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論文

Construction of machine-learning Zr interatomic potentials for identifying the formation process of c-type dislocation loops

沖田 泰良*; 寺山 怜志*; 津川 聖人*; 小林 恵太; 奥村 雅彦; 板倉 充洋; 鈴木 克幸*

Computational Materials Science, 202, p.110865_1 - 110865_9, 2022/02

 被引用回数:7 パーセンタイル:48.44(Materials Science, Multidisciplinary)

In this study, a Neural Network Potential (NNP) using an Artificial Neural Network (ANN) was developed for Zr, which is used as fuel cladding material in light water reactors. The reference data were obtained through first-principles calculations of various quantities, such as strained hexagonal-closed-packed (hcp) cells, strained face-centered cubic cells, cells containing a vacancy, several vacancies, and surfaces and $$gamma$$-surface energy on all five slip planes in the hcp structures. These data were converted to training data for the ANN, which were invariant to the rotation and translation of the atoms and independent of the number of atoms in the cells. The ANN was defined as a three-layer structure and the number of the nodes was set to 26-12-18-1. The NNP reproduced the first-principles calculations, particularly for the shear deformation, vacancy formation energy, surface energy, and $$gamma$$-surface energy, with much higher accuracy than any of the existing potentials that have been developed for classical molecular dynamics simulations. The NNP was applied to identify the formation process of c-type dislocation loops in Zr, which is a key microstructure responsible for abrupt increases in hydrogen absorption. The formation process was determined by the balance of the vacancy formation energy, surface energy and the $$gamma$$-surface energy on the basal plane, both of which were precisely reproduced only by the NNP developed in this study. The formation process was identified based on the atomistic behavior of the NNP.

論文

Molecular dynamic simulations evaluating the effect of the stacking fault energy on defect formations in face-centered cubic metals subjected to high-energy particle irradiation

寺山 怜志*; 岩瀬 祐樹*; 早川 頌*; 沖田 泰良*; 板倉 充洋; 鈴木 克幸*

Computational Materials Science, 195, p.110479_1 - 110479_12, 2021/07

 被引用回数:9 パーセンタイル:57.69(Materials Science, Multidisciplinary)

Austenitic stainless steels, which are used as incore structural materials in light water reactors, are characterized by an extremely low stacking fault energy (SFE) among face-centered cubic (FCC) metals. To evaluate the effects of SFE on defect formation under high-energy particle irradiation, molecular dynamics simulations were performed using the interatomic potential sets for FCC metals with different SFEs and a primary knock-on atom energy (E$$_{rm PKA}$$) of 100 keV at 600 K. The results show that the number of residual defects is independent of the SFE. However, the characteristics of self-interstitial atom (SIA) clusters do depend on the SFE. For clusters smaller than a certain size, the ratio of glissile SIA clusters decreases as the SFE increases, which is similar to the trend observed at the low E$$_{rm PKA}$$. However, for larger clusters, which can be detected only at a high E$$_{rm PKA}$$, the ratio of glissile clusters increases. These results correspond to static energy calculations, in which the difference in the formation energy between a Frank loop and perfect loop ($$Delta$$E$$_{rm F-P}$$) for the small clusters decreases as the SFE increases. In contrast, for the larger clusters, the SFE dependence of $$Delta$$E$$_{rm F-P}$$ changes due to the shape restrictions of stable perfect loops. At a high temperature of 600 K, large vacancy clusters with stacking faults can be detected at E$$_{rm PKA}$$ = 100 keV, resulting in the enhanced formation of these clusters at lower SFEs. Furthermore, several of these clusters were similar to perfect loops, with the edges split into two partial dislocations with stacking faults, although the largest clusters detected at low E$$_{rm PKA}$$s were similar to stacking fault tetrahedrons.

口頭

機械学習分子動力学法によるZr中の空孔集合体挙動解明に関する研究

津川 聖人*; 寺山 怜志*; 沖田 泰良*; 奥村 雅彦; 板倉 充洋

no journal, , 

BWRで使用されるZry-2では、燃焼度が一定値($$sim$$40GWd/t)を超すと、c-type転位ループが観察されることが知られている。c-type転位ループは、中性子照射下で形成した原子空孔が異方拡散し、六方晶底面上に集合体を形成し、これが潰れてできたものと考えられている。c-type転位ループが観察される燃焼度とほぼ同時にZry-2の水素吸収量が急激に増加し、水素脆化が誘起されることが確認されている。これらを踏まえると、c-type転位ループの形成過程を解明することは、水素脆化を抑制した材料設計のためにも重要な研究課題と位置付けられる。本研究では、機械学習ポテンシャル(MLP)を用いた分子動力学(MD)法により、c-type転位ループ形成過程を原子レベルの挙動から解明することを目的とする。

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