Accurate interatomic potential for the nucleation in liquid Ti-Al binary alloy developed by deep neural network learning method

B Zhai and HP Wang, COMPUTATIONAL MATERIALS SCIENCE, 216, 111843 (2023).

DOI: 10.1016/j.commatsci.2022.111843

For the traditional empirical potentials, the accuracy of chemical bonding in a wide temperature range is regrettable, and thereby they are not an appropriate choice to explore the nucleation process of crystals. As a developed superalloy in aerospace, the liquid structure and nucleation mechanism of Ti-Al alloy desire to be investigated to comprehend the solidification process deeply. This work develops an accurate deep neural network (DNN) potential for Ti-Al binary system by employing a machine-learning method. Then, the local structure and the nucleation process of liquid Ti-48 %Al and Ti-52 %Al (atomic percentage) alloys from the normal to the undercooled state are performed by molecular dynamics simulation with the DNN potential. It is found that the formation of the locally ordered structures depends on the Ti-Al atom pair during the solidifi-cation. The Honeycutt-Andersen indices of 1431,1541 and 1551 are dominant, and the liquid structure is mainly characterized by perfect and distorted icosahedral clusters. By Voronoi Polyhedron (VP) analysis, Ti-centered VPs favor the formation of body- centered-cubic (BCC) structure, while Al-centered VPs have the main contri-bution to the icosahedral structure. Besides, liquid Ti-52 %Al alloy prefers to form face-centered-cube (FCC) nuclei than BCC nuclei in a high undercooling, which is closely related to the ability to capture Ti-Al bonds. The favor of the nucleation of the gamma phase causes the nuclei with non-equilibrium composition, comprised of Ti-rich BCC and Al-rich FCC coordinated atoms, respectively. Novelty, the FCC nuclei in liquid Ti-Al alloys are inde-pendently generated without the precursor of BCC coordinated atoms. High undercooling can transform the nucleation mechanism from two steps to one step.

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