The Hierarchical Potential Energy Landscape of Screw Dislocation Motion in Refractory High-entropy Alloys

Abstract

High-entropy alloys (HEAs) with concentrated solid solutions are conceived to possess a rugged atomic and energy landscape in which dislocation motion necessarily proceeds to accommodate mechanical deformation. Fundamental questions remain as to how rough the energy landscape is and to what extent it can be influenced by the local ordering of their constituent elements. Here, we construct and report the potential energy landscape (PEL) governing screw dislocation motion in refractory HEAs that reveals a hierarchical and multilevel structure with a collection of small basins nested in large metabasin. This striking feature pertaining to HEAs exerts a trapping force and back stress on saddle point activations, retarding dislocation movement. By introducing chemical short-range order, the energy landscape is smoothed but skewed to different degrees that shifts the rate-liming process from kink-glide to kink-pair nucleation. The chemical disorder-roughened PEL in HEAs, analogous to structural disorder induced in metallic glasses, signifies the role of various barrier-hopping processes underlying the extraordinary mechanical behaviors of the two distinct groups of materials.

References in corpus (5)

• Verification of Short-Range Order and Its Impact on the Properties of the CrCoNi Medium Entropy Alloy
• 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
• Mechanistic origin of high retained strength in refractory BCC high entropy alloys up to 1900K
• A scalable parallel Monte Carlo algorithm for atomistic simulations of precipitation in alloys

Cited by in corpus (4)

• The origin of jerky dislocation motion in high-entropy alloys
• Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential
• Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys
• Deformation Mechanisms in High Entropy Alloys: A Minireview of Short-Range Order Effects