Machine Learning-Augmented Spectroscopies for Intelligent Materials Design [E-Book] / by Nina Andrejevic.
The thesis contains several pioneering results at the intersection of state-of-the-art materials characterization techniques and machine learning. The use of machine learning empowers the information extraction capability of neutron and photon spectroscopies. In particular, new knowledge and new phy...
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Full text |
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Personal Name(s): | Andrejevic, Nina, author |
Edition: |
1st edition 2022. |
Imprint: |
Cham :
Springer,
2022
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Physical Description: |
XII, 97 pages 29 illustrations, 28 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783031148088 |
DOI: |
10.1007/978-3-031-14808-8 |
Series Title: |
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Springer Theses, Recognizing Outstanding Ph.D. Research
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Subject (LOC): |
- Chapter1: Introduction
- Chapter2: Background
- Chapter3: Data-efficient learning of materials' vibrational properties
- Chapter4: Machine learning-assisted parameter retrieval from polarized neutron reflectometry measurements
- Chapter5: Machine learning spectral indicators of topology
- Chapter6: Conclusion and outlook.