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Lobzenko, I.; 都留 智仁; 他2名*
Computational Materials Science, 219, p.112010_1 - 112010_9, 2023/02
To elucidate the origin of excellent mechanical properties of high-entropy alloys (HEA), it is essential to develop the atomic-level depiction of defect structures considering the effects of the constituent elements. While classical molecular dynamics have been one of the most effective tools for understanding the defect structures from the atomic level, there is still a problem with the accuracy of the inter-atomic potential of complicated alloy systems such as HEA. A new technique for building such potentials based on machine learning was recently developed. We employed the technique and constructed highly accurate potentials with good robustness for two BCC medium-entropy alloys: MoNbTa and ZrNbTa. Atomic simulation based on the new potentials indicate significant differences in the fundamental mechanical properties of two alloys, depending on the constituent elements, that dominate deformation behavior.
川久保 陽子; Stevens, R.*; Pickett, S.*; 関根 恵; 野呂 尚子; 井上 尚子
Proceedings of INMM & ESARDA Joint Virtual Annual Meeting (Internet), 10 Pages, 2021/08
Integrated Support Center for Nuclear Nonproliferation and Nuclear Security (ISCN) of Japan Atomic Energy Agency (JAEA) in cooperation with the International Atomic Energy Agency (IAEA) executed the first online regional training course on the State System of Accounting for and Control of Nuclear Material (Online RTC-SSAC) from 9 to 20 November 2020. JAEA and its predecessor organizations have held RTC-SSAC every year since 1996 in in-person style for supporting the capacity building in the IAEA member states, however; COVID-19 pandemic posed a serious impact on implementing conventional in-person training in 2020. In addition to that, ISCN had recognized the advantages of developing the online SSAC course as it can supplement the in-person course. With this background, ISCN/JAEA in cooperation with IAEA initiated the development of the online RTC-SSAC in April 2020. This paper provides a summary of the experience in developing the first Online RTC-SSAC including the steps taken to transition the course from an in-person course to an online course. It also identifies good practices that were established during the conduct of the two-week course as well as lessons learned to integrate into future courses. The paper concludes with a look at the future of online training and possible next steps to ensure that it will support the needs of the IAEA Member States.
Gil, J.; 小林 恵太; 板倉 充洋
no journal, ,
This study aims to calculate the solution enthalpies of solutes in liquid metals with experimental accuracy at reasonable cost. For this purpose, we train machine-learning potentials on first-principles data generated with meta-GGA exchange-correlation functionals. Previous GGA-based calculations required empirical post-calculation corrections, while direct meta-GGA simulations - though more accurate - are generally too costly to achieve statistically meaningful results for liquid metals. Machine-learning potentials trained on meta-GGA data via active learning enable much faster calculations with improved accuracy. Applying our approach to oxygen in liquid sodium, the calculated solution enthalpy closely matches experimental values without any corrections. This workflow can be extended to other solutes in liquid metals, particularly where experimental data are lacking or unreliable, to build a comprehensive database.
