.png){kind=icon}
.png){kind=icon}
Return
Grain boundary structure search by using an evolutionary algorithm with effective mutation methods
.png){kind=icon}
OA
AI
DOI：10.1016/j.commatsci.2020.109812.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Grain boundaries (GBs) accommodate the misorientation between adjacent grains in a polycrystalline material. GBs are geometrically described by the macroscopic and microscopic degrees of freedom. Besides, at the atomistic level, GBs exhibit complicated behaviors under varying thermodynamic conditions. The complexity of atomistic GB structures demands stochastic searching for possible states. The effectiveness of stochastic search methods relies on techniques to recreate and select atomistic structures. In this work, we developed a new mutation operator that can induce direct and collective atomistic structure changes to boost the search efficiency of exploring GB structures with evolutionary algorithms (EA). We implemented the mutation methods along with innovative selection, crossover, boundary condition preprocessing methods to form an EA-based package to explore GB structures in grand canonical ensembles with atomistic simulations. We used this package to study the [001] symmetric tilt grain boundaries (STGBs) in FCC copper (Cu), the [110] STGBs in BCC tungsten (W), and the [1 (2) over bar 10] STGBs in HCP magnesium (Mg). The results show that our design and implementation based on new mutation procedures, selection, and boundary conditions provide a high-quality search of atomistic GB structures in the grand canonical ensemble for different crystal lattices.
Keywords:
Grain boundary structures
Evolutionary algorithm
Metals
Atomistic simulations
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
1.3W
Citations:
3.6W