Reconsideration of numerical model for hydrogen thermal desorption spectra of iron with hydrogen-enhanced strain-induced vacancies

Ebihara, Kenichi; Yamaguchi, Masatake; Itakura, Mitsuhiro

Metallurgical and Materials Transactions A, 57(4), p.1480 - 1489, 2026/04

https://doi.org/10.1007/s11661-026-08146-7

Hydrogen (H) embrittlement is an important issue for steel. The experimental thermal desorption spectra of H from an iron sample containing H-enhanced strain-induced vacancies (Vs) were successfully reproduced by revising a previous numerical model. In the revised model, we adopted concentration variables for Vs and V clusters, which are distinguished by the number of trapped H atoms. This revision eliminated the assumption of V and V cluster migration, required in the original model. Simulation results of the revised model revealed that the spike-like desorption on the peak attributed to Vs and V clusters in the spectra simulated by the original model was an artifact caused by the assumption. In addition, it was suggested that V clusters can exist other than Vs in the specimens after deformation with H charging. It is considered that the revised model is a useful framework for studying Vs and V clusters under H-affected conditions.

Construction and deployment of an on-premises generative AI infrastructure using supercomputers

Takaku, Yuhi; Sakazume, Shun; Kimura, Hideo

JAEA-Technology 2025-017, 33 Pages, 2026/03

JAEA-Technology-2025-017.pdf:2.73MB

At the Japan Atomic Energy Agency (JAEA), expectations and demand for generative AI had been increasing, particularly to improve operational efficiency and foster ideas in research and development. However, cloud-based external generative AI services such as ChatGPT typically use input data for learning, which raised security concerns and prevented handling a considerable amount of information. In addition, the required procedures and applications before use were cumbersome, making it hard to say that generative AI was widely adopted or effectively used within JAEA. To address these issues, we built a generative AI infrastructure using JAEA's existing computing resources, including its supercomputers, and open-source software. This approach kept implementation costs low while ensuring safety and ease of use. After deployment across the organization, we observed notable improvements in daily operational efficiency and a surge in interest in generative AI, leading to expanded initiatives for its utilization.

Effects of non-decontamination human activities on the reduction of ambient dose equivalent rates in residential areas near the Fukushima Daiichi Nuclear Power Plant

Kim, M.; Yoshimura, Kazuya; Sakuma, Kazuyuki; Malins, A.*; Abe, Tomohisa; Nakama, Shigeo; Machida, Masahiko; Saito, Kimiaki

Journal of Environmental Radioactivity, 294, p.107931_1 - 107931_8, 2026/03

https://doi.org/10.1016/j.jenvrad.2026.107931

This study quantitatively evaluated the effects of non-decontamination human activities, such as traffic, on ambient dose rates in residential areas near the Fukushima Daiichi Nuclear Power Plant through field measurements and simulations. Field surveys showed that areas with higher traffic had greater reductions in Cs deposition, suggesting that vehicular movement may enhance cesium weathering. Monte Carlo simulations using 3D-ADRES confirmed that human activities accelerate the decrease in ambient dose rates on paved surfaces, with reductions of up to approximately 50%. These results indicate that non-decontamination human activities significantly contribute to lowering ambient dose rates.

A Scheme to efficiently select air-dose-rate measurement positions in estimating radiation source distribution

Machida, Masahiko; Yamada, Susumu; Yoshida, Toru*; Hasegawa, Yukihiro*; Yanagi, Hideaki*; Furutachi, Naoya*

Proceedings of Waste Management Symposia 2026 (WM2026) (Internet), 15 Pages, 2026/03

Operational improvements of the job scheduling system in the large-scale computer system at the Japan Atomic Energy Agency

Kawazu, Ryohei

JAEA-Technology 2025-014, 48 Pages, 2026/02

JAEA-Technology-2025-014.pdf:2.19MB

The Japan Atomic Energy Agency (JAEA) conducts research and development in various fields related to nuclear energy as a comprehensive research and development organization for nuclear power. Computational science and technology are utilized in many of these research and development activities. The supercomputer system HPE SGI8600 (hereinafter referred to as the "supercomputer") was introduced in December 2020 as critical infrastructure to meet the increasing computational demands driven by advancements in technologies such as digital twins, machine learning, and big data processing. It has become indispensable for promoting research and development at JAEA. Improving the efficiency of job operations and program waiting times (hereinafter referred to as "job waiting times") on the supercomputer, which is an essential infrastructure supporting JAEA's computational science and technology, is useful for enhancing research and development efficiency. This report presents the results of the investigation into the changes in job waiting times following the integration of queue classes, which was implemented in fiscal year 2022 to efficiently utilize computational resources. It summarizes the process from the analysis of the supercomputer's usage information to the improvements made for the integration of queue classes and the improvement of job waiting times.

Summaries of research and development activities by using supercomputer system of JAEA in FY2024 (April 1, 2024 - March 31, 2025)

HPC Technology Promotion Office, Center for Computational Science & e-Systems

JAEA-Review 2025-044, 140 Pages, 2026/01

JAEA-Review-2025-044.pdf:8.77MB

Japan Atomic Energy Agency (JAEA) conducts research and development (R&D) in various fields related to nuclear power as a comprehensive institution of nuclear energy R&Ds, and utilizes computational science and technology in many activities. Over the past 10 years or so, the publication of papers utilizing computational science and technology at JAEA has accounted for about 20 percent of the total publications each fiscal year. The supercomputer system of JAEA has become an important infrastructure to support computational science and technology. In FY2024, the system was utilized in R&D activities that were prioritized in the Fourth Medium- to Long-Term Plan, including contributing to carbon neutrality through the development of innovative technologies for improving safety, creating innovation by promoting diverse R&D related to nuclear science and technology, promoting R&D in response to the accident at TEPCO's Fukushima Daiichi Nuclear Power Station, steadily implementing technological developments for the treatment and disposal of high-level radioactive waste, and supporting nuclear safety regulatory administration and nuclear disaster prevention by promoting safety research for these purposes. This report presents a great number of R&D results accomplished by using the system in FY2024, as well as user support, operational records and overviews of the system, and so on.

Doping-dependent Fe phonon dynamics in LaFeAsOH studied by Fe nuclear resonant inelastic scattering

Kawachi, Shiro*; Hiraka, Haruhiro*; Yamaura, Junichi*; Iimura, Soshi*; Nakamura, Hiroki; Tsutsui, Satoshi*; Yoda, Yoshitaka*; Machida, Masahiko; Hosono, Hideo*; Kobayashi, Hisao*

Physical Review B, 113(2), p.024519_1 - 024519_7, 2026/01

https://doi.org/10.1103/7zd9-5t9z

To investigate the phonon dynamics of iron in the heavily hydride-ion-substituted region of LaFeAsOH (), Fe nuclear resonant inelastic scattering measurements were performed over a wide temperature range from 5 to 300 K on two polycrystalline samples with and 0.51, which exhibit superconducting and antiferromagnetic ground states, respectively. The resulting inelastic scattering spectra revealed distinct differences between the two compositions. The Fe phonon density of states (PDOS) exhibits a pronounced peak at 15 meV for , whereas this peak is absent in . Density functional theory calculations support the interpretation that the PDOS peak at 15 meV is associated with the optical vibrational modes of Fe atoms along the nearest-neighbor direction, mediated by As atoms. The calculations further suggest that the suppression of the PDOS peak at 15 meV for originates from in-plane electronic inequivalence. These findings suggest that signatures of electronic nematicity may persist over a wide temperature range in the composition, which exhibits magnetic and structural order, whereas such signatures are almost absent in the superconducting composition.

First-principles calculations of hydrogen trapping energy at an edge dislocation core in iron

Yamaguchi, Masatake; Ebihara, Kenichi; Itakura, Mitsuhiro

Scripta Materialia, 268, p.116887_1 - 116887_6, 2025/11

https://doi.org/10.1016/j.scriptamat.2025.116887

Lattice investigation of custodial two-Higgs-doublet model at weak quartic couplings

Catumba, G.*; Hiraguchi, Atsuki; Hou, W.-S.*; Jansen, K.*; Kao, Y.-J.*; David Lin, C.-J.*; Ramos, A.*; Sarkar, M.*

Journal of High Energy Physics (Internet), 2025(10), p.214_1 - 214_34, 2025/10

https://doi.org/10.1007/JHEP10(2025)214

The SU(2)-gauged custodial two-Higgs-doublet model, which shares the same global-symmetry properties with the standard model, is studied non-perturbatively on the lattice. The additional Higgs doublet enlarges the scalar spectrum and opens the possibility for spontaneous breaking of the global symmetry. In this work we start by showing the occurrence of spontaneous breaking of the custodial symmetry in a region of the parameter space of the model. Following this, both the spectrum and the running of the gauge coupling are examined at weak quartic couplings in the presence of the custodial symmetry. The calculations are performed with energy cutoffs ranging from 300 to 600 GeV on a line of constant standard model physics, obtained by tuning bare couplings to fix the ratio between the masses of the Higgs and the W bosons, as well as the value of the renormalized gauge coupling at the scale of the W boson mass. The realizable masses for the additional scalar states are explored. For the choice of bare quartic couplings in this work, the estimated lower bound of these masses is found to be well below the W boson mass, and independent of the cutoff. We also study the finite temperature electroweak transition along this line of constant standard model physics, revealing properties of a smooth crossover behavior.

Machine learning molecular dynamics simulations of materials with complex structures

Okumura, Masahiko

Journal of Electronic Materials, 54(9), p.7015 - 7026, 2025/09

https://doi.org/10.1007/s11664-025-12182-1

The machine learning molecular dynamics (MLMD) method enables simulations with high prediction accuracy at low computational cost by learning the results of first-principles calculations (quantum mechanical calculations) using artificial neural networks. This presentation will show how machine-learning molecular dynamics can simulate materials with complex structures. Our MLMD simulations succeeded in reproducing the experimental results of the phonon spectrum of the hydroxy groups of a clay mineral (kaolinite), the superionic transition of thorium dioxide, and the medium-range ordered structure of silica glass, which are difficult to accurately evaluate using other simulation methods, such as classical molecular dynamics.