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Souta Miyamoto

Publications and source records attributed to Souta Miyamoto.

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A deviatoric-stress closure for constitutive modeling of viscoelastic dynamics

Standard rheological measurements yield only selected stress components; thus, inferring tensorial constitutive equations from experimentally accessible observables is complicated. We propose a constitutive formulation written in terms of a deviatoric stress tensor, whose trace is zero, rather than the extra stress tensor. From rheometric data including shear stress, first and second normal stress differences under shear, and elongational stress under uniaxial elongation, we can construct a deviatoric stress state without the indeterminate isotropic stress. The deviatoric-stress dynamics is represented by a closure inferred through symbolic regression, constrained to satisfy material objectivity and a given linear Maxwell response. To demonstrate the proposed formulation, two closures inferred from stress responses of the Giesekus and Larson models successfully captured untrained transient-flow responses under planar elongation and mixed shear/uniaxial elongations at deformation rates around an inverse relaxation time. Steady rheological functions of the closures agreed with the original models in the linear-response regime and over a deformation-rate range connected to the training data, whereas deviations and divergent responses appeared at larger deformation rates outside the training regime. These results demonstrate that the proposed deviatoric-stress formulation provides a practical route for constitutive modeling of observable linear and nonlinear viscoelastic dynamics, while clarifying its range of validity under strong deformation.

cond-mat.soft

Development of Rheological Constitutive Modeling Method Using a Sparse Identification Algorithm: A Case Study for Extensional Flows

Deriving constitutive models (CMs) from numerical data has been an attractive approach as a systematic CM building method. One recent study is Rheo-SINDy, which extended the sparse identification of nonlinear dynamics (SINDy) method to rheology. Although the Rheo-SINDy framework discovered an approximate CM from numerical data under shear flow, its versatility has not been investigated. To clarify its applicability to other types of flows, this study applied Rheo-SINDy to numerically generated data under extensional flow conditions. As baseline tests for extensional flow, we considered two problems: (i) whether the Rheo-SINDy framework can reproduce the famous Giesekus model from data generated by that model, and (ii) whether it can derive an approximate CM from data generated by a dumbbell model with a finite extensible nonlinear elastic (FENE) spring. For problem (i), we confirmed that Rheo-SINDy can identify the exact expression of the Giesekus model under extensional flow. For problem (ii), the Rheo-SINDy framework discovered a relatively simple expression of the approximate CM by manually designing the library matrix based on rheological knowledge. The identified approximate CM can reasonably predict extensional rheological properties of the FENE dumbbell model, including an extrapolation region. These findings demonstrate the fundamental validity of using Rheo-SINDy under extensional flow.

physics.flu-dyn

Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset

We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides.

cond-mat.supr-con

Rheo-SINDy: Finding a Constitutive Model from Rheological Data for Complex Fluids Using Sparse Identification for Nonlinear Dynamics

Rheology plays a pivotal role in understanding the flow behavior of fluids by discovering governing equations that relate deformation and stress, known as constitutive equations. Despite the importance of these equations, current methods for deriving them lack a systematic methodology, often relying on sense of physics and incurring substantial costs. To overcome this problem, we propose a novel method named Rheo-SINDy, which employs the sparse identification of nonlinear dynamics (SINDy) algorithm for discovering constitutive models from rheological data. Rheo-SINDy was applied to five distinct scenarios, four with well-established constitutive equations and one without predefined equations. Our results demonstrate that Rheo-SINDy successfully identified accurate models for the known constitutive equations and derived physically plausible approximate models for the scenario without established equations. Notably, the identified approximate models can accurately reproduce nonlinear shear rheological properties, especially at steady state, including shear thinning. These findings validate the robustness of Rheo-SINDy in handling data complexities and underscore its efficacy as a tool for advancing the development of data-driven approaches in rheology.

cond-mat.soft

Enhancement of Curie temperature due to the coupling between Fe itinerant electrons and Dy localized electrons in DyFe2Zn20

The temperature dependence of magnetization and specific heat of the ferromagnetic compound DyFe2Zn20 has been measured in detail in various magnetic fields. We have observed anomalous magnetic behavior, i.e., a strong anisotropy at 2 K, disappearance of this anisotropy between approximately 30 K and T_c, and anomalous behavior of the specific heat in the magnetic fields near 20 K. These anomalous phenomena have been analyzed based on the strong exchange interaction between the Fe itinerant electrons and the Dy localized electrons as well as the crystalline electric field, Zeeman energy, and an usual exchange interaction between two Dy atoms. The higher T_c of DyFe2Zn20 compared with that of DyRu2Zn20 is caused by this exchange interaction between the Fe and Dy atoms.

cond-mat.str-el