English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We employ a machine-learning force field, trained by a neural network (NN)with bispectrum coefficients as descriptors, to investigate the short-rangeorder (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NNinteratomic potential provides a transferable force field with densityfunctional theory accuracy. This novel NN potential is applied to elucidate theSRO effects on the elasticity, vibrational modes, plasticity, and strength inthe NbMoTaW multi-principal element alloy (MPEA). The results show the strongattraction among Mo-Ta pairs forming the local ordered B2 structures, whichcould be tuned via temperature and improved by Nb content. SRO increases theelastic constants and high-frequency phonon modes as well as introduces extralattice friction of dislocation motion. This approach enables a rapidcompositional screening, paves the way for computation-guided materials designof new MPEAs with better performance, and opens avenues for tuning themechanical properties by processing optimization.

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