Nanoinformatics
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...
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Springer Nature
2018
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Subjects: | |
Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Isao Tanaka |4 auth | |
245 | 1 | 0 | |a Nanoinformatics |
260 | |b Springer Nature |c 2018 | ||
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506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a 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 trend in materials science and engineering. | ||
536 | |a Japan Society for the Promotion of Science (JSPS) | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
653 | |a First-principles calculations | ||
653 | |a Nanomaterials synthesis | ||
653 | |a Machine learning | ||
653 | |a Big data | ||
653 | |a Atomic resolution characterization | ||
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856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/46733 |7 0 |z DOAB: description of the publication |