Artificial neural network molecular mechanics of iron grain boundaries. (15th January 2022)

• Record Type: Journal Article
• Authors: Shiihara, Yoshinori; Kanazawa, Ryosuke; Matsunaka, Daisuke; Lobzenko, Ivan; Tsuru, Tomohito; Kohyama, Masanori; Mori, Hideki
• Is Part Of: Scripta materialia. Number 207(2022)
• Publication Date: 2022-01-15
• Subjects: Grain boundary -- Artificial neural network potential -- Uniaxial tension -- Iron -- Molecular mechanics; Materials -- Periodicals; Metallurgy -- Periodicals; Metalen; Legeringen; Materiaalkunde; Metals, metalworking and machinery industries; Metals; Electronic journals; 620.11
• DOI: 10.1016/j.scriptamat.2021.114268
• Languages: English
• ISSNs: 1359-6462

Abstract

Graphical abstract: Abstract: This study reports grain boundary (GB) energy calculations for 46 symmetric-tilt GBs in α -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 ∑ 3 ( 1 1 ¯ 2 ) 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.