• Accueil   >  
  • Reference   >  
  • Science   >  
  • Machine Learning-Augmented Spectroscopies for Intelligent Materials Design
Téléchargez le livre : Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design



de

Éditeur :

Springer


Collection :

Springer Theses

Paru le : 2022-10-06



eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
147,69 €
Téléchargement immédiat
Dès validation de votre commande
Ajouter à ma liste d'envies
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

badge apple Application Louise Reader
badge Android Application Louise Reader
Description

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.
Pages
97 pages
Collection
Springer Theses
Parution
2022-10-06
Marque
Springer
EAN papier
9783031148071
EAN PDF
9783031148088
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
9943 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
147,69 €
EAN EPUB
9783031148088
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
15370 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
147,69 €

Suggestions personnalisées