Keywords:
Materials science
;
Spectroscopy
;
Chemistry, Physical and theoretical
;
Nanoscale science
;
Nanoscience
;
Nanostructures
;
Microscopy
;
Nanotechnology
;
Materials Science
;
Nanotechnology
;
Theoretical and Computational Chemistry
;
Nanoscale Science and Technology
;
Characterization and Evaluation of Materials
;
Spectroscopy/Spectrometry
;
Spectroscopy and Microscopy
Description / Table of Contents:
1. Descriptors for Machine Learning of Materials Data --- 2. Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process Model --- 3. Machine learning predictions of factors affecting the activity of heterogeneous metal catalysts --- 4. Machine Learning-based Experimental Design in Materials Science --- 5. Persistent homology and materials informatics --- 6. Polyhedron and Polychoron codes for describing Atomic Arrangements --- 7. Topological Data Analysis for the Characterization of Atomic Scale Morphology from Atom Probe Tomography Images --- 8. Atomic-scale nanostructures by advanced electron microscopy and informatics --- 9. High spatial resolution hyperspectral imaging with machine-learning techniques --- 10. Fabrication, Characterization, and Modulation of Functional Nanolayers --- 11. Grain Boundary Engineering of Alumina Ceramics --- 12. Structural relaxation of oxide compounds from the high-pressure phase.-13.Synthesis and structures of novel solid-state electrolytes
Pages:
Online-Ressource (VIII, 298 pages)
,
188 illustrations, 142 illustrations in color
ISBN:
9789811076176
URL:
https://link.springer.com/openurl?genre=book&isbn=978-981-10-7617-6
Language:
English
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