About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Energy Technology 2026: Advancement in Energy Materials - Theory, Simulation, Characterization, Application
|
| Presentation Title |
Design and Optimization of Hydrogen-Resistant Steels Based on First-Principles Calculations and Machine Learning |
| Author(s) |
Zhishan Mi, Yaqing Hou, Guocai Chai |
| On-Site Speaker (Planned) |
Zhishan Mi |
| Abstract Scope |
The hydrogen embrittlement (HE) issue in ultra-high-strength steels has always been a hot topic of concern for researchers, particularly regarding the unclear influence of alloying elements on HE resistance. In this study, a high-precision machine learning force field (MLFF) for the Fe-C-H system was constructed by combining first-principles calculations and crystal structure prediction (CSP) with machine learning. Using this MLFF, molecular dynamics simulations were performed to investigate the diffusion behavior of hydrogen atoms in steels with different carbon contents, and the hydrogen diffusion coefficients were calculated. It was found that the hydrogen diffusion coefficient generally decreased with increasing carbon content, in good agreement with experimental results. The algorithm model established in this study can analyze the influence of carbon content on the hydrogen resistance of iron and steel materials, which is of significant importance for studying hydrogen-induced damage in steel materials and composition design. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Iron and Steel, Modeling and Simulation |