Nanotechnologies / Materials
Hydrogen charging stations, Hydrogen-fueled cars, Ultra-strength structural materials

Atomistic study of hydrogen effects on materials strength
Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science
Full Professor Shigenobu Ogata
Researchmap https://researchmap.jp/read0185246

Nanotechnologies / Materials

Abstract
Hydrogen embrittlement has been known for a long time, and thephenomenon is particularly pronounced in advanced high-strength
materials. In this study, we analyze the behavior of hydrogen atoms
in materials undergoing deformation and fracture, which are extremely difficult to observe experimentally, by means of high-precision atomic simulations (molecular dynamics simulations) to clarify
the atomistic details of the mechanism. Using neural networks, wehave solved the problem of accuracy in the atomic interactions
used in the atomic simulations, which have been the bottleneck fora long time in these simulations. We have made it possible to perform atomic-level simulations with unprecedented accuracy andscale.

▍ Background & Results
Hydrogen embrittlement is an essential issue for the safe handling of hydrogen. Although many experimental and simulation
studies have been carried out for more than half a century, they
have remained unachieved due to the difficulty of direct observation of hydrogen and the difficulty of atomic simulations for thedescription of atomic interactions. In our study, the developmentof high-precision atomic interactions based on neural network has
removed the bottleneck, solving the long-standing problem in hydrogen embrittlement research. In the future, we expect to establish material design guidelines for hydrogen-resistant materials andaccelerate the development of a high performance hydrogen-resistant materials.

L. Wan, S. Ogata et al., Int. J. Plasticity (2019)▍ Significance of the research and Future perspective
The hydrogen embrittlement phenomenon has been studied
for more than half a century and a number of theories have been
proposed. However, the mechanism of hydrogen embrittlement
remains unclear due to the difficulty in directly observing the hydrogen in materials. So far, research on hydrogen embrittlement has
been conducted both experimentally and through atomic-level simulations. In experiments, the mechanism was inferred based on indirect data collected because direct observation of hydrogen is notpossible. On the other hand, atomic simulations, such as moleculardynamics, can directly analyze the behavior of hydrogen in materials. Still, there was a problem in the accuracy and the computational cost for the expression of the atomic interactions, In this study,
we have developed a neural network atomic interactions that iscapable of machine learning the results of high-precision first-principles calculations to enable large-scale analysis while maintaining
high accuracy, thereby allowing us to track the behavior of hydrogen with unprecedented accuracy. Through this research, thespecific mechanism by which hydrogen strongly interacts with defects in materials, thereby accelerating defect motion and growth
in materials undergoing the deformation and the fracture, has beenclarified at the atomic-evel.

F. Meng, S. Ogata et al., Phys. Rev. Mater. (2021)

P a t e n t
Wan, Liang; Geng, Wen Tong; Ishii, Akio et al. Hydrogen embrittlement controlled by reaction of dislocation with grain boundary in alpha-iron. International
Journal of Plasticity. 2019; 112: 206-219. doi: 10.1016/j.ijplas.2018.08.013
Meng, Fan-Shun; Du, Jun-Ping; Shinzato, Shuhei et al. A General-Purpose Neural Network Interatomic Potential for α-iron and Hydrogen Binary System.
Tr e a t i s e
Physical Review Materials. 2021; (5): 113606-1-16. doi: 10.1103/PhysRevMaterials.5.113606
Kimizuka, Hajime; Ogata, Shigenobu; Shiga, Motoyuki Unraveling anomalous isotope effect on hydrogen diffusivities in fcc metals from first principles
including nuclear quantum effects. Physical Review B. 2019; 100(2): 024104-1-9. doi: 10.1103/PhysRevB.100.024104
URL https://tsme.me.es.osaka-u.ac.jp/publications.html
K e y w o r d hydrogen embrittlement, deformation and fracture, atomic simulation, machine learning