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Katsuaki Tanabe

Publications and source records attributed to Katsuaki Tanabe.

6 recordsLinked to original sources

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

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

Refined Thermodynamic Uncertainty Relation for Chemical Reactions

Thermodynamic uncertainty relations elucidate the intricate balance between the precision of current and the thermodynamic costs or dissipation, marking a recent and enthralling advancement at the confluence of statistical mechanics, thermodynamics, and information theory. In this study, we derive a time-energy uncertainty relation tailored for chemical reactions, expressed in terms of the Gibbs free energy and chemical potential. This inequality holds true irrespective of whether the total substance of chemical species is conserved during the reaction. Furthermore, it supports the general thermodynamic framework by ensuring the spontaneous decrease in Gibbs free energy. We present two formulations of the thermodynamic uncertainty relation: one based on chemical species concentrations and the other on molar fractions. The validity of our inequalities is numerically demonstrated using model systems of the Belousov-Zhabotinsky and Michaelis-Menten reactions. Our uncertainty relation may find practical applications in measuring and optimizing thermodynamic properties relevant to chemical reaction systems out of equilibrium.

cond-mat.stat-mech

Pareto's 80/20 Rule and the Gaussian Distribution

The statistical state for the empirical Pareto's 80/20 rule has been found to correspond to a normal or Gaussian distribution with a standard deviation that is twice the mean. This finding represents large characteristic variations in our society and nature. In this distribution, the rule can be also referred to as, for example, the 25/5, 45/10, 60/15, or 90/25 rule. In addition, our result suggests the existence of implicit negative contributors.

q-fin.GN

Coupled-Double-Quantum-Dot Environmental Information Engines: A Numerical Analysis

We conduct numerical simulations for an autonomous information engine comprising a set of coupled double quantum dots using a simple model. The steady-state entropy production rate in each component, heat and electron transfer rates are calculated via the probability distribution of the four electronic states from the master transition-rate equations. We define an information-engine efficiency based on the entropy change of the reservoir, implicating power generators that employ the environmental order as a new energy resource. We acquire device-design principles, toward the realization of corresponding practical energy converters, including that (1) higher energy levels of the detector-side reservoir than those of the detector dot provide significantly higher work production rates by faster states' circulation, (2) the efficiency is strongly dependent on the relative temperatures of the detector and system sides and becomes high in a particular Coulomb-interaction strength region between the quantum dots, and (3) the efficiency depends little on the system dot's energy level relative to its reservoir but largely on the antisymmetric relative amplitudes of the electronic tunneling rates.

cond-mat.stat-mech

Mutual Information in Coupled Double Quantum Dots: A Simple Analytic Model for Potential Artificial Consciousness

The integrated information theory is thought to be a key clue towards the theoretical understanding of consciousness. In this study, we propose a simple numerical model comprising a set of coupled double quantum dots, where the disconnection of the elements is represented by the removal of Coulomb interaction between the quantum dots, for the quantitative investigation of integrated information. As a measure of integrated information, we calculate the mutual information in the model system, as the Kullback-Leibler divergence between the connected and disconnected status, through the probability distribution of the electronic states from the master transition-rate equations. We reasonably demonstrate that the increase in the strength of interaction between the quantum dots leads to higher mutual information, owing to the larger divergence in the probability distributions of the electronic states. Our model setup could be a useful basic tool for numerical analyses in the field of integrated information theory.

quant-ph