Shin Kiyohara

Prediction of interface structures and energies via virtual screening

Grain boundaries dramatically affect the properties of polycrystalline materials because of differences in atomic configuration. To fully understand the relationship between grain boundaries and materials properties, systematic studies of the grain boundary atomic structure are crucial. However, such studies are limited by the extensive computation necessary to determine the structure of a single grain boundary. If the structure could be predicted with more efficient computation, the understanding of the grain boundary would be accelerated significantly. Here, we predict grain boundary structures and energies using a machine-learning technique. Training data for non-linear regression of four symmetric-tilt grain boundaries of copper were used. The results of the regression analysis were used to predict 12 other grain boundary structures. The method accurately predicts both the structures and energies of grain boundaries. The method presented in this study is very general and can be utilized in understanding many complex interfaces.A virtual screening method achieved a maximum boost in speed of several tens of thousands–fold while determining the interface structure. Interfaces markedly affect the properties of materials because of differences in their atomic configurations. Determining the atomic structure of the interface is therefore one of the most significant tasks in materials research. However, determining the interface structure usually requires extensive computation. If the interface structure could be efficiently predicted, our understanding of the mechanisms that give rise to the interface properties would be significantly facilitated, and this would pave the way for the design of material interfaces. Using a virtual screening method based on machine learning, we demonstrate a powerful technique to determine interface energies and structures. On the basis of the results obtained by a nonlinear regression using training data from 4 interfaces, structures and energies for 13 other interfaces were predicted. Our method achieved an efficiency that is more than several hundred to several tens of thousand times higher than that of the previously reported methods. Because the present method uses geometrical factors, such as bond length and atomic density, as descriptors for the regression analysis, the method presented here is robust and general and is expected to be beneficial to understanding the nature of any interface.

Investigation of segregation of silver at copper grain boundaries by first principles and empirical potential calculations

Segregation of silver at copper grain boundaries was investigated using theoretical calculations. Empirical potentials for copper-silver alloys were generated to systematically investigate the segregation. The segregation energies of the [001]-axis symmetric tilt Σ5 (210) and Σ25 (430) grain boundaries were calculated, and the most stable segregation sites for silver at these copper grain boundaries were determined. The generated empirical potential was validated by comparing it with that obtained from the first principles calculation. The segregation of silver at copper grain boundaries strongly depends on the open space at the segregation site.

Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree search

Non-stoichiometric structure localized at the grain boundary, namely, segregations of impurities, dopants, and vacancies, has an important effect on a broad variety of material properties. An understanding of this behavior is therefore indispensable for further material development. Although molecular dynamics simulation and a simulation combined with randomly swapping atoms and vacancies have usually been used to investigate the segregation structures, they require more than ten thousand structures and energy calculations to reach the stable configuration. Although several mathematical or informatics approaches, for example, genetic algorithm and Bayesian optimization, have been proposed to solve such combination optimization problems, they required some hyper parameters which crucially affect efficiency and huge computations to tune these parameters. Furthermore, a parallelization of the computation task is often impossible in molecular dynamics simulation and Bayesian optimization because their structures are related to each other before and after the time or simulation steps. Here, we develop a Monte Carlo tree search algorithm for grain boundary segregation and apply it to determine the stable segregation configuration of copper Σ5[001]/(210) and Σ37[001]/(750) with silver impurities. We achieved a determination of the stable configuration by searching only 1% of all possible configurations. Furthermore, we found that the search path and the number of playouts at the branch provide important insight to comprehend the background of the search. In the present case, the search path was identical to the sites with the spatially larger sites.

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy

Spectroscopy is indispensable for determining atomic configurations, chemical bondings, and vibrational behaviours, which are crucial information for materials development. Despite their importance, the interpretation of spectra using “human-driven” methods, such as the manual comparison of experimental spectra with reference/simulated spectra, is difficult due to the explosive increase in the number of experimental spectra to be observed. To overcome the limitations of the “human-driven” approach, we develop a new “data-driven” approach based on machine learning techniques by combining the layer clustering and decision tree methods. The proposed method is applied to the 46 oxygen-K edges of the ELNES/XANES spectra of oxide compounds. With this method, the spectra can be interpreted in accordance with the material information. Furthermore, we demonstrate that our method can predict spectral features from the material information. Our approach has the potential to provide information about a material that cannot be determined manually as well as predict a plausible spectrum from the geometric information alone.

Atomic-Scale Nanostructures by Advanced Electron Microscopy and Informatics

Interfaces dramatically affect the properties of materials because their atomic configurations often differ from the bulk material. A determination of the atomic structure of the interface is, therefore, one of the most significant tasks in materials research. Electron microscopy and theoretical calculations have been effectively used to accomplish this important task. In addition, an informatics approach has recently been combined with theoretical calculations to efficiently determine the atomic structures of interfaces. This chapter introduces the determination of interface structures using an informatics approach (Bayesian optimization and virtual screening) along with advanced electron microscopy. In the informatics approach, calculation acceleration on the order of 106 can be achieved. Determination of the interface structure with resolution better than ~45 pm is now possible using advanced electron microscopy. In this way, nanostructures at grain boundaries and heterointerfaces can be qualified. We will introduce these state of the art methods to investigate nanostructures.