Machine Learning-Augmented Spectroscopies for Intelligent Materials Design (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Theses
Release date:	October 2022
First published:	2022
Authors:	Nina Andrejevic
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	97
Edition:	1st ed. 2022
ISBN-13:	978-3-03-114807-1
Categories:	Books > Professional & Technical > Mechanical engineering & materials > Materials science > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Circuits & components Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Science & Mathematics > Chemistry > Analytical chemistry > Qualitative analytical chemistry > Chemical spectroscopy, spectrochemistry > General
LSN:	3-03-114807-X
Barcode:	9783031148071

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