  This study reports grain boundary (GB) energy calculations for 46
symmetric-tilt GBs in alpha-iron using molecular mechanics based on an
artificial neural network (ANN) potential and compares the results with
calculations based on the density functional theory (DFT), the embedded atom
method (EAM), and the modified EAM (MEAM). The results by the ANN potential are
in excellent agreement with those of the DFT (5% on average), while the EAM and
MEAM significantly differ from the DFT results (about 27% on average). In a
uniaxial tensile calculation of Sigma 3 (1-12) GB, the ANN potential reproduced
the brittle fracture tendency of the GB observed in the DFT while the EAM and
MEAM showed mistakenly showed ductile behaviors. These results demonstrate the
effectiveness of the ANN potential in grain boundary calculations of iron as a
fast and accurate simulation highly in demand in the modern industrial world.
