About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Computing Grain Boundary Diagrams |
Author(s) |
Chongze Hu, Jian Luo |
On-Site Speaker (Planned) |
Chongze Hu |
Abstract Scope |
The grain boundary (GB) counterparts to bulk phase diagrams are important materials science tool to optimize the properties of polycrystalline materials. In this talk, we will first review (i) an atomistic simulation study to construct GB diagrams of Si-Au system and (ii) a data-driven study to predict the GB diagrams of Cu-Ag system as a function of GB five crystallographic degrees of freedom (DOFs) by using a genetic algorithm-guided deep learning technique. Second, we will discuss our recent study to compute GB mechanical properties diagrams for a classical GB embrittlement system Al-Ga alloy. Finally, we will discuss how to use machine learning techniques to predict GB diagrams of high-entropy Cantor alloys (CrMnFeCoNi) as a function of temperature and four independent compositional DOFs in 5-D space. The coupling effect between GB segregation of multiple element and disordering in HEA will be discussed along with a surrogate data-based analytical model (DBAM). |
Proceedings Inclusion? |
Planned: |
Keywords |
Modeling and Simulation, Machine Learning, Other |