Speaker
Description
The development of alloys with the necessary properties is one of the main objects of research in materials science. The phase structure of the material is the important characteristics of this process. The presence of secondary phases might change the mechanical properties of the materials. Therefore, the formation of ordered structures should be meticulously investigated for a safe and reliable usage.
The development of the alloy is carried out mainly by trial and error. This significantly increases the cost and time of the work. In addition, this approach is very limited for multicomponent alloys due to the dimensionality of the search space. Computational methods can reduce the number of experiments, but they are still time-consuming.
In the present work, a novel rapid approach to the detection of ordered structures in multicomponent alloys is proposed. The method relies on simulations performed using novel high-precision interatomic potentials [1] and atomistic Monte-Carlo method. The simulation results are then passed to a machine-learning algorithm that guides automatic search of ordered structures in the system. This approach was successfully tested on the example of the well-studied binary systems AuCu and FeNi and applied to the search for ordered phases in the CoCrFeNi high-entropy alloy, known as a solid solution. As a result, it was predicted that the Ni-Cr ordering could form in this alloy. This conclusion was further supported by DFT-based calculations using the USPEX evolutionary structure predictor code.
References:
[1] Shapeev, A. Accurate representation of formation energies of crystalline alloys with many components // Comput. Mater. Sci. – 2017 – Vol. 139 – p. 26-30
| Speaker Country | Russia |
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