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Researcher profile

Ryo Tamura

Ryo Tamura contributes to research discovery and scholarly infrastructure.

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cond-mat.stat-mechcond-mat.mtrl-sciphysics.comp-phcond-mat.dis-nncond-mat.mes-hallcond-mat.str-elMachine Learningquant-phArtificial Intelligencehep-thphysics.app-phRobotics

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Published work

26 published item(s)

preprint2026arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A machine learning-based classification approach for phase diagram prediction

Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus one of the central tasks in materials science. When exploring new materials for which the phase diagram is unknown, experimentalists often try to determine the key experiments that should be performed by referencing known phase diagrams of similar systems. To enhance this practical strategy, we attempted to estimate unknown phase diagrams based on known phase diagrams using a machine learning-based classification approach. As a proof of concept, we focused on predicting the number of coexisting phases across the 800 K isothermal section of each of the 10 ternaries of the Al-Cu-Mg-Si-Zn system from the other 9 sections. To increase the prediction accuracy, we introduced new descriptors generated from the thermodynamic properties of the elements and CALPHAD extrapolations from lower-order systems. Using the random forest method, the presence of single-, two-, and three-phase domains was predicted with an average accuracy of 84% across all 10 considered sections with a standard deviation of 11%. The proposed approach represents a promising tool for assisting the investigator in developing new materials and determining phase equilibria efficiently.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bayesian optimization package: PHYSBO

PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application in the basic sciences such as physics and materials science. Bayesian optimization is used to select an appropriate input for experiments/simulations from candidate inputs listed in advance in order to obtain better output values with the help of machine learning prediction. PHYSBO can be used to find better solutions for both single and multi-objective optimization problems. At each cycle in the Bayesian optimization, a single proposal or multiple proposals can be obtained for the next experiments/simulations. These proposals can be obtained interactively for use in experiments. PHYSBO is available at https://github.com/issp-center-dev/PHYSBO.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Black-box optimization for integer-variable problems using Ising machines and factorization machines

Black-box optimization has potential in numerous applications such as hyperparameter optimization in machine learning and optimization in design of experiments. Ising machines are useful for binary optimization problems because variables can be represented by a single binary variable of Ising machines. However, conventional approaches using an Ising machine cannot handle black-box optimization problems with non-binary values. To overcome this limitation, we propose an approach for integer-variable black-box optimization problems by using Ising/annealing machines and factorization machines in cooperation with three different integer-encoding methods. The performance of our approach is numerically evaluated with different encoding methods using a simple problem of calculating the energy of the hydrogen molecule in the most stable state. The proposed approach can calculate the energy using any of the integer-encoding methods. However, one-hot encoding is useful for problems with a small size.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Unsupervised learning-based structural analysis: Search for a characteristic low-dimensional space by local structures in atomistic simulations

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of datapoints for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Data-driven determination of the spin Hamiltonian parameters and their uncertainties: The case of the zigzag-chain compound KCu$_4$P$_3$O$_{12}$

We propose a data-driven technique to estimate the spin Hamiltonian, including uncertainty, from multiple physical quantities. Using our technique, an effective model of KCu$_4$P$_3$O$_{12}$ is determined from the experimentally observed magnetic susceptibility and magnetization curves with various temperatures under high magnetic fields. An effective model, which is the quantum Heisenberg model on a zigzag chain with eight spins having $J_1= -8.54 \pm 0.51 \{\rm meV}$, $J_2 = -2.67 \pm 1.13 \{\rm meV}$, $J_3 = -3.90 \pm 0.15 \{\rm meV}$, and $J_4 = 6.24 \pm 0.95 \{\rm meV}$, describes these measured results well. These uncertainties are successfully determined by the noise estimation. The relations among the estimated magnetic interactions or physical quantities are also discussed. The obtained effective model is useful to predict hard-to-measure properties such as spin gap, spin configuration at the ground state, magnetic specific heat, and magnetic entropy.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Expanding the horizon of automated metamaterials discovery via quantum annealing

Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Leveraging Legacy Data to Accelerate Materials Design via Preference Learning

Machine learning applications in materials science are often hampered by shortage of experimental data. Integration with legacy data from past experiments is a viable way to solve the problem, but complex calibration is often necessary to use the data obtained under different conditions. In this paper, we present a novel calibration-free strategy to enhance the performance of Bayesian optimization with preference learning. The entire learning process is solely based on pairwise comparison of quantities (i.e., higher or lower) in the same dataset, and experimental design can be done without comparing quantities in different datasets. We demonstrate that Bayesian optimization is significantly enhanced via addition of legacy data for organic molecules and inorganic solid-state materials.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

A generalized magnetic refrigeration scheme

We have investigated the magnetocaloric effects in antiferromagnets and compared them with those in ferromagnets using Monte Carlo simulations. In antiferromagnets, the magnetic entropy reaches a maximum value at a finite magnetic field when the temperature is fixed below the Néel temperature. Using the fact, we proposed a protocol for applying magnetic fields to achieve the maximum efficiency for magnetic refrigeration in antiferromagnets. In particular, we found that at low temperatures, antiferromagnets are more useful for magnetic refrigeration than ferromagnets.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Magnetic ordered structure dependence of magnetic refrigeration efficiency

We have investigated the relation between magnetic ordered structure and magnetic refrigeration efficiency in the Ising model on a simple cubic lattice using Monte Carlo simulations. The magnetic entropy behaviors indicate that the protocol, which was first proposed in [Appl. Phys. Lett. {\bf 104}, 052415 (2014).], can produce the maximum isothermal magnetic entropy change and the maximum adiabatic temperature change in antiferromagnets. Furthermore, the total amount of heat transfer under the proposed protocol reaches a maximum. The relation between measurable physical quantities and magnetic refrigeration efficiency is also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Interlayer-Interaction Dependence of Latent Heat in the Heisenberg Model on a Stacked Triangular Lattice with Competing Interactions

We study the phase transition behavior of a frustrated Heisenberg model on a stacked triangular lattice by Monte Carlo simulations. The model has three types of interactions: the ferromagnetic nearest-neighbor interaction $J_1$ and antiferromagnetic third nearest-neighbor interaction $J_3$ in each triangular layer and the ferromagnetic interlayer interaction $J_\perp$. Frustration comes from the intralayer interactions $J_1$ and $J_3$. We focus on the case that the order parameter space is SO(3)$\times C_3$. We find that the model exhibits a first-order phase transition with breaking of the SO(3) and $C_3$ symmetries at finite temperature. We also discover that the transition temperature increases but the latent heat decreases as $J_\perp/J_1$ increases, which is opposite to the behavior observed in typical unfrustrated three-dimensional systems.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Network-Growth Rule Dependence of Fractal Dimension of Percolation Cluster on Square Lattice

To investigate the network-growth rule dependence of certain geometric aspects of percolation clusters, we propose a generalized network-growth rule introducing a generalized parameter $q$ and we study the time evolution of the network. The rule we propose includes a rule in which elements are randomly connected step by step and the rule recently proposed by Achlioptas {\it et al.} [Science {\bf 323} (2009) 1453]. We consider the $q$-dependence of the dynamics of the number of elements in the largest cluster. As $q$ increases, the percolation step is delayed. Moreover, we also study the $q$-dependence of the roughness and the fractal dimension of the percolation cluster.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Phase Transitions with Discrete Symmetry Breaking in Antiferromagnetic Heisenberg Models on a Triangular Lattice

We study phase transition behavior of the Heisenberg model on a distorted triangular lattice with competing interactions. The ground-state phase diagram indicates that underlying symmetry can be changed by tuning parameters. We focus on two cases in which a phase transition with discrete symmetry breaking occurs. The first is that the order parameter space is SO(3)$\times C_3$. In this case, a first-order phase transition, with threefold symmetry breaking, occurs. The second has the order parameter space SO(3)$\times Z_2$. In this case, a second-order phase transition occurs with twofold symmetry breaking. To investigate finite-temperature properties of these phase transitions from a microscopic viewpoint, we introduce a method to make the connection between continuous frustrated spin systems and the Potts model with invisible states.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Second-order phase transition in the Heisenberg model on a triangular lattice with competing interactions

We discover an example where the dissociation of the Z2 vortices occurs at the second-order phase transition point. We investigate the nature of phase transition in a classical Heisenberg model on a distorted triangular lattice with competing interactions. The order parameter space of the model is SO(3)xZ2. The dissociation of the Z2 vortices which comes from SO(3) and a second-order phase transition with Z2 symmetry breaking occur at the same temperature. We also find that the second-order phase transition belongs to the universality class of the two-dimensional Ising model.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

A Method to Control Order of Phase Transition: Invisible States in Discrete Spin Models

It is an important topic to investigate nature of the phase transition in wide area of science such as statistical physics, materials science, and computational science. Recently it has been reported the efficiency of quantum adiabatic evolution/quantum annealing for systems which exhibit a phase transition, and we cannot obtain a good solution in such systems. Thus, to control the nature of the phase transition has been also attracted attention in quantum information science. In this paper we review nature of the phase transition and how to control the order of the phase transition. We take the Ising model, the standard Potts model, the Blume-Capel model, the Wajnflasz-Pick model, and the Potts model with invisible states for instance. Until now there is no general method to avoid the difficulty of annealing method in systems which exhibit a phase transition. It is a challenging problem to propose a method how to erase or how to control the nature of the phase transition in the target system.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Entanglement Spectra of the quantum hard-square model: Holographic minimal models

We study the entanglement properties of a quantum lattice-gas model for which we can find the exact ground state (of the Rokhsar-Kivelson type). The ground state can be expressed as a superposition of states, each of which is characterized by a particle configuration with nearest-neighbor exclusion. We show that the reduced density matrix of the model on a ladder is intimately related to the transfer matrix of the classical hard-square model. The entanglement spectra of the model on square and triangular ladders are critical when parameters are chosen so that the corresponding classical hard-square models are critical. A detailed analysis reveals that the critical theories for the entanglement Hamiltonians are $c<1$ minimal conformal field theories. We further show that the entanglement Hamiltonian for the triangular ladder is integrable despite the fact that the original quantum lattice-gas model is non-integrable.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Relation between dispersion lines and conductance of telescoped armchair double-wall nanotubes analyzed using perturbation formulas and first-principles calculations

The Landauer&#39;s formula conductance of the telescoped armchair nanotubes is calculated with the Hamiltonian defined by first-principles calculations (SIESTA code). Herein, partially extracting the inner tube from the outer tube is called &#39;telescoping&#39;. It shows a rapid oscillation superposed on a slow oscillation as a function of discrete overlap length $(L-1/2)a$ with an integer variable $L$ and the lattice constant $a$. Considering the interlayer Hamiltonian as a perturbation, we obtain the approximate formula of the amplitude of the slow oscillation as $|A|^2/(|A|^2+\varepsilon^2)$ where $A$ is the effective interlayer interaction and $\varepsilon$ is the band split without interlayer interaction. The approximate formula is related to the Thouless number of the dispersion lines.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Suppression of the anti-symmetry channel in the conductance of telescoped double-wall nanotubes

The conductance of telescoped double-wall nanotubes (TDWNTs) composed of two armchair nanotubes ($(n_O, n_O)$ and $(n_O-5, n_O-5)$ with $n_O \geq 10$) is calculated using the Landauer formula and a tight binding model. The results are in good agreement with the conductance calculated analytical by replacing each single-wall nanotube with a ladder, as expressed by $(2e^2/h)(T_+ + T_-)$, where $T_+$ and $T_-$ are the transmission rates of the symmetry and anti-symmetry channels, respectively. Perfect transmission in both channels is possible in this TDWNT when $n_O=10$, while $T_-$ is considerably small in the other TDWNTs. $T_-$ is particularly low when either $n_O$ or $n_O-5$ is a multiple of three. In this case, a three body effect of covalent-like interlayer bonds plays a crucial role in determining the finite $T_-$. When $n_O$ is a multiple of five, the five-fold symmetry increases $T_-$, although this effect diminishes with increasing $n_O$.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Double-$\it q$ Order in a Frustrated Random Spin System

We use the three-dimensional Heisenberg model with site randomness as an effective model of the compound Sr(Fe$_{1-x}$Mn$_x$)O$_2$. The model consists of two types of ions that correspond to Fe and Mn ions. The nearest-neighbor interactions in the ab-plane are antiferromagnetic. The nearest-neighbor interactions along the c-axis between Fe ions are assumed to be antiferromagnetic, whereas other interactions are assumed to be ferromagnetic. From Monte Carlo simulations, we confirm the existence of the double-$\boldsymbol{q}$ ordered phase characterized by two wave numbers, $(πππ)$ and $(π\pi0)$. We also identify the spin ordering pattern in the double-$\boldsymbol{q}$ ordered phase.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

First-Order Phase Transition with Breaking of Lattice Rotation Symmetry in Continuous-Spin Model on Triangular Lattice

Using a Monte Carlo method, we study the finite-temperature phase transition in the two-dimensional classical Heisenberg model on a triangular lattice with or without easy-plane anisotropy. The model takes account of competing interactions: a ferromagnetic nearest-neighbor interaction $J_1$ and an antiferromagnetic third nearest-neighbor interaction $J_3$. As a result, the ground state is a spiral spin configuration for $-4 < J_1/J_3 < 0$. In this structure, global spin rotation cannot compensate for the effect of 120-degree lattice rotation, in contrast to the conventional 120-degree structure of the nearest-neighbor interaction model. We find that this model exhibits a first-order phase transition with breaking of the lattice rotation symmetry at a finite temperature. The transition is characterized as a $Z_2$ vortex dissociation in the isotropic case, whereas it can be viewed as a $Z$ vortex dissociation in the anisotropic case. Remarkably, the latter is continuously connected to the former as the magnitude of anisotropy decreases, in contrast to the recent work by Misawa and Motome [J. Phys. Soc. Jpn. \textbf{79} (2010) 073001.] in which both the transitions were found to be continuous.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Hybrid Quantum Annealing for Clustering Problems

We develop a hybrid type of quantum annealing in which we control temperature and quantum field simultaneously. We study the efficiency of proposed quantum annealing and find a good schedule of changing thermal fluctuation and quantum fluctuation. In this paper, we focus on clustering problems which are important topics in information science and engineering. We obtain the better solution of the clustering problem than the standard simulated annealing by proposed quantum annealing.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Phase Transition of Generalized Ferromagnetic Potts Model - Effect of Invisible States -

We investigate the nature of the phase transition of the ferromagnetic Potts model with invisible states. The ferromagnetic Potts model with invisible states can be regarded as straightforward extension of the standard ferromagnetic Potts model. The invisible states contribute the entropy, however they do not affect the internal energy. They also do not change the symmetry which breaks at the transition temperature. The invisible states stimulate a first-order phase transition. We confirm that the first-order phase transition with spontaneous $q$-fold symmetry breaking for $q=2,3$, and 4 takes place even on two-dimensional lattice by Monte Carlo simulation. We also find that the transition temperature decreases and the latent heat increases as the number of invisible states increases.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Random Fan-Out State Induced by Site-Random Interlayer Couplings

We study the low-temperature properties of a classical Heisenberg model with site-random interlayer couplings on the cubic lattice. This model is introduced as a simplified effective model of Sr(Fe$_{1-x}$Mn$_{x}$)O$_2$, which was recently synthesized. In this material, when $x=0.3$, $(πππ)$ and $(π\pi0)$ mixed ordering is observed by neutron diffraction measurements. By Monte Carlo simulations, we find an exotic bulk spin structure that explains the experimentally obtained results. We name this spin structure the &#34;random fan-out state&#34;. The mean-field calculations provide an intuitive understanding of this phase being induced by the site-random interlayer couplings. Since Rietveld analysis assuming the random fan-out state agrees well with the neutron diffraction pattern of Sr(Fe$_{0.7}$Mn$_{0.3}$)O$_2$, we conclude that the random fan-out state is reasonable for the spin-ordering pattern of Sr(Fe$_{0.7}$Mn$_{0.3}$)O$_2$ at the low-temperature phase.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Conductance of telescoped double-walled nanotubes from perturbation calculations

In a telescoped double-walled nanotube (TDWNT), with the inner tube partially extracted from the outer tube, the total current is forced to flow between the layers. Considering the interlayer Hamiltonian as a perturbation, we can obtain an analytic formula for the interlayer conductance. The accuracy of the perturbation formula is systematically improved by including higher-order terms. The interlayer interaction effective in the perturbation formula is the product of the interlayer Hamiltonian and the wave function. It clarifies the effects of the spatial range of the interlayer Hamiltonian and the band energy shift.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Dynamical properties of Potts model with invisible states

We study dynamic behavior of Potts model with invisible states near the first-order phase transition temperature. We focus on melting process starting from the perfect ordered state. This model is regarded as a standard model to analyze nature of phase transition. We can control the energy barrier between the ordered state and paramagnetic state without changing the symmetry which breaks at the transition point. We calculate time-dependency of the order parameter, density of invisible state, and internal energy. They show two-step relaxation behavior. We also consider the relation between the characteristic melting time and characteristic scale of the energy barrier by changing the number of invisible states. We find that characteristic melting time increases as the energy barrier enlarges in this model. Thus, this model is regarded as a fundamental model to analyze dynamic behavior near the first-order phase transition point.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Phase Transition in Potts Model with Invisible States

We study phase transition in the ferromagnetic Potts model with invisible states that are added as redundant states by mean-field calculation and Monte Carlo simulation. Invisible states affect the entropy and the free energy, although they do not contribute to the internal energy. The internal energy and the number of degenerated ground states do not change, if invisible states are introduced into the standard Potts model. A second-order phase transition takes place at finite temperature in the standard $q$-state ferromagnetic Potts model on two-dimensional lattice for $q=2,3$, and 4. However, our present model on two-dimensional lattice undergoes a first-order phase transition with spontaneous $q$-fold symmetry breaking ($q=2,3$, and 4) due to entropy effect of invisible states. We believe that our present model is a fundamental model for analysis of a first-order phase transition with spontaneous discrete symmetry breaking.

0 citations0 reviewsNo code linkedOpen access