Artificial neural network molecular mechanics of iron grain boundaries

Y Shiihara and R Kanazawa and D Matsunaka and I Lobzenko and T Tsuru and M Kohyama and H Mori, SCRIPTA MATERIALIA, 207, 114268 (2022).

DOI: 10.1016/j.scriptamat.2021.114268

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 (1) over bar2) GB, the ANN potential reproduced the brittle fracture tendency of the GB observed in the DFT while the EAM and MEAM mistakenly showed ductile behaviors. These results demonstrate the effectiveness of the ANN potential in calculating grain boundaries of iron, which is in high demand in modern industry. (C) 2021 The Author(s). Published by Elsevier Ltd on behalf of Acta Materialia Inc.

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