| Abstract Scope |
The recently developed physically-informed neural network (PINN) method permits the construction of interatomic potentials fitted to large DFT databases with nearly-DFT accuracy and significantly improved transferability in comparison with the existing machine-learning potentials. The superior transferability is due to the underlying analytical bond-order atomic interaction model which properly captures the nature of interatomic bonding in both metallic and covalent systems. We present a new PINN potential for tantalum, which shows excellent accuracy for many different environments including a variety of crystal structures, vacancies, self-interstitials, generalized stacking faults, grain boundaries, surfaces, clusters, deformation paths, highly compressed states occurring in shock wave experiments, and liquid. The transferability of the new potential is demonstrated by application to the dislocation core structure, dislocation glide motion, and a number of other cases. |