arXiv subjects

Ryo Tamura

Publications and source records attributed to Ryo Tamura.

At least 19 recordsLinked to original sources

arXiv2026

Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes

Maximizing ionic conductivity remains a fundamental challenge in the atomistic design of solid electrolytes. To this end, we present a framework that combines kinetic Monte Carlo (KMC) and factorization machine with quadratic-optimization annealing (FMQA), an Ising-machine-based black-box optimization algorithm for large-scale combinatorial optimization. KMC evaluates the ionic conductivity for a given dopant configuration, whereas FMQA iteratively learns a surrogate model from a small configuration-conductivity dataset and proposes configurations expected to maximize conductivity. To address the severe combinatorial explosion in large KMC simulation cells, we partition the configuration space for parallel optimization. As a proof of concept, we apply this KMC-FMQA framework to bulk 8 mol % yttria-stabilized zirconia, identifying a dopant configuration with an order-of-magnitude higher conductivity than that of random configurations and the experimentally reported conductivity. Combined with experiments, this framework will enable the determination of microscopic structures from measured conductivity, providing insight into the underlying transport mechanisms.

cond-mat.mtrl-sci↗

arXiv2026

NIMO: A Software Platform for Closed-Loop Materials Exploration with Diverse AI Algorithms

Self-driving laboratories (SDLs), where artificial intelligence proposes subsequent experiments and robotic systems execute them, are rapidly becoming the vanguard of materials discovery. A critical bottleneck, however, lies in seamlessly bridging diverse AI algorithms tailored for specific exploration goals with the heterogeneous robotic hardware found across different laboratories. Here, we present NIMO, an open-source software platform designed to dissolve this barrier through three core paradigms: a modular AI-robot decoupling mediated via simple CSV file exchange, a discrete candidate-pool architecture that seamlessly absorbs domain knowledge, and a unified Python interface pre-loaded with twelve distinct AI algorithms. In this Perspective, we review the operational principles of each algorithm alongside six diverse SDL implementations driven by NIMO, covering electrolyte discovery, organic synthesis, thin-film exploration, fuel-cell process informatics, coffee-ring phase exploration, and legacy liquid-handling automation. One of these also demonstrates NIMO's seamless interoperability with the IvoryOS orchestration framework. To democratize autonomous science, we also introduce a no-code desktop application that enables intuitive, human-in-the-loop exploration for non-programmers. NIMO is freely available at https://github.com/NIMS-DA/nimo, offering a versatile, plug-and-play foundation to accelerate autonomous materials exploration across diverse experimental landscapes.

cond-mat.mtrl-sci↗

arXiv2026

ProvMind: Provenance-grounded reasoning for materials synthesis

Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten synthesis procedures into text or ordered steps. We introduce MatProcBench, a provenance-grounded benchmark constructed from literature-mined MatPROV graphs, to evaluate seven process-reasoning tasks spanning route continuity, step-level variable inference and global causal consistency under both same-split and shift-aware evaluation, including a strict dual-OOD split that combines temporal and material-class shift. We further introduce ProvMind, a process-memory reasoning framework that retrieves analogous training processes, converts them into provenance-aware option-level compatibility scores, and uses a language model for constrained final decision making. ProvMind achieves 52.84\% accuracy on the dual-OOD split, outperforming prompting, retrieval-augmented and supervised fine-tuning baselines.

cs.AI↗

arXiv2026

Revisiting spin Hamiltonian parameters in a Kitaev material via Bayesian optimization of magnetization curves

Determining the spin Hamiltonian of a magnetic compound is crucial for understanding its magnetic properties. A standard approach is to derive model parameters from $ab$ $initio$ calculations based on the crystal structure. However, the resulting Hamiltonian can depend sensitively on methodological details of the $ab$ $initio$ procedure. This issue is particularly evident in $\alpha$-RuCl$_3$, a candidate Kitaev material. Here, we present an alternative, data-driven approach to determine the spin Hamiltonian parameters of $\alpha$-RuCl$_3$ by Bayesian optimization of experimental magnetization curves along the $b$- and $c$-axis directions. We optimize five parameters, namely the Kitaev interaction $K$, off-diagonal interactions $\Gamma$ and $\Gamma'$, the Heisenberg interaction $J$, and the $c$-axis $g$-factor $g_c$. The parameter set that minimizes the cost function is $(K,\Gamma,\Gamma',J,g_c)=(-6.0,\,7.5,\,-0.3,\,-1.75,\,2.3)$, where the exchange couplings are in meV. We find that the cost function is insensitive to the absolute value of the Kitaev coupling $K$. Thus, the magnetization data alone do not determine its energy scale. The cost function also depends only weakly on $\Gamma'$ and $J$, while the optimization favors a large positive $\Gamma$. By computing the static spin structure factor, magnetic susceptibility, and specific heat, we show that these quantities favor the large-$\Gamma$ scenario over the small-$g_c$ scenario and that the parameter set that minimizes the cost function yields good agreement with experiment. The combination of Bayesian optimization and accurate low-energy solvers provides an effective approach for determining parameters of spin Hamiltonians. This methodology opens a systematic route to determining spin Hamiltonians in quantum magnets from experimental data.

cond-mat.str-el↗

arXiv2026

NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol

Self-driving laboratories (SDLs) have attracted increasing attention as a means of accelerating scientific discovery; however, developing SDL software remains technically demanding. To improve accessibility, orchestration software frameworks have been proposed to coordinate SDL components. Nevertheless, existing frameworks are primarily designed for human interaction and do not provide standardized interfaces suitable for AI agents. In this work, we propose an SDL software architecture based on the Model Context Protocol (MCP), in which all SDL functionalities are exposed through MCP servers. Following this design principle, we introduce an MCP-based SDL orchestrator, named NIMO Controller. It provides a visual programming interface automatically generated through MCP-based tool discovery, allowing human users to design experimental workflows without writing code. The same MCP backend can also be accessed by AI agents, providing a unified interface for both human users and AI agents. We demonstrate the proposed system through a case study on a color-matching SDL. The results validate the usability of the proposed MCP-based SDL architecture.

cs.AI↗

arXiv2026

LLM-guided phase diagram construction through high-throughput experimentation

Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate whether large language models (LLMs) can guide experimental planning for phase diagram construction. In our framework, a general-purpose LLM serves as the experimental planner, suggesting compositions for measurement at each cycle in a closed loop with high-throughput synthesis and X-ray diffraction phase identification. Using this framework, we experimentally constructed the ternary phase diagram of the Co-Al-Ge system at 900 degree C through iterative synthesis and characterization. We compared two strategies that differ in how the initial compositions are selected: one uses predictions from a domain-specific LLM trained on phase diagram data (aLLoyM), while the other relies solely on the general-purpose LLM. The two strategies exhibited complementary strengths. aLLoyM directed the initial measurements toward compositionally complex regions in the interior of the ternary diagram, enabling the earliest discovery of all three novel phases that form only in the ternary system. In contrast, the general-purpose LLM adopted a textbook-like approach which efficiently identified a larger number of phases in fewer cycles. In addition, a simulated benchmark comparing the LLM against conventional machine learning confirmed that the LLM achieves more efficient exploration. The results demonstrate that LLMs have high potential as experimental planners for phase diagram construction.

cond-mat.mtrl-sci↗

arXiv2026

Strain Correlated Linearly Polarized Photoluminescence in WS2/WSe2 Moir\'e Superlattices

Reliable optical control of valley degrees of freedom in moir\'e excitons requires that the emitted polarization faithfully reflect the underlying valley state. Here, we show that linearly polarized photoluminescence from WSe2/WS2 moir\'e excitons is largely insensitive to the excitation polarization and therefore does not arise from valley coherence. Automated polarization-resolved photoluminescence and Raman mapping at cryogenic temperature reveals that the degree of linear polarization correlates strongly with local Raman shifts and moir\'e-exciton observables, identifying strain as the dominant experimental correlate. Linear-regression analysis further shows that strain-related descriptors provide the best prediction of the observed polarization. Guided by theory, we attribute this behavior to strain-amplified breaking of C3 symmetry in the moir\'e potential: weak uniaxial strain produces only partial cancellation of locally elliptical emission, yielding a finite far-field degree of linear polarization. These results establish strain as a key control parameter for reliable optical readout in TMD moir\'e superlattices.

cond-mat.mtrl-sci↗

arXiv2026

Update of PHYSBO: Improving Usability and Portability of Bayesian Optimization for Physics and Materials Research

Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While many BO frameworks focus on algorithmic efficiency, practical usability and portability are equally critical for sustained use in real research environments. PHYSBO is a Bayesian optimization library designed to address these needs by enabling optimization over user-defined candidate pools and by supporting domain-specific problem settings. This paper presents the major updates introduced in PHYSBO versions 2 and 3, with a focus on improvements in usability, portability, and practical deployment rather than on new optimization algorithms. In PHYSBO version 2, the software license was changed from GPL to MPL to improve compatibility with a wider range of research and software ecosystems. Building on this revision, PHYSBO version 3 introduces a set of implementation-oriented updates aimed at improving usability and portability, without modifying the core optimization algorithms. These updates include improvements in computational performance and scalability, extended support for multi-objective optimization, the introduction of range-based policies for continuous-variable optimization, the removal of environment-dependent components such as tightly coupled Cython modules, and compatibility with NumPy 2. These improvements reduce the technical and organizational burden on users, enabling PHYSBO to be deployed across diverse computing environments and research workflows. By emphasizing portability and ease of integration while maintaining sufficient performance, PHYSBO version 3 is positioned as a sustainable research infrastructure for Bayesian optimization in physics and materials science.

physics.comp-ph↗

arXiv2026

Effects of shear displacement on the conductance of monolayer/gapped bilayer/monolayer graphene junctions: Implications for ac-dc conversion

Analytical treatments of tunneling in bilayer graphene have typically relied on minimal models including only the vertical interlayer hopping $\gamma_1$ and have been restricted to the weak interlayer-bias regime ($2\varepsilon \ll \gamma_1$). Consequently, they cannot adequately describe lattice deformations or strong electric-field effects. In this work, we present an analytical theory of evanescent states in electrically gapped bilayer graphene that overcomes both limitations. Our approach explicitly incorporates the skew interlayer hoppings $\gamma_3$ and $\gamma_4$ and remains valid even when the interlayer bias $2\varepsilon$ is comparable to $\gamma_1$. Focusing on low-energy electronic states near the charge neutrality point, we analytically derive the complex longitudinal wave numbers, the gap width, and the sublattice pseudospin within the electric-field-induced gap. We then systematically analyze the dependence of these quantities on the interlayer shear displacement $\vec{\delta}=(\delta_x,\delta_y)$, and find that skew interlayer hoppings, in particular $\gamma_3$, play an essential role. For transport along the zigzag ($x$) direction, the longitudinal wave vector becomes complex, whereas the transverse wave vector remains real. For a monolayer/bilayer/monolayer junction with transport along the zigzag direction, we find that $\delta_y$ has a significantly stronger impact on the conductance than $\delta_x$. Furthermore, we identify a shear-induced phase proportional to $\delta_y$ that appears universally in the analytical expressions for the gap width, the sublattice pseudospin, and the decay length. These results establish a unified framework for shear- and bias-controlled evanescent tunneling in bilayer graphene and suggest broader relevance to nonequilibrium transport phenomena in layered materials.

cond-mat.mes-hall↗

arXiv2025

aLLoyM: A large language model for alloy phase diagram prediction

Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifically trained on alloy compositions, temperatures, and their corresponding phase information. To develop aLLoyM, we curated question-and-answer (Q&A) pairs for binary and ternary phase diagrams using the open-source Computational Phase Diagram Database (CPDDB) and assessments based on CALPHAD (CALculation of PHAse Diagrams). We fine-tuned Mistral, an open-source pre-trained LLM, for two distinct Q&A formats: multiple-choice and short-answer. Benchmark evaluations demonstrate that fine-tuning substantially enhances performance on multiple-choice phase diagram questions. Moreover, the short-answer model of aLLoyM exhibits the ability to generate novel phase diagrams from its components alone, underscoring its potential to accelerate the discovery of previously unexplored materials systems. To promote further research and adoption, we have publicly released the short-answer fine-tuned version of aLLoyM, along with the complete benchmarking Q&A dataset, on Hugging Face.

cond-mat.mtrl-sci↗

arXiv2025

Active Learning for Predicting the Enthalpy of Mixing inBinary Liquids Based on Ab Initio Molecular Dynamics

The enthalpy of mixing in the liquid phase is an important property for predicting phase formation in alloys. It can be estimated in a large compositional space from pair wise interactions between elements, for which machine learning has recently provided the most accurate predictions. Further improvements requires acquiring high quality data in liquids where models are poorly constrained. In this study, we propose an active learning approach to identify in which liquids additional data are most needed to improve an initial dataset that covers over 400 binary liquids. We identify a critical need for new data on liquids containing refractory elements, which we address by performing ab initio molecular dynamics simulations for 29 equimolar alloys of Ir, Os, Re and W. This enables more accurate predictions of the enthalpy of mixing, and we discuss the trends obtained for refractory elements of period 6. We use clustering analysis to interpret the results of active learning and to explore how our features can be linked to Miedema's semi empirical theory.

cond-mat.mtrl-sci↗

arXiv2025

Black-box optimization using factorization and Ising machines

Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In this review, a factorization machine with quantum annealing or with quadratic-optimization annealing (FMQA) algorithm to realize fast computations of BBO using Ising machines (IMs) is discussed. The FMQA algorithm uses a factorization machine (FM) as a surrogate model for BBO. The FM model can be directly transformed into a quadratic unconstrained binary optimization model that can be solved using IMs. This makes it possible to optimize the acquisition function in BBO, which is a difficult task using conventional methods without IMs. Consequently, it has the advantage of handling large BBO problems. To be able to perform BBO with the FMQA algorithm immediately, we introduce the FMQA algorithm along with Python packages to run it. In addition, we review examples of applications of the FMQA algorithm in various fields, including physics, chemistry, materials science, and social sciences. These successful examples include binary and integer optimization problems, as well as more general optimization problems involving graphs, networks, and strings, using a binary variational autoencoder. We believe that BBO using the FMQA algorithm will become a key technology in IMs including quantum annealers.

cond-mat.stat-mech↗

arXiv2025

CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines

Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still ineffective due to symmetry agnostic encoding of atomic coordinates. We introduce CRYSIM, an algorithm that encodes the space group, the Wyckoff positions combination, and coordinates of independent atomic sites as separate variables. This encoding reduces the search space substantially by exploiting the symmetry in space groups. When CRYSIM is interfaced to Fixstars Amplify, a GPU-based Ising machine, its prediction performance was competitive with CALYPSO and Bayesian optimization for crystals containing more than 150 atoms in a unit cell. Although it is not realistic to interface CRYSIM to current small-scale quantum devices, it has the potential to become the standard CSP algorithm in the coming quantum age.

cond-mat.mtrl-sci↗

arXiv2025

Exploring utilization of generative AI for research and education in data-driven materials science

Generative AI has recently had a profound impact on various fields, including daily life, research, and education. To explore its efficient utilization in data-driven materials science, we organized a hackathon -- AIMHack2024 -- in July 2024. In this hackathon, researchers from fields such as materials science, information science, bioinformatics, and condensed matter physics worked together to explore how generative AI can facilitate research and education. Based on the results of the hackathon, this paper presents topics related to (1) conducting AI-assisted software trials, (2) building AI tutors for software, and (3) developing GUI applications for software. While generative AI continues to evolve rapidly, this paper provides an early record of its application in data-driven materials science and highlights strategies for integrating AI into research and education.

cs.CY↗

arXiv2025

Energy symmetry and interlayer wave function ratio of tunneling electrons in partially overlapped graphene

While the exponential decay of tunneling probability with barrier thickness is well known, the accompanying oscillations with thickness have been comparatively less explored. Using a tight binding model, we investigate an AB-stacked bilayer graphene region acting as an energy barrier between two monolayer graphene leads, under a vertical electric field. We discuss the case where the energy gap induced by the vertical electric field is comparable to the interlayer transfer integral. In the up (down) junction, the left and right monolayer leads are connected to different layers (a common layer) of the central bilayer, while the remaining, unconnected layers form armchair-type open edges. We reveal a characteristic relation between the tunneling probability and the wave function structure. Among the valley-resolved transmission probabilities, only the valley-reversed transmission in the up junction exhibits even symmetry with respect to energy $E$. This result is counterintuitive. The interlayer wave function ratio $\beta$ is asymmetric in $E$, i.e., $\beta(-E) \neq \beta(E)$, and electrons cannot bypass the interlayer path in the up junction, whereas they can in the down junction. We attribute this unexpected symmetry to a self-cancellation effect of $\beta$, which arises from chiral and rotational symmetry operations combined with the conservation of probability. Our results demonstrate that the energy dependence of conductance in double junction structures serves as evidence of this effect.

cond-mat.mes-hall↗

arXiv2024

Data-driven study of the enthalpy of mixing in the liquid phase

The enthalpy of mixing in the liquid phase is a thermodynamic property reflecting interactions between elements that is key to predict phase transformations. Widely used models exist to predict it, but they have never been systematically evaluated. To address this, we collect a large amount of enthalpy of mixing data in binary liquids from a review of about 1000 thermodynamic evaluations. This allows us to clarify the prediction accuracy of Miedema's model which is state-of-the-art. We show that more accurate predictions can be obtained from a machine learning model based on LightGBM, and we provide them in 2415 binary systems. The data we collect also allows us to evaluate another empirical model to predict the excess heat capacity that we apply to 2211 binary liquids. We then extend the data collection to ternary metallic liquids and find that, when mixing is exothermic, extrapolations from the binary systems by Muggianu's model systematically lead to slight overestimations of roughly 10% close to the equimolar composition. Therefore, our LightGBM model can provide reasonable estimates for ternary alloys and, by extension, for multicomponent alloys. Our findings extracted from rich datasets can be used to feed thermodynamic, empirical and machine learning models for material development.

cond-mat.mtrl-sci↗

arXiv2023

Tunnel Valley Current Filter in the Partially Overlapped Graphene under the Vertical Electric Field

The tunnel current (TC) and valley current (VC) are crucial in realizing high-speed and energy-saving in next-generation devices. This paper presents the TC and VC link in the partially overlapped graphene. Under the vertical electric field, the two graphene layers have the opposite AB sublattice symmetry, followed by a block on the intravalley transmission. In the allowed intervalley transmission, the difference in the phase of the decay factor prefers only one of the valleys in the output according to the overlapped length. These results suggest that the band gap with no edge state is a new platform of valleytronics.

cond-mat.mes-hall↗

arXiv2023

Interlayer Conductance in the Armchair Nanotube -- Zigzag Graphene Ribbon Parallel Contact: Theoretical Proposal of Detection of Wavefunction Growing from the Edge to the Center in the Graphene Ribbon

Sublattices A and B are opposite in the decay direction of the edge state of the zigzag graphene ribbon (ZGR). Detecting exponential growth from the zigzag edges to the ZGR center remains challenging. The tight-binding model calculations in this letter reveal that interlayer conductance manifests this growth in parallel contact with the armchair nanotube. The transfer integrals of oblique interlayer bonds are comparable to those of vertical interlayer bonds. However, the phase of the ZGR wave function strongly suppresses the contribution of oblique bonds, allowing the selective detection of the growing component.

cond-mat.mes-hall↗