國立虎尾科技大學 |

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning-Augmented Spectroscopies for Intelligent Materials Design/ by Nina Andrejevic.
作者:	Andrejevic, Nina.
面頁冊數:	XII, 97 p. 29 illus., 28 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Spectrum analysis. -
電子資源:	https://doi.org/10.1007/978-3-031-14808-8
ISBN:	9783031148088

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design
Andrejevic, Nina.

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design[electronic resource] /by Nina Andrejevic. - 1st ed. 2022. - XII, 97 p. 29 illus., 28 illus. in color.online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5061. - Springer Theses, Recognizing Outstanding Ph.D. Research,.

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.

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.

ISBN: 9783031148088

Standard No.: 10.1007/978-3-031-14808-8doiSubjects--Topical Terms:

582358
Spectrum analysis.

LC Class. No.: QD95-96

Dewey Class. No.: 543

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