Refine your search�ソスF     
Report No.
 - 

Dislocations shape and dynamics in BCC medium entropy alloys; Classical modelling with machine learning potentials

Lobzenko, I.   ; Shiihara, Yoshinori*; Tsuru, Tomohito   

High-entropy alloys (HEA) are excellent structural materials due to their promising mechanical properties. Works on body-centered cubic (BCC) HEAs show increased ductility if group 4 elements are present in the composition. Theoretical studies of that effect by first-principles modeling are complicated by the essential randomness of HEA atomic structure, which requires large systems. To achieve high accuracy in classical molecular dynamics we have developed interatomic potentials using machine learning of artificial neural networks (we refer to them as ANN potentials). We present in the current work results for two medium-entropy alloys (MEA): MoNbTa and ZrNbTa. Comparison of basic mechanical properties show decrease of bulk modulus and elastic constants if Mo is substituted with group 4 element Zr. Edge and screw dislocations are studied. Classical modelling allows construction of big calculation cells, that prevents self-interaction of the dislocation core due to long-range stress field. Moreover, big cells ensures better randomness of alloys, which is vital in simulations of HEA and MEA mechanical properties. Screw dislocation movement is induced by applying shear strain. In case of edge dislocation the shape and energy is studied in the process of migration of the dislocation core between two adjacent easy core configurations. In this way the Peierls barrier is calculated. Results for two MEA are compared to elucidate the role of group 4 element. Finally, to understand the stress field of dislocations we employ atomic stress calculation scheme in the framework of ANN potentials. Atomic stress calculations is possible based on virial stress definition due to the fact that atomic energy in the ANN scheme ultimately depends on pair distances between atoms.

Accesses

:

- Accesses

InCites™

:

Altmetrics

:

[CLARIVATE ANALYTICS], [WEB OF SCIENCE], [HIGHLY CITED PAPER & CUP LOGO] and [HOT PAPER & FIRE LOGO] are trademarks of Clarivate Analytics, and/or its affiliated company or companies, and used herein by permission and/or license.