arXiv subjects

Kairi Masuda

Publications and source records attributed to Kairi Masuda.

4 recordsLinked to original sources

arXiv2026

Atomic-scale phase-field modeling with dopants: Stochastic self-consistent harmonic approximation with fractional site occupancy

Phase-field modeling has achieved great success in predicting pattern formation in materials, such as the formation of ferroelectric domains. However, because it is typically based on continuum mechanics, conventional phase-field modeling cannot be straightforwardly applied to atomic-scale pattern formation, such as dopant segregation and vacancy ordering, which are driven by chemical potentials. Here, we extend the phase-field concept to the atomic scale by formulating the free energy of atomic systems within stochastic self-consistent harmonic approximation (SSCHA) theory to allow fractional site occupations. Our methodology enables us to directly calculate the derivative of the free energy with respect to site occupation and thereby obtain the chemical potential, successfully reproducing Ag distributions in bulk Cu as well as the resulting lattice expansion. Furthermore, we applied our methodology to investigate dopant segregation around a {\Sigma}5(310)[001] Cu grain boundary doped with Ag atoms. We found that Ag atoms preferentially segregate at the vertices of the triangular motif of a grain boundary. As the number of dopants increases, excess Ag atoms segregate near the vertices and then at the bottom sites of the triangular motif. This study extends the phase-field concept to discrete atomic systems, enabling the identification of preferential dopant-segregation sites and thereby visualizing atomic-scale pattern formation.

cond-mat.mtrl-sci↗

arXiv2026

Atomic-scale phase-field modeling for 2D ferroelectrics including non-Gaussian fluctuations

Atomic-scale phase-field modeling extends the phase-field framework down to the level of individual atoms by treating the probability density of atomic vibrations as a field variable and constructing a corresponding free-energy functional from this atomic field together with interatomic potentials. In this way, the framework has the potential to visualize local thermodynamic states with atomic-level resolution, just as conventional phase-field modeling has served as a computational microscope for free energy, stress, and related quantities at mesoscopic scales. However, existing formulations mainly assume Gaussian probability distributions for atomic vibrations, which limits their applicability to more complex and heterogeneous systems such as surfaces. In this work, we generalize the atomic-scale phase-field methodology by extending the free-energy functional to include non-Gaussian fluctuations. We apply this approach to monolayer SnTe in the NVT ensemble and show that the predicted equilibrium polarization is in better agreement with molecular dynamics simulations and that the ferroelectric-to-paraelectric phase transition is successfully reproduced. Furthermore, by decomposing the entropy on a per-atom basis, we visualize atomically resolved maps of local entropy and find that Sn atoms contribute more strongly to the entropy than Te atoms at high temperature, which is a driving factor of the ferroelectric-to-paraelectric phase transition. These results broaden the applicability of phase-field approaches to a wider range of atomic systems and suggest a route toward an atom-resolved theory of phase transitions based on high-resolution thermodynamics.

cond-mat.mtrl-sci↗

arXiv2025

On-demand phase-field modeling: Three-dimensional Landau energy for HfO2 through machine learning

The unexpected emergence of ferroelectricity in HfO2 at reduced dimensions has attracted considerable attention, as it provides a pathway toward the realization of ultrasmall ferroelectric devices. Ab initio calculations suggest that this effect arises from a unique mode coupling, in which an antipolar displacement mode stabilizes a robust polar distortion. Based on these insights, Landau-Devonshire energy models have been proposed using such lattice modes as order parameters. However, most existing models are limited to a simplified one-dimensional model because of the computational cost of ab initio calculations and the limitations of conventional Landau polynomials. Here, we constructed a three-dimensional Landau-Devonshire potential for HfO2 by employing the tetragonal, antipolar, and polar modes as coupled order parameters, based on the latest machine-learning technologies. We generated a large-scale dataset of energies over a three-dimensional structural space, with the computational cost drastically reduced through the use of machine-learning interatomic potentials, and trained a multilayer perceptron (MLP) to learn the relationship between the order parameters and the energy. The energy predicted by the MLP successfully captures the characteristic coupling behavior whereby the antipolar modes induce the polar mode. Furthermore, by extending this MLP-based Landau potential to a position-dependent functional, that is, to a phase-field modeling framework, we revealed that the polarization magnitude in thin films decreases compared with the bulk state, while the critical strain required for the onset of spontaneous polarization increases due to surface effects. This study presents a new framework for the on-demand construction of Landau energy and phase-field modeling using the latest machine-learning techniques, enabling multiscale analysis of complex ferroelectric phenomena.

cond-mat.mtrl-sci↗

arXiv2025

Atomic-scale phase-field modeling with universal machine learning potentials

Atomic-scale phase-field modeling formulates the probability densities of atomic vibrations as Gaussian distributions and derives a free energy functional using variational Gaussian theory and interatomic potentials. This framework permits per-Gaussian decomposition of the free energy, providing a description of local thermodynamic states with atomic resolution. However, existing formulations are limited to classical pairwise interatomic potentials, restricting their applicability to specific materials and compromising quantitative accuracy. In this work, we extend the atomic-scale phase-field methodology by incorporating universal machine learning interatomic potentials, thereby generalizing the free energy functional to many-body systems. This extension enhances both the accuracy and transferability of the approach. We demonstrate the method by applying it to bulk copper under NVT and NPT ensembles, where the predicted pressures and equilibrium lattice constants show excellent agreement with molecular dynamics simulations, validating the theoretical framework. Furthermore, we apply the method to Σ5(310)[001] grain boundaries in copper, enabling the visualization of local free energy distributions with atomic-scale resolution. The results reveal a pronounced free energy concentration at the grain boundary core, capturing the thermodynamic signature of the interface. This study establishes a versatile and accurate framework for atomic-scale thermodynamic modeling, significantly broadening the scope of phase-field approaches to include complex materials and defect structures.

cond-mat.mtrl-sci↗