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Mori, Hideki*; Tsuru, Tomohito; Okumura, Masahiko; Matsunaka, Daisuke*; Shiihara, Yoshinori*; Itakura, Mitsuhiro
Physical Review Materials (Internet), 7(6), p.063605_1 - 063605_8, 2023/06
Times Cited Count：0 Percentile：0(Materials Science, Multidisciplinary)The introduction of obstacles (e.g., precipitates) for controlling dislocation motion in molecular structures is a prevalent method for designing the mechanical strength of metals. Owing to the nanoscale size of the dislocation core (
1 nm), atomic modeling is required to investigate the interactions between the dislocation and obstacles. However, conventional empirical potentials are not adequately accurate, in contrast to the calculations based on density functional theory (DFT). Therefore, the atomic-level details of the interactions between the dislocations and obstacles remain unclarified. To this end, this study applied an artificial neural network (ANN) framework to construct an atomic potential by leveraging the high accuracy of DFT. Using the constructed ANN potential, we investigated the dynamic interaction between the 
edge dislocation and obstacles in BCC iron. When the dislocation crossed the void, an ultrasmooth and symmetric half-loop was observed for the bowing-out dislocation. Except for the screw dislocation, the Peierls stress of all the dislocations predicted using the ANN was less than 100 MPa. More importantly, the results confirmed the formation of an Orowan loop in the interaction between a rigid sphere and dislocation. Furthermore, we discovered a phenomenon in which the Orowan loop disintegrated into two small loops during its interaction with the rigid sphere and dislocation.
Shiihara, Yoshinori*; Kanazawa, Ryosuke*; Matsunaka, Daisuke*; Lobzenko, I.; Tsuru, Tomohito; Koyama, Masanori*; Mori, Hideki*
Scripta Materialia, 207, p.114268_1 - 114268_4, 2022/01
Times Cited Count：12 Percentile：73.14(Nanoscience & Nanotechnology)This study reports grain boundary (GB) energy calculations for 46 symmetric-tilt GBs in 
-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 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.
Tsuru, Tomohito; Kaji, Yoshiyuki; Matsunaka, Daisuke*; Shibutani, Yoji*
Physical Review B, 82(2), p.024101_1 - 024101_6, 2010/07
Times Cited Count：31 Percentile：74.65(Materials Science, Multidisciplinary)An incipient plastic deformation of several types of grain boundaries subjected to nanoindentation was investigated by atomistic simulations. Crystal defects such as grain boundaries undermine the nucleation resistance. In this paper, we examined the dislocation nucleation mechanism at the twin and several coincidence site lattice grain boundaries and the resulting weakening of the dislocation nucleation resistance. We found that for the twin and the relatively stable ![$$Sigma 11(bar{1}13)[110]$$](/search/images/EROFG0LHNVQSAMJRFBOGEYLSPMYX0MJTFFNTCMJQLUSA.gif)
grain boundary the primary slip deformation is activated on the grain boundary plane prior to the defect-free region because of the low fault energy of the grain boundaries during slip deformation. Subsequently, the secondary slip is activated from the grain boundary. On the other hand the dislocation is initially generated from the heterogeneous grain boundary plane for the unstable high-energy grain boundaries.
Mori, Hideki*; Itakura, Mitsuhiro; Okumura, Masahiko; Shiihara, Yoshinori*; Matsunaka, Daisuke*
no journal, ,
no abstracts in English
Lobzenko, I.; Shiihara, Yoshinori*; Mori, Hideki*; Matsunaka, Daisuke*; Tsuru, Tomohito
no journal, ,
Recently methods of machine learning have become an important part of materials science. Particularly, interatomic potentials built using such methods demonstrate accuracy of geometrical characteristics of materials approaching the accuracy of first-principle calculations. In our work we use artificial neural networks to build potentials, and therefore they are called ANN potentials. We focus on the atomic stress, one of the important properties of materials, which can be calculated in classical approximation. Analysis of microscopic stress can be applied to any type of nonuniform system (such as defects in bulk, two-dimensional crystals, molecules assemblies, etc.). We show how the central-force decomposition (CFD) scheme can be used in the framework of ANN potentials for the derivation of atomic stress tensor. It is important to use CFD due to the fact that the symmetry of the stress tensor may be broken in other schemes. Finally, we calculate atomic stress distributions near surfaces of pure Al with different orientations. It is known from first-principle studies that there is a charge oscillations near Al surfaces, however it cannot be captured by existing interatomic potentials. Our results, obtained with the new ANN potential, show oscillations of atomic stress near Al surface. Even though our potential was fitted to only energies of Al structures (calculated in quantum-mechanics approximation), we attribute the atomic stress oscillations to the charge distribution of the real system. Charge oscillations are affecting total energies of structures, and therefore are implicitly included in the data set, which we use for building the potential.
Lobzenko, I.; Shiihara, Yoshinori*; Mori, Hideki*; Matsunaka, Daisuke*; Tsuru, Tomohito
no journal, ,
Refractory multi-component alloys (MCA) form an important class of materials with high potential for use in severe conditions. One of the main problems hindering the application of these alloys is the low ductility inherited from the body-centred cubic (BCC) crystal structure. Dislocation motion is the factor significantly influencing the ductility of the material, so a comprehensive understanding of the dislocation dynamics in refractory MCAs should be achieved to pave the way for designing refractory alloys with increased ductility. To achieve high accuracy in classical molecular dynamics simulations of dislocation motion, we apply the technique of machine learning (ML) for interatomic potential development. It is known that alloys with hexagonal closed-packed (HCP) elements such as Zr exhibit higher ductility, which is why two medium-entropy alloys, MoNbTa and ZrNbTa, were chosen to study the influence of elements' constitution on dislocations dynamics. The inter-atomic potentials for MCAs built using ML need a specific dataset. In the process of the potential development, we identify which structures contribute to a better quality of materials' mechanical properties prediction by the potentials. Results of the simulations have shown qualitative and quantitative differences between the two alloys under study. One example of that difference can be seen in the shapes of the screw dislocation core. In contrast to MoNbTa, ZrNbTa demonstrates a non-compact core with an extension on a (110) plane.
