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Building robust interatomic potentials for BCC medium-entropy alloys using artificial neural networks

Lobzenko, I.   ; Tsuru, Tomohito   

Excellent mechanical properties of high-entropy alloys (HEA), such as increased strength and high ductility, have recently became a subject of extensive studies. First-principles modeling of HEA is complicated by the essential randomness of the atomic structure, which requires large systems. Therefore, classical molecular dynamics is one of the best tools for studying mechanical properties of HEAs. On the other hand, there is a lack of interatomic potentials due to rather low accuracy of embedded atom type potentials in the case of alloying of many elements in close concentrations. That is why a relatively new approach based on artificial neural networks should be employed to build interatomic potentials for such materials as HEA. Current work discusses two new potentials for medium entropy ternary alloys MoNbTa and ZrNbTa. The technique of machine learning allows effective fitting of the data set calculated using the quantum mechanics approach. We have verified the quality of our potentials by comparing elastic constants values with results of first-principles modeling. Comparing two alloys we found that bulk modulus and elastic constants become smaller if Mo is substituted with Zr. Also, the change in C11 and C12 components show that the material comes closer to the elastic instability region.

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