Unlimited

Authorized users

Can lend chapters, not whole ebooks

Machine learning-augmented spectroscopies for intelligent materials design / Nina Andrejevic.

9783031148088 (electronic bk.)
3031148088 (electronic bk.)
303114807X
9783031148071

Cham : Springer, [2022]

©2022

English

1 online resource (xii, 97 pages) : illustrations (chiefly color).

10.1007/978-3-031-14808-8 doi

TA418.9.S62

620.1/12

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.

"Doctoral thesis accepted by Massachusetts Institute of Technology, USA."

Includes bibliographical references.

Access limited to authorized users.

Description based on print version record.

Springer theses.

Online Resources > Ebooks
All Resources