Speaker: Shigenobu Ogata, Osaka University, Japan

Time: 15:00 – 16:30 p.m., Sep 2, 2024, GMT+8

Venue: 3048, Floor 3, ENN Engineering Building, PKU

Abstract: 

The study of hydrogen's behavior and its impact on deformation and fracture in metals has been extensive, with numerous models and theories developed. However, direct observation of hydrogen in materials remains challenging, leaving many questions unanswered. Atomistic simulation emerges as a valuable tool for directly investigating hydrogen behaviors and their effects on deformation and fracture. The effectiveness of these simulations largely depends on accurately describing atomic interactions within the iron hydrogen system. Recent advancements include the proposal of artificial neural network (ANN) atomic interactions for the iron-hydrogen binary system, trained using a dataset based on Density Functional Theory (DFT) calculations of energy, force, and structure. These ANN atomic interactions combine the computational efficiency of empirical models with the accuracy and transferability of DFT, allowing for a quantitative elucidation of phenomena contributing to hydrogen embrittlement. This has led to a clearer understanding of how hydrogen influences vacancy movement, dislocation activities, and the mechanisms behind crack formation at both grain boundaries and within grains.

Source: College of Engineering, PKU