arXiv subjects

Mitsuhiro Itakura

Publications and source records attributed to Mitsuhiro Itakura.

6 recordsLinked to original sources

arXiv2026

Spin Neural Network Potential for Magnetic Phase Transitions in Uranium Dioxide

Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at low temperatures, where spin-orbit coupling produces strong coupling between spin and lattice degrees of freedom. Direct DFT simulations of magnetic phase transitions at finite temperatures are computationally prohibitive. Here, we develop a spin neural network potential (SpinNNP) that explicitly incorporates spin degrees of freedom together with spin-orbit coupling to describe the magnetic states of UO2.Reference datasets were generated using magnetic constrained DFT+U calculations with spin-orbit coupling, covering a wide range of non-collinear spin configurations. The SpinNNP accurately reproduces DFT energies, atomic forces, spin forces, and lattice constants. Machine learning molecular dynamics simulations with spin dynamics successfully capture the antiferromagnetic-paramagnetic transition. Although the predicted magnetic ground state differs from experiment due to known limitations of the underlying DFT description, the transition temperature obtained is of the correct order of magnitude compared with experiment. These results demonstrate that machine-learning potentials can enable large-scale spin-lattice simulations of actinide oxides and provide a practical route toward predictive modeling of complex magnetic materials.

cond-mat.mtrl-sci↗

arXiv2024

Specific Heat Anomalies and Local Symmetry Breaking in (Anti-)Fluorite Materials: A Machine Learning Molecular Dynamics Study

Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In this study, we employ machine learning molecular dynamics (MLMD) simulations to investigate the high-temperature thermal properties of thorium dioxide, which has a fluorite structure, and lithium oxide, which has an anti-fluorite structure. Our results show that MLMD simulations effectively reproduce the reported thermal properties of these materials. A central focus of this work is the analysis of specific heat anomalies in these materials at high temperatures, commonly referred to as Bredig, pre-melting, or $\lambda$-transitions. We demonstrate that a local order parameter, analogous to those used to describe liquid-liquid transitions in supercooled water and liquid silica, can effectively characterize these specific heat anomalies. The local order parameter identifies two distinct types of defective structures: lattice defect-like and liquid-like local structures. Above the transition temperature, liquid-like local structures predominate, and the sub-lattice character of mobile atoms disappears.

cond-mat.mtrl-sci↗

arXiv2021

Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica

Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report (Nagai, {\it et al}. Phys. Rev. B 102, 041124(R) (2020)), the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric ensembles to enable general applications for soft materials and liquids with large volume fluctuation. As a demonstration, the isothermal-isobaric SLHMC method was used to study the vibrational structure of liquid silica at temperatures close to the melting point, whereby the slow diffusive motion is beyond the time scale of first-principles molecular dynamics. It was found that the static structure factor thus computed from first-principles agrees quite well with the high-energy X-ray data.

cond-mat.dis-nn↗

arXiv2020

Density Functional Theory Study of Solute Cluster Growth Processes in Mg-Y-Zn LPSO Alloys

Solute clusters in long period stacking order (LPSO) alloys play a key role in their idiosyncratic plastic behavior, for example kink formation and kink strengthening. Identifying atomistic details of cluster structures is a prerequisite for atomistic modeling of LPSO alloys and is crucial for improving their strength and ductility; however, there is much uncertainty regarding interstitial atoms in the cluster. Although density functional theory calculations have shown that the inclusion of Mg interstitial atoms is energetically most favorable in majority of LPSO alloys, solute elements have also been experimentally observed at interstitial sites. To predict the distributions of interstitial atoms in the cluster and to determine the kind of elements present, it is necessary to identify mechanisms by which interstitial atoms are created. In the present work, we use density functional theory calculations to investigate growth processes of solute clusters, specifically the Mg-Y-Zn LPSO alloy, in order to determine the precise atomistic structure of its solute clusters. We show that a pair of an interstitial atom and a vacancy are spontaneously created when a certain number of solute atoms are absorbed into the cluster, and that all full-grown clusters should include interstitial atoms. We also demonstrate that interstitial atoms are mostly Mg, while the rest are Y; interstitial Zn atoms are negligible. This knowledge greatly simplifies the atomistic modeling of solute clusters in Mg-Y-Zn alloys. Owing to the vacancies emitted from the cluster, vacancy density should be super-saturated in regions where solute clusters are growing, and increased vacancy density accelerates cluster growth.

cond-mat.mtrl-sci↗

arXiv2016

Novel Cross-Slip Mechanism of Pyramidal Screw Dislocations in Magnesium

Compared to cubic metals, whose primary slip mode includes twelve equivalent systems, the lower crystalline symmetry of hexagonal close-packed metals results in a reduced number of equivalent primary slips and anisotropy in plasticity, leading to brittleness at the ambient temperature. At higher temperatures, the ductility of hexagonal close-packed metals improves owing to the activation of secondary c+a pyramidal slip systems. Thus understanding the fundamental properties of corresponding dislocations is essential for the improvement of ductility at the ambient temperature. Here, we present the results of large-scale ab-initio calculations for c+a pyramidal screw dislocations in Mg and show that their slip behavior is a stark counterexample to the conventional wisdom that a slip plane is determined by the stacking fault plane of dislocations. A stacking fault between dissociated partial dislocations can assume a non-planar shape with a negligible energy cost and can migrate normal to its plane by a local shuffling of atoms. Partial dislocations dissociated on a {2-1-12} plane "slither" in the {01-11} plane, dragging the stacking fault with them in response to an applied shear stress. This finding resolves the apparent discrepancy that both {2-1-12} and {01-11} slip traces are observed in experiments while ab-initio calculations indicate that dislocations preferably dissociate in the {01-11} planes.

cond-mat.mtrl-sci↗

arXiv2012

First-Principles Study on the Mobility of Screw Dislocations in BCC Iron

The fully two-dimensional Peierls barrier map of screw dislocations in BCC iron has been calculated using the first principles method to identify the migration path of a dislocation core. An efficient method to correct the effect of the finite size cell used in the first principles method on the energy of a lattice defect was devised to determine the accurate barrier profile. We find that the migration path is close to a straight line which is confined in a $\{110\}$ plane and the Peierls barrier profile is single-humped. This result clarifies a reason that the existing empirical potentials of BCC iron fail to predict the correct mobility path. A line tension model, incorporating these first principles calculation results, is used to predict the kink activation energy to be 0.73 eV in agreement with experiment.

cond-mat.mtrl-sci↗