  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.
