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

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发表于 2025-3-21 19:47:04 | 显示全部楼层 |阅读模式
书目名称Nanoinformatics
编辑Isao Tanaka
视频video
概述Enables readers to learn the state of the art of the emerging research area efficiently.Will keenly interest theorists and experimentalists in both the academic and industry sectors.Presents interdisc
图书封面Titlebook: Nanoinformatics;  Isao Tanaka Book‘‘‘‘‘‘‘‘ 2018 The Editor(s) (if applicable) and The Author(s) 2018 Machine learning.Big data.Atomic resol
描述This 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 computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. "Materials informatics" is the central paradigm in the new trend. "Nanoinformatics" is its essential subset, which focuses on nanostructures of materials such as surfaces, interfaces, dopants, and point defects, playing a critical role in determining materials properties. There have been significant advances in experimental and computational techniques to characterize individual atoms in nanostructures and to gain quantitative information. The collaboration of researchers in materials science and information science is growing actively and is creating a new trendin materials science and engineering.
出版日期Book‘‘‘‘‘‘‘‘ 2018
关键词Machine learning; Big data; Atomic resolution characterization; First-principles calculations; Nanomater
版次1
doihttps://doi.org/10.1007/978-981-10-7617-6
isbn_softcover978-981-13-5661-2
isbn_ebook978-981-10-7617-6
copyrightThe Editor(s) (if applicable) and The Author(s) 2018
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发表于 2025-3-21 23:23:29 | 显示全部楼层
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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978-981-13-5661-2The Editor(s) (if applicable) and The Author(s) 2018
发表于 2025-3-22 20:04:49 | 显示全部楼层
Book‘‘‘‘‘‘‘‘ 2018 has been great progress in the area in which “big-data” generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. "Materials informat
发表于 2025-3-23 01:13:53 | 显示全部楼层
Polyhedron and Polychoron Codes for Describing Atomic Arrangementsthird nearest neighbours, …. However, there existed no methods for describing the arrangements of polyhedra, or atomic arrangements. To overcome this problem, we have recently created the polyhedron and polychoron codes [Sci. Rep. 6, 23455, Sci. Rep. 7, 40269, and Bull. Soc. Sci. Form 32, 1 (2017)]. In this chapter, we review the methods.
发表于 2025-3-23 01:34:33 | 显示全部楼层
Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process ed using a model case of proton conduction in a well-known proton-conducting oxide, barium zirconate (BaZrO.) with the cubic perovskite structure. The proposed procedure efficiently evaluates the partial PES in the region of interest that characterizes proton conduction in the host crystal lattice of BaZrO..
发表于 2025-3-23 07:39:55 | 显示全部楼层
Atomic-Scale Nanostructures by Advanced Electron Microscopy and Informaticsn acceleration on the order of 10. 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.
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