  BCC transition metals (TMs) exhibit complex temperature and strain-rate
dependent plastic deformation behaviour controlled by individual crystal
lattice defects. Classical empirical and semi-empirical interatomic potentials
have limited capability in modelling defect properties such as the screw
dislocation core structures and Peierls barriers in the BCC structure. Machine
learning (ML) potentials, trained on DFT-based datasets, have shown some
successes in reproducing dislocation core properties. However, in group VB TMs,
the most widely-used DFT functionals produce erroneous shear moduli C44 which
are undesirably transferred to machine-learning interatomic potentials, leaving
current ML approaches unsuitable for this important class of metals and alloys.
Here, we develop two interatomic potentials for BCC vanadium (V) based on (i)
an extension of the partial electron density and screening parameter in the
classical semi-empirical modified embedded-atom method (XMEAM-V) and (ii) a
recent hybrid descriptor in the ML Deep Potential framework (DP-HYB-V). We
describe distinct features in these two disparate approaches, including their
dataset generation, training procedure, weakness and strength in modelling
lattice and defect properties in BCC V. Both XMEAM-V and DP-HYB-V reproduce a
broad range of defect properties relevant to plastic deformation and fracture.
In particular, XMEAM-V reproduces nearly all mechanical and thermodynamic
properties at DFT accuracies and with C44 near experimental value. XMEAM-V also
naturally exhibits the anomalous slip at 77 K widely observed in group VB and
VIB TMs and outperforms all existing, publically available interatomic
potentials for V. The XMEAM thus provides a practical path to developing
accurate and efficient interatomic potentials for nonmagnetic BCC TMs and
possibly multi-principal element TM alloys.

Materials Science