Origin of the yield stress anomaly in L12 intermetallics unveiled with physically-informed machine-learning potentials

TL;DR

This study uses advanced machine-learning potentials to simulate dislocation behavior in L12 intermetallics, revealing the temperature-dependent nature of the Kear-Wilsdorf lock and explaining the yield stress anomaly.

Contribution

It introduces a physically-informed machine-learning potential for accurate dislocation modeling and develops a phenomenological model for unlocking stresses in L12 intermetallics.

Findings

Dislocation cross-slip and KWL formation are observed in simulations.

Unlocking stress varies significantly with temperature.

The phenomenological model applies broadly to L12 intermetallics.

Abstract

The yield stress anomaly of L12 intermetallics such as Ni3Al or Ni3Ga is controlled by the so-called Kear-Wilsdorf lock (KWL), of which the formation and unlocking are governed by dislocation cross-slip. Despite the importance of L12 intermetallics for strengthening Ni-based superalloys, microscopic understanding of the KWL is limited. Here, molecular dynamics simulations are conducted by employing a dedicated machine-learning interatomic potential derived via physically-informed active-learning. The potential facilitates modelling of the dislocation behavior in Ni3Al with near ab initio accuracy. KWL formation and unlocking are observed and analyzed. The unlocking stress demonstrates a pronounced temperature dependence, contradicting the assumptions of existing analytical models. A phenomenological model is proposed to effectively describe the atomistic unlocking stresses and extrapolate them to the macroscopic scale. The model is general and applicable to other L12 intermetallics. The acquired knowledge of KWLs provides a deeper understanding on the origin of the yield stress anomaly.