Progress in nanoinformatics and informational materials science
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Introduction Data-centric approaches have been adopted to dramatically accelerate progress in materials science. This is also the case for the study of nanostructures of materials.1 Thanks to advances in computational power and techniques, theoretical calculations using density functional theory (DFT) can be systematically performed for many different crystals and nanostructures with predictive performances; these have been stored as open databases, as shown in the other articles in this issue, or in local depositories. Progress of digitally controlled microscopy and spectroscopy has enabled acquisition of big data from nanostructures with atomic resolution. The combination of such digital data with modern machine-learning (ML) techniques has been used to explore materials and structures. It has been used to extract meaningful and useful information and patterns from existing data, or data-driven discovery. This article reviews recent progress in ideas and tools in nanoinformatics and informational materials science. Actual applications of ML techniques for materials problems will also be demonstrated. Topics include descriptions of materials properties, construction of interatomic potentials, discovery of new inorganic compounds, exploration of potential energy
surfaces for efficient characterization of ionic transport, efficient search of interface structures, data analysis of hyperspectral images by transmission electron microscopy, and design of catalytic nanoparticles.
Descriptions of materials properties How compounds are represented in a data set is a key factor in controlling the performance of an ML approach. Representations of compounds are called “descriptors” or “features.” A useful strategy is to use a set of quantities, derived from elemental and structural representations of a compound, as descriptors since such representations are abundant in the literature. Kernel ridge regression prediction models for the DFT cohesive energy have been used to evaluate the performance of descriptors derived from elemental and structural representations.2 Our best prediction model has a prediction error of 0.045 eV/atom.2 Therefore, the present method should be useful to search for compounds with diverse chemical properties that are applicable to a wide range of chemical and structural spaces without performing exhaustive DFT calculations. Our previous research confirmed that descriptors based on elemental and structural representations are useful in other applications such as the prediction of thermal and electronic properties.3–5
Atsuto Seko, Department of Materials Science and Engineering, Kyoto University, Japan; [email protected] Kazuaki Toyoura, Department of Materials Science and Engineering, Kyoto University, Japan; [email protected] Shunsuke Muto, Institute of Materials and Systems for Sustainability, and Ultra-High Voltage Electron Microscopy Laboratory, Nagoya University, Japan; [email protected] Teruyasu Mizoguchi, Institute of Industrial Science, The University of Tok
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