Deformation mechanisms and compressive response of NbTaTiZr alloy via machine learning potentials

TL;DR

This study develops a machine learning potential to simulate and analyze the deformation mechanisms and mechanical behavior of NbTaTiZr refractory alloy under various conditions, revealing anisotropic properties and strain-rate effects.

Contribution

The paper introduces a novel ML potential for NbTaTiZr and systematically investigates its deformation mechanisms using MD simulations, highlighting the effects of orientation, strain rate, temperature, and composition.

Findings

NbTaTiZr exhibits structural and mechanical anisotropy in compression.

High strain rates increase yield strength and promote disordering.

Higher Nb/Ta content enhances yield strength, while Ti/Zr reduce it.

Abstract

Refractory multi-principal element alloys (MPEAs) are key research focus for excellent high-temp properties and engineering potential. Deformation mechanisms/mechanical behaviors of quaternary NbTaTiZr MPEA under high strain rates/extreme temps remain unclear. We built a variable-composition ML potential for NbTaTiZr, combined with MD simulations to study effects of crystal orientation, strain rate, temp, composition on compressive mechanics. NbTaTiZr shows structural/mechanical anisotropy in compression [111] max yield strength, [110] min (prone to twinning), [100] via local disorder/dislocation slip (dominant 1/2<111> dislocations). At 10^10 s^-1, yield strength rises sharply, disordered structures increase; high strain rates suppress dislocations to promote disordering. Retains high strength at 2100 K. Higher Nb/Ta boosts yield strength, Ti/Zr reduce it. Reveals MPEA mechanical anisotropy and strain-rate-dependent disordering, guiding high-performance refractory alloy design.