arXiv subjects

Kansei Kanayama

Publications and source records attributed to Kansei Kanayama.

4 recordsLinked to original sources

arXiv2024

Nuclear Quantum Effects on Proton Diffusivity in Perovskite Oxides

In the present study, the nuclear quantum effects (NQEs) on proton diffusivity in oxides were evaluated by molecular dynamics (MD) simulations with the quantum thermal bath (QTB) based on the Langevin dynamics. We employed the proton diffusion in barium zirconate (BaZrO3) with the cubic perovskite structure as the model system, in which protons migrate by rotation around single oxide ions and hopping between adjacent oxide ions. MD simulations with the standard classical thermal bath (CTB) and phonon calculations were also conducted to verify the conventionally used classical harmonic transition state theory (classical h-TST), in which the transition state theory (TST), the harmonic approximation, and the classical approximation are assumed. As a result, the h-TST are reasonable for the proton rotation, while significantly overestimate the activation energy and the pre-exponential factor of the jump frequency for the proton hopping. Furthermore, the classical approximation makes the proton jump frequencies close to linear in the Arrhenius plots, which should actually be nonlinear by the NQEs in the temperature range of 500-2000 K. This suggests the necessity of the treatment beyond the classical h-TST for accurate evaluation of the proton diffusivity in oxides even in the intermediate temperature range (573-873 K).

cond-mat.mtrl-sci↗

arXiv2024

Quantitative evaluation of nuclear quantum effects on the phase transitions in BaTiO3 using large-scale molecular dynamics simulations based on machine learning potentials

The machine learning potential (MLP) based molecular dynamics (MD) method was applied for constructing the pressure-temperature phase diagram in the barium titanate (BaTiO3) crystals. The nuclear quantum effects (NQEs) on the phase transitions were quantitatively evaluated from the difference in the phase transition pressures between the NQEs-incorporated and classical simulations. In this study, the quantum thermal bath (QTB) method was used for incorporating the NQEs. The constructed phase diagrams verified that the NQEs lower the phase transition temperatures and pressures. The NQEs are more significant at lower temperatures but cannot be ignored even at room temperature. The phase diagram in the QTB-based MLPMD is in good agreement with those of the previous studies based on dielectric measurements and path-integral based simulations. In addition, this study clarified that the large cell size (a 16x16x16 or larger cell) and friction coefficient (>= 15 THz) are required for accurately reproducing the phase transitions during the QTB-MD simulations.

cond-mat.mtrl-sci↗

arXiv2024

First-principles study of phase transition in cadmium titanate by molecular dynamics incorporating nuclear quantum effects

First-principles molecular dynamics (FPMD) simulations were applied for the paraelectric-ferroelectric phase transition in the perovskite-type cadmium titanate, CdTiO3. Since the phase transition is reported to occur at the low temperature around 80 K, the quantum thermal bath (QTB) method was utilized in this study, which incorporates the nuclear quantum effects (NQEs). The structural evolutions in the QTB-FPMD simulations are in reasonable agreement with the experimental results, by contrast in the conventional FPMD simulations using the classical thermal bath (CTB-FPMD). According to our phonon calculations, volume expansion is the key in the stabilization of the ferroelectric phase at low temperatures, which was well reproduced in the QTB-FPMD with the NQEs. Thus, the NQEs are of importance in phase transitions at low temperatures, particularly below the room temperature, and the QTB is useful in that it incorporates the NQEs in MD simulations with low computational costs comparable to the conventional CTB.

cond-mat.mtrl-sci↗

arXiv2020

Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal

An efficient machine-learning-based method combined with a conventional local optimization technique has been proposed for exploring local energy minima of interstitial species in a crystal. In the proposed method, an effective initial point for local optimization is sampled at each iteration from a given feasible set in the search space. The effective initial point is here defined as the grid point that most likely converges to a new local energy minimum by local optimization and/or is located in the vicinity of the boundaries between energy basins. Specifically, every grid point in the feasible set is classified by the predicted label indicating the local energy minimum that the grid point converges to. The classifier is created and updated at every iteration using the already-known information on the local optimizations at the earlier iterations, which is based on the support vector machine (SVM). The SVM classifier uses our original kernel function designed as reflecting the symmetries of both host crystal and interstitial species. The most distant unobserved point on the classification boundaries from the observed points is sampled as the next initial point for local optimization. The proposed method is applied to three model cases, i.e., the six-hump camelback function, a proton in strontium zirconate with the orthorhombic perovskite structure, and a water molecule in lanthanum sulfate with the monoclinic structure, to demonstrate the high performance of the proposed method.

physics.comp-ph↗