Multi-scale Investigation of Chemical Short-Range Order and Dislocation Glide in the MoNbTi and TaNbTi Refractory Multi-Principal Element Alloys

Abstract

Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of chemical short-range ordering (CSRO) on dislocation glide in two model RMPEAs - TaNbTi and MoNbTi - using a multi-scale modeling approach. A highly accurate machine learning interatomic potential was developed for the Mo-Ta-Nb-Ti system and used to demonstrate that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFE). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs, thereby increasing the stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress, with higher SRO requiring higher applied stresses to achieve smooth screw dislocation glide.

References in corpus (12)

• The Alloy Theoretic Automated Toolkit: A User Guide
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Verification of Short-Range Order and Its Impact on the Properties of the CrCoNi Medium Entropy Alloy
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Tunable stacking fault energies by tailoring local chemical order in CrCoNi medium-entropy alloys
• Strengthening in multi-principal element alloys with local-chemical-order roughened dislocation pathways
• Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
• Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials
• Phase stability of ternary fcc and bcc Fe-Cr-Ni alloys
• Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
• Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
• A 3D phase field dislocation dynamics model for body-centered cubic crystals

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