arXiv subjects

Junichiro Shiomi

Publications and source records attributed to Junichiro Shiomi.

At least 19 recordsLinked to original sources

arXiv2026

Janus-induced atomic reconstruction amplifies twist-angle modulation of interlayer thermal transport in moiré bilayers

In two-dimensional moiré bilayers, atomic reconstruction, the spontaneous structural relaxation toward energy-minimizing stacking registries in the near-commensurate regime, can strongly modify local stacking and interlayer coupling, providing a possibility to significantly control phonon-mediated properties. Here we show that the twist-angle dependence of interlayer thermal conductance can be modified by introducing Janus-induced mirror-symmetry breaking into bilayer MoS2. The intrinsic out-of-plane dipole in MoSSe/MoS2 bilayers leads to frictionless interface, and reduces the lattice deformation energy, thereby promoting atomic-reconstruction into locally distorted aperiodic moiré patterns. These features weaken interlayer coupling and suppress phonon transmission across the interface, leading to an anomalously strong twist-angle dependence of thermal conductance, with a pronounced minimum at small twist angles and a reduction rate nearly one order of magnitude larger than that of twisted bilayer MoS2. Our results demonstrate that interlayer thermal transport is modified by atomic reconstruction, highlighting Janus-induced mirror-symmetry breaking as an effective way to promoting phonon engineering in two-dimensional moiré structures.

cond-mat.mes-hall↗

arXiv2026

Observation of coherently modulated phonon band and lifetime in superlattice

Similar to the behavior of elementary particles, such as photons and electrons, the interference of phonon waves in artificial periodic nanostructures coherently modulates phonon band structures, serving as the foundation for phonon band engineering. However, direct observation of such coherently modulated phonon band structures remains challenging despite substantial insights from existing literature. Here, utilizing high-resolution inelastic X-ray scattering, we observed coherently modulated phonon band structures with phononic band gaps in a short-period GaAs/AlAs superlattice at 300 K and 500 K. Our findings provide the first direct evidence of phonon coherence at and above room temperatures, signifying a major advancement in the artificial engineering of phonon band structures. Furthermore, our experimental observations and ab initio lattice dynamics revealed that the coherently modulated phonon band structure enhances three-phonon scattering channels, strengthening high-order anharmonic effects such as three-phonon scattering and optical phonon softening. Our observations demonstrate the robustness of phonon coherence at high temperatures, and opens new routes for engineering phonon band structure and high-order phonon-phonon scattering by employing a flexible, bottom-up nanostructuring approach, with extensive applications in phononic metamaterials, microelectronics, and thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates

The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantially accelerated phonon calculations, high-fidelity prediction of lattice thermal conductivity \k{appa}lat still requires accurate treatment of anharmonic interactions, which remains a key challenge for existing potentials across novel chemical spaces. To address this challenge, we present a comprehensive benchmark of 15 surrogate models for predicting \k{appa}lat using the Phonix database, which contains 6,966 entries with anharmonic phonon properties derived from first-principles calculations. Firstly, We categorize these surrogate models into three distinct groups: Physical-informed feature descriptors combined with ML models, end-to-end deep neural networks, and pre-trained MLIP-embeddings combined with ML models. By evaluating model performance across random, space-group disjoint (testing generalization to unseen crystal symmetries), and Out-Of-Distribution splits (OOD dataset that testing extrapolation to property regimes beyond the training range) based on \k{appa}lat, we probe both interpolation and exploration capabilities. Our results reveal that MLIP-embedded models excel in interpolation within well-sampled regions, deep neural network models especially ALiEGNN demonstrate superior robustness in OOD regimes critical for discovering novel low-\k{appa}lat. Additionally, we find a systematic degradation in performance when the structural representation is reduced. Although surrogate models exhibit lower accuracy than direct simulations using first-principles calculation, they reduce computational costs by orders of magnitude, enabling efficient high-throughput screening of thermoelectric materials with minimal loss in generative design workflows.

cond-mat.mtrl-sci↗

arXiv2025

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6,000 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities -- both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

cond-mat.mtrl-sci↗

arXiv2025

Omics-scale polymer computational database transferable to real-world artificial intelligence applications

Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such as natural language processing, materials science, particularly polymer research, has significantly lagged in developing extensive open datasets. This lag is primarily due to the high costs of polymer synthesis and property measurements, along with the vastness and complexity of the chemical space. This study presents PolyOmics, an omics-scale computational database generated through fully automated molecular dynamics simulation pipelines that provide diverse physical properties for over $10^5$ polymeric materials. The PolyOmics database is collaboratively developed by approximately 260 researchers from 48 institutions to bridge the gap between academia and industry. Machine learning models pretrained on PolyOmics can be efficiently fine-tuned for a wide range of real-world downstream tasks, even when only limited experimental data are available. Notably, the generalisation capability of these simulation-to-real transfer models improve significantly as the size of the PolyOmics database increases, exhibiting power-law scaling. The emergence of scaling laws supports the "more is better" principle, highlighting the significance of ultralarge-scale computational materials data for improving real-world prediction performance. This unprecedented omics-scale database reveals vast unexplored regions of polymer materials, providing a foundation for AI-driven polymer science.

physics.chem-ph↗

arXiv2025

Non-equilibrium Magnon Engineering Enabling Significant Thermal Transport Modulation

Thermal conductivity, a fundamental parameter characterizing thermal transport in solids, is typically determined by electron and phonon transport. Although other transport properties including electrical conductivity and thermoelectric conversion coefficients have material-specific values, it is known that thermal conductivity can be modulated artificially via phonon engineering techniques. Here, we demonstrate another way of artificially modulating the heat conduction in solids: magnonic thermal transport engineering. The time-domain thermoreflectance measurements using ferromagnetic metal/insulator junction systems reveal that the thermal conductivity of the ferromagnetic metals and interfacial thermal conductance vary significantly depending on the spatial distribution of nonequilibrium spin currents. Systematic measurements of the thermal transport properties with changing the boundary conditions for spin currents show that the observed thermal transport modulation stems from magnon origin. This observation unveils that magnons significantly contribute to the heat conduction even in ferromagnetic metals at room temperature, upsetting the conventional wisdom that the thermal conductivity mediated by magnons is very small in metals except at low temperatures. The magnonic thermal transport engineering offers a new principle and method for active thermal management.

cond-mat.mtrl-sci↗

arXiv2025

Suppression of Thin-Film Thermal Conductivity due to Surface Roughness

Understanding thermal transport in silicon nanostructures is crucial for effective thermal management in semiconductor devices. In such nanostructures, boundary scattering can significantly reduce thermal conductivity. Diffusive boundary scattering explains the experimentally observed thickness dependence of thermal conductivity in thin films with thicknesses of tens of nanometers; however, introducing surface roughness further reduces the thermal conductivity, which falls far below the theoretical lower limit. In this study, we calculated the thermal conductivity and phonon transport properties of rough thin films with thicknesses of up to 25 nm using anharmonic lattice dynamics and investigated the mechanisms underlying the suppression of thermal conductivity arising from surface roughness. We found that in ultrathin films with rough surfaces, thermal conductivity was suppressed by a reduction in group velocity caused by hybridization with surface-localized modes, as well as a reduction in relaxation time due to the modulation of the anharmonic interatomic force constants of surface atoms. The reduction in group velocity significantly suppressed thermal conductivity across a wide range of thicknesses and surface-roughness values. In contrast, the reduction in relaxation time exhibited strong thickness dependence. Thus, this relaxation-time reduction should be considered in ultrathin films with roughness of approximately 0.1 nm and thicknesses below 5 nm. These thermal-conductivity suppression mechanisms due to surface roughness were not considered in the boundary-scattering model, resulting in an overestimation of the thermal conductivity of the roughened thin films by up to approximately 100%.

cond-mat.mtrl-sci↗

arXiv2025

Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model

Metamaterials are artificially engineered structures that manipulate electromagnetic waves, having optical properties absent in natural materials. Recently, machine learning for the inverse design of metamaterials has drawn attention. However, the highly nonlinear relationship between the metamaterial structures and optical behaviour, coupled with fabrication difficulties, poses challenges for using machine learning to design and manufacture complex metamaterials. Herein, we propose a general framework that implements customised spectrum-to-shape and size parameters to address one-to-many metamaterial inverse design problems using conditional diffusion models. Our method exhibits superior spectral prediction accuracy, generates a diverse range of patterns compared to other typical generative models, and offers valuable prior knowledge for manufacturing through the subsequent analysis of the diverse generated results, thereby facilitating the experimental fabrication of metamaterial designs. We demonstrate the efficacy of the proposed method by successfully designing and fabricating a free-form metamaterial with a tailored selective emission spectrum for thermal camouflage applications.

physics.optics↗

arXiv2025

Anharmonic phonon renormalization and thermal transport in the type-I Ba$_{\rm 8}$Ga$_{\rm 16}$Sn$_{\rm 30}$ clathrate from first principles

Effects of strong phonon anharmonicity of a type-I clathrate Ba$_{\rm 8}$Ga$_{\rm 16}$Sn$_{\rm 30}$ induced by quadruple-well potential of guest atoms were investigated. Phonon transport including coherent interbranch component was analyzed using a first-principles-based self-consistent phonon (SCP) theory that gives temperature-dependent harmonic interatomic force constants and by solving off-diagonal components of group velocity operator. Experimentally observed thermal conductivities have been reasonably reproduced by considering both lattice and electron contributions. Through the analysis with the SCP theory, we found that hardening of guest modes leads to an increase in lattice thermal conductivity at frequencies below those of framework-dominant flat modes (< 40 cm$^{\rm -1}$), which finally results in the slow decay and slight increase in the total lattice thermal conductivity with increasing temperature. Detailed analyses revealed that the increase in lattice thermal conductivity at low frequency is attributed to (a) the increase in group velocities of phonon modes located at frequencies below that of the flat guest modes and (b) abnormal increase in lifetimes of phonon modes located between frequencies of the flat guest and framework modes with increasing temperature. From an engineering point of view, this effect may lead to an intriguing phenomenon, a larger decrease in thermal conductivity due to nanostructuring at higher temperatures.

cond-mat.mtrl-sci↗

arXiv2025

High thermoelectric power factor in Ni-Fe alloy for active cooling applications

Metallic thermoelectric materials are promising candidates for active cooling applications, where high thermal conductivity and a high thermoelectric power factor are essential to maximize effective thermal conductivity. While metals inherently possess high thermal and electrical conductivities, they typically exhibit low Seebeck coefficients. In this work, we create a database and apply machine learning techniques to identify metallic binary alloys with large Seebeck coefficients. Specifically, we identify Ni-Fe as a promising candidate for active cooling. We then fabricate Ni-Fe ingots and demonstrate thermoelectric power factor values as high as 120 μW/{cm.K^2} at 200 K for these stable alloys, which are composed of cost-effective and abundant elements. Furthermore, we show that the effective thermal conductivity of these alloys, under small temperature differences, can exceed that of pure copper at temperatures above 250 K.

cond-mat.mtrl-sci↗

arXiv2025

ALATDYN: a set of Anharmonic LATtice DYNamics codes to compute thermodynamic and thermal transport properties of crystalline solids

We introduce a lattice dynamics package which calculates elastic, thermodynamic and thermal transport properties of crystalline materials from data on their force and potential energy as a function of atomic positions. The data can come from density functional theory (DFT) calculations or classical molecular dynamics runs performed in a supercell. First, the model potential parameters, which are anharmonic force constants are extracted from the latter runs. Then, once the anharmonic model is defined, thermal conductivity and equilibrium properties at finite temperatures can be computed using lattice dynamics, Boltzmann transport theories, and a variational principle respectively. In addition, the software calculates the mechanical properties such as elastic tensor, Gruneisen parameters and the thermal expansion coefficient within the quasi-harmonic approximation (QHA). Phonons, elastic constants and thermodynamic properties results applied to the germanium crystal will be illustrated. Using the force constants as a force field, one may also perform molecular dynamics (MD) simulations in order to investigate the combined effects of anharmonicity and defect scattering beyond perturbation theory.

cond-mat.mtrl-sci↗

arXiv2025

Human-AI collaboration for modeling heat conduction in nanostructures

In recent years, materials informatics, which combines data science and artificial intelligence (AI), has garnered significant attention owing to its ability to accelerate material development, reduce costs, and enhance product design. However, despite the widespread use of AI, human involvement is often limited to the initiation and oversight of machine learning processes and rarely includes more substantial roles that capitalize on human intuition or domain expertise. Consequently, true human-AI collaborations, where integrated insights can be maximized, are scarce. This study considers the problem of heat conduction in a two-dimensional nanostructure as a case study. An integrated human-AI collaboration framework is designed and used to construct a model to predict the thermal conductivity. This approach is used to determine the parameters that govern phonon transmission over the full range of frequencies and incidence angles. During operation, the self-learning entropic population annealing technique, which combines entropic sampling with a surrogate machine learning model, generates a small dataset that can be interpreted by a human. Therefore, data-efficient and global modeling is achieved, and parameters with physical interpretations are developed, which can guide nanostructural design to produce materials with specific properties. The proposed framework can leverage the complementary strengths of humans and AI, thereby enhancing the understanding and control of materials.

physics.comp-ph↗

arXiv2024

ARIM-mdx Data System: Towards a Nationwide Data Platform for Materials Science

In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge research. However, existing integrated systems that handle data from these resources have primarily focused just on smaller-scale cross-institutional or single-domain operations. As a result, they often lack the scalability, efficiency, agility, and interdisciplinarity, needed for handling substantial volumes of data from various researchers. In this paper, we introduce ARIM-mdx data system, aiming at a nationwide data platform for materials science in Japan. Currently in its trial phase, the platform has been involving 11 universities and institutes all over Japan, and it is utilized by over 800 researchers from around 140 organizations in academia and industry, being intended to gradually expand its reach. The ARIM-mdx data system, as a pioneering nationwide data platform, has the potential to contribute to the creation of new research communities and accelerate innovations.

cs.DC↗

arXiv2024

Boosting the quality factor of Tamm structures to millions by quantum inspired classical annealer with factorization machine

The Tamm structures show high quality (Q) factor property with simple photonic Bragg reflectors composed of alternative high/low refractive index layers and metal reflectors. However, the Q-factor of Tamm structures is inherently limited by the periodic constraints and fixed thicknesses of the Bragg reflector pairs. Removing periodic constraints and adopting aperiodic designs can lead to significantly higher Q-factors. Nevertheless, to fully exploit the potential of Tamm structures, it is essential to thoroughly explore the extensive search space introduced by these aperiodic designs. Herein, we introduce a novel approach that utilizes the quantum-inspired classical annealer combined with a factorization machine to facilitate the design of photonic Bragg reflectors with the aim of achieving ultrahigh Q-factor properties. Our investigation begins by establishing the effectiveness of global optimization in a 15-bit problem setting, showcasing a remarkable efficiency improvement of nearly one order of magnitude compared to random search strategies. Subsequently, we expand our analysis to a 20-bit problem, demonstrating comparable efficiency to state-of-the-art Bayesian optimization methods. Moreover, the proposed method proves its capability in handling extremely large search spaces, exemplified by a 40-bit problem, where we successfully uncover a Tamm structure design exhibiting an extraordinary ultrahigh Q-factor surpassing millions. The designed aperiodic Tamm structure exhibits exceptional high localization and enhancement of electric field, and the power loss is concentrated in the low loss dielectric layer. Hence, the decay rate of power dissipation significantly decreases and light-matter interaction time increases, resulting in ultrahigh Q-factor.

cond-mat.mtrl-sci↗

arXiv2024

Dynamic hysteresis of an oscillatory contact line

During oscillatory wetting, a phase retardation emerges between contact angle variation and contact line velocity, presenting as a hysteresis loop in their correlation -- an effect we term dynamic hysteresis. This phenomenon is found to be tunable by modifying the surface with different molecular layers. A comparative analysis of dynamic hysteresis, static hysteresis, and contact line friction coefficients across diverse substrates reveals that dynamic hysteresis is not a result of dissipative effects but is instead proportionally linked to the flexibility of the grafted layer on the surface. In the quest for appropriate conditions to model oscillatory contact line motion, we identify the generalized Hocking's linear law and modified Generalized Navier Boundary Condition (GNBC) as alternative options for predicting realistic dynamic hysteresis.

physics.flu-dyn↗

arXiv2024

Enhancing and Mapping Thermal Boundary Conductance across Bonded Si/SiC Interface

SiC is a promising substrate for Si-on-insulator (SOI) wafers for efficient thermal management owing to its high thermal conductivity and large bandgap. However, fabricating a Si device layer on a SiC substrate with a high and uniform thermal-boundary conductance (TBC) at the wafer scale is challenging. In this study, a 4-inch Si-on-SiC wafer was fabricated using a room-temperature surface-activated bonding method, and the TBC was measured using the time-domain thermoreflectance (TDTR) method. The obtained TBC was 109 MW/m2K in the as-bonded sample, improving to 293 MW/m2K after annealing at 750 oC, representing a 78% increase compared to previously reported values for a Si-SiC interface formed by bonding methods. Such enhancement is attributed to the absence of an oxide layer at the interface. Furthermore, we assessed the actual spatial distribution of the TBC in the SOI system by combining the TDTR mapping with a mathematical model to remove the influence of random errors in the experiment. The spatial distributions before and after annealing were 7% and 17%, respectively. Such variation highlights the need to consider the TBC distribution when designing thermal management systems.

cond-mat.mtrl-sci↗

arXiv2024

Low thermal boundary resistance at bonded GaN/diamond interface by controlling ultrathin heterogeneous amorphous layer

Thermal boundary resistance (TBR) in semiconductor-on-diamond structure bottlenecks efficient heat dissipation in electronic devices. In this study, to reduce the TBR between GaN and diamond, surface-activated bonding with a hybrid SiOx-Ar ion source was applied to achieve an ultrathin interfacial layer. The simultaneous surface activation and slow deposition of the SiOx binder layer enabled precise control over layer thickness (2.5-5.3 nm) and formation of an amorphous heterogeneous nanostructure comprising a SiOx region between two inter-diffusion regions. Crucially, the 2.5-nm-thick interfacial layer achieved a TBR of 8.3 m2-W/GW, a record low for direct-bonded GaN/diamond interface. A remarkable feature is that the TBR is extremely sensitive to the interfacial thickness; rapidly increasing to 34 m2-K/GW on doubling the thickness to 5.3 nm. Theoretical analysis revealed the origin of this increase: a diamond/SiOx interdiffusion layer extend the vibrational frequency, far-exceeding that of crystalline diamond, which increases the lattice vibrational mismatch and suppresses phonon transmission.

cond-mat.mtrl-sci↗

arXiv2023

Electron-phonon coupling and non-equilibrium thermal conduction in ultrafast heating systems

The electron-phonon coupling in ultrafast heating systems is studied within the framework of Boltzmann transport equation (BTE) with coupled electron and phonon transport. A discrete unified gas kinetic scheme is developed to solve the BTE, in which the electron/phonon advection, scattering and electron-phonon interactions are coupled together within one time step by solving the BTE again at the cell interface. Numerical results show that the present scheme can correctly predict the electron-phonon coupling constant, and is in excellent agreement with typical two-temperature model (TTM) and experimental results in existing literatures and our performed time-domain thermoreflectance technique. It can also capture the ballistic or thermal wave effects when the characteristic length/time is comparable to or smaller than the mean free path/relaxation time where the TTM fails. Finally, the electron-phonon coupling in transient thermal grating geometry and Au/Pt bilayer metals with interfacial thermal resistance is simulated and discussed. For the former, heat flow from phonon to electron is predicted in both the ballistic and diffusive regimes. For the latter, the reflected signal increases in the early tens of picoseconds and then decreases with time after the heat source is removed.

cond-mat.mes-hall↗