Revealing atomic-scale distributions of energy and stress in defective or complex systems, based on the behavior of electrons, should contribute much to materials science and engineering, while only few practical ab initio methods were developed for this purpose. Thus, we developed computational techniques of local-energy and local-stress calculations within the plane-wave PAW (projector augmented wave)-GGA (generalized gradient approximation) framework. This is natural extension of ab initio energy-density and stress-density schemes, while the inherent gauge dependency is removed by integrating these densities in each local region where the contained gauge-dependent terms are integrated to be zero. In this overview, we explain our scheme with some details and discuss the concepts or physical meanings of local energy and local stress via the comparison with related schemes using similar densities, LCAO (linear combination of atomic orbitals) methods, Green’s function formulation implemented by multiple scattering or TB (tight-binding) methods, or EAM (embedded-atom method) potentials. We present recent applications to metallic surfaces, grain boundaries (GBs) with and without solute segregation, tensile tests of metallic GBs, local elastic constants of microstructures in alloys, and machine-learning based GB-energy prediction, where the local-energy and local-stress analyses provide novel aspects of phenomena, deep insights into the mechanism, and effective data for novel machine-learning based modelling. We discuss unsettled issues and future applications, especially for large-scale metallic systems.

Mater. Trans. 62 (2021) 1-15に掲載

The effect of electropolishing conditions on the outgassing properties of austenitic stainless steel (SUS316L) electropolished by a wiping method (WiEP) and an immersion method (ImEP) was investigated using thermal desorption spectroscopy. In the case of electropolishing by the WiEP in which the metal surface was wiped with an electrolyte-solution-impregnated felt wiper cathode, the SUS316L surface became smoother and the amount of outgassing decreased as the applied voltage (4-8 V) increased. In the case of electropolishing by the ImEP, the SUS316L surface became smoother and the amount of outgassing decreased as the current density (8-15 A dm⁻²) increased. After electropolishing by the WiEP at higher applied voltages and the ImEP at higher current densities, diffusible hydrogen desorbed from the SUS316L surface below 723 K was almost completely removed, however most of the non-diffusible hydrogen remained. These results suggest that the migration of diffusible hydrogen to the SUS316L surface is enhanced by the increase in the applied voltage of electropolishing.

In the present study, a layer type neural network computation was applied to estimate the standard entropy of binary solid oxides, sulfides and halides. Independent variables to influence the thermodynamics property associated with dispersion or randomness in the crystals were used as input parameters for the calculation. 325 substances involving 12 input parameters were applied to the calculation. The regression computation enabled reproduction of training data cited in learning process and prediction of test data not used in the learning process with high accuracy.

In addition, the contribution of each input property to the estimation of the standard entropy was also evaluated. It was found that the volume and the weight per a composition had positive impacts, and the atomic weight and the orbital radius of an anion had negative impacts. Furthermore, it was suggested that coordination numbers of composition elements have little effect on the precision of reproduction and prediction of the standard entropy.