  Large-scale atomistic simulations of materials heavily rely on interatomic
potentials, which predict the system energy and atomic forces. One of the
recent developments in the field is constructing interatomic potentials by
machine-learning (ML) methods. ML potentials predict the energy and forces by
numerical interpolation using a large reference database generated by
quantum-mechanical calculations. While high accuracy of interpolation can be
achieved, extrapolation to unknown atomic environments is unpredictable. The
recently proposed physically-informed neural network (PINN) model significantly
improves the transferability by combining a neural network regression with a
physics-based bond-order interatomic potential. Here, we demonstrate that
general-purpose PINN potentials can be developed for body-centered cubic (BCC)
metals. The proposed PINN potential for tantalum reproduces the reference
energies within 2.8 meV/atom. It accurately predicts a broad spectrum of
physical properties of Ta, including (but not limited to) lattice dynamics,
thermal expansion, energies of point and extended defects, the dislocation core
structure and the Peierls barrier, the melting temperature, the structure of
liquid Ta, and the liquid surface tension. The potential enables large-scale
simulations of physical and mechanical behavior of Ta with nearly
first-principles accuracy while being orders of magnitude faster. This approach
can be readily extended to other BCC metals.
