Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential

TL;DR

This study uses a neural network-based machine-learning potential to explore how short-range atomic order affects the mechanical and vibrational properties of the NbMoTaW BCC alloy, enabling efficient materials design.

Contribution

It introduces a transferable neural network interatomic potential trained with bispectrum descriptors for accurate simulation of NbMoTaW alloy properties.

Findings

Mo-Ta pairs form local B2 structures influenced by temperature and Nb content.

SRO increases elastic constants and phonon frequencies.

SRO affects dislocation mobility and alloy strength.

Abstract

We employ a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the short-range order (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NN interatomic potential provides a transferable force field with density functional theory accuracy. This novel NN potential is applied to elucidate the SRO effects on the elasticity, vibrational modes, plasticity, and strength in the NbMoTaW multi-principal element alloy (MPEA). The results show the strong attraction among Mo-Ta pairs forming the local ordered B2 structures, which could be tuned via temperature and improved by Nb content. SRO increases the elastic constants and high-frequency phonon modes as well as introduces extra lattice friction of dislocation motion. This approach enables a rapid compositional screening, paves the way for computation-guided materials design of new MPEAs with better performance, and opens avenues for tuning the mechanical properties by processing optimization.