Titlebook: Nanoinformatics; Isao Tanaka Book‘‘‘‘‘‘‘‘ 2018 The Editor(s) (if applicable) and The Author(s) 2018 Machine learning.Big data.Atomic resol

生锈

Structural Relaxation of Oxide Compounds from the High-Pressure Phasenquenchable pressure-induced phase (A-type structure) is not the stable phase under high pressure. Knowledge about the unquenchable and/or metastable phases in recovered high-pressure products is beneficial for advanced computational materials design.

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in both the academic and industry sectors.Presents interdiscThis open access book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which “big-data” generated by experiments or compu

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Machine Learning Predictions of Factors Affecting the Activity of Heterogeneous Metal Catalystsmer–Nørskov d-band model. The present work demonstrates the possibility of employing state-of-the-art machine learning methods to predict the d-band centers of metals and bimetals while using negligible CPU time compared to the more common first-principles approach.

GLUE

Fabrication, Characterization, and Modulation of Functional Nanolayerstaxial growth techniques, especially “reactive solid-phase epitaxy” of functional oxides and chalcogenides, are reviewed based on the authors’ efforts. Additionally, this chapter reviews several modulation methods of optical, electrical, and magnetic properties of functional oxide nanolayers.

出来

Descriptors for Machine Learning of Materials Dataements and structures of compounds are known, these representations are difficult to use as descriptors in their unchanged forms. This chapter shows how compounds in a dataset can be represented as descriptors and applied to machine-learning models for materials datasets.

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