hydrogen and energy storage. This review article not only overviews the latest activities on both fundamental aspects of Mg-based hydrides and their applications, but also presents a historic overview on the topic and outlines projected future developments. Particular attention is paid to the theoretical and experimental studies of Mg-H system at extreme pressures, kinetics and thermodynamics of the systems based on MgH2,nanostructuring, new Mg-based compounds and novel composites, and catalysis in the Mg based H storage systems. Finally, thermal energy storage and upscaled H storage systems accommodating MgH2 are presented.

cond-mat.mtrl-sci↗

arXiv2006

First-principles study on thermodynamical stability of metal borohydrides: Aluminum borohydride Al(BH4)3

The thermodynamical stability of ${\rm Al(BH_4)_3}$ has been investigated using first-principles calculations based on density functional theory. The heats of formation are obtained to be -132 and $-131 {\rm kJ/mol}$ without the zero-point energy corrections for $α$- and $β$-${\rm Al(BH_4)_3}$, respectively, which are made up of discrete molecular ${\rm Al(BH_4)_3}$ units. It is predicted correctly that the $α$ phase is more stable than the $β$ phase. The energy difference between the solid phases and the isolated molecule is only about 10 kJ/mol. An analysis of the electronic structure also suggests the weak interaction between ${\rm Al(BH_4)_3}$ molecules in the solid phases. It is confirmed that ${\rm Al(BH_4)_3}$ obeys the linear relationship between the heat of formation and the Pauling electronegativity of the cation, which has been proposed in our previous study [Nakamori {\it et al.}, Phys. Rev. B {\bf 74}, 045126 (2006)].

cond-mat.mtrl-sci↗