氏名： Shihao Zhang, Fanshun Meng, Junping Du, Dan Wei, Md. Hossain Rana, Heting Liao, Yangen Li, Yujie Jia, Shuhei Shinzato and Shigenobu Ogata

所属： Osaka University

概要：Artificial neural network potentials (NNPs) have emerged as effective tools for understanding atomic interactions at the atomic scale in various phenomena. Recently, we developed highly transferable NNPs for α-iron and α-iron/hydrogen binary systems (Physical Review Materials 5 (11), 113606, 2021). These potentials allowed us to investigate deformation and fracture in α-iron under the influence of hydrogen. However, the computational cost of the NNP remains relatively high compared to empirical potentials, limiting their applicability in addressing practical issues related to hydrogen embrittlement. In this work, building upon our prior research on iron-hydrogen NNP, we developed a new NNP that not only maintains the excellent transferability but also significantly improves computational efficiency (more than 40 times faster). We applied this new NNP to study the impact of hydrogen on the cracking of iron and the deformation of polycrystalline iron. We employed large-scale through-thickness {110}〈110〉 crack models and large-scale polycrystalline α-iron models. The results clearly show that hydrogen atoms segregated at crack tips promote brittle-cleavage failure followed by crack growth. Additionally, hydrogen atoms at grain boundaries facilitate the nucleation of intergranular nanovoids and subsequent intergranular fracture. We anticipate that this high-efficiency NNP will serve as a valuable tool for gaining atomic-scale insights into hydrogen embrittlement.

 

論文掲載,発表実績:
（学術雑誌掲載論文）

• Shihao Zhang, Fanshun Meng, Rong Fu, Shigenobu Ogata, “Highly efficient and transferable interatomic potentials for α-iron and α-iron/hydrogen binary systems using deep neural networks”, Computational Materials Science, Vol.215, pp.112843, 2024.