Machine learning-augmented spectroscopies for intelligent materials design / Nina Andrejevic.
2022
TA418.9.S62
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Title
Machine learning-augmented spectroscopies for intelligent materials design / Nina Andrejevic.
Author
Andrejevic, Nina, author.
ISBN
9783031148088 (electronic bk.)
3031148088 (electronic bk.)
303114807X
9783031148071
3031148088 (electronic bk.)
303114807X
9783031148071
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (xii, 97 pages) : illustrations (chiefly color).
Item Number
10.1007/978-3-031-14808-8 doi
Call Number
TA418.9.S62
Dewey Decimal Classification
620.1/12
Summary
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 physics insights to aid spectroscopic analysis may hold great promise for next-generation quantum technology. As a prominent example, the so-called proximity effect at topological material interfaces promises to enable spintronics without energy dissipation and quantum computing with fault tolerance, yet the characteristic spectral features to identify the proximity effect have long been elusive. The work presented within permits a fine resolution of its spectroscopic features and a determination of the proximity effect which could aid further experiments with improved interpretability. A few novel machine learning architectures are proposed in this thesis work which leverage the case when the data is scarce and utilize the internal symmetry of the system to improve the training quality. The work sheds light on future pathways to apply machine learning to augment experiments.
Note
"Doctoral thesis accepted by Massachusetts Institute of Technology, USA."
Bibliography, etc. Note
Includes bibliographical references.
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Description based on print version record.
Series
Springer theses.
Available in Other Form
MACHINE LEARNING-AUGMENTED SPECTROSCOPIES FOR INTELLIGENT MATERIALS DESIGN.
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Table of Contents
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.
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.