| 书目名称 | Machine Learning-Augmented Spectroscopies for Intelligent Materials Design |
| 编辑 | Nina Andrejevic |
| 视频video | |
| 概述 | Nominated as an outstanding PhD thesis by Massachusetts Institute of Technology.Introduces machine learning methods for neutron and photon scattering and spectroscopy.Identifies spectral signatures of |
| 丛书名称 | Springer Theses |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2022 |
| 关键词 | machine learning for materials characterization; machine learning Raman spectra; machine learning neut |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-14808-8 |
| isbn_softcover | 978-3-031-14810-1 |
| isbn_ebook | 978-3-031-14808-8Series ISSN 2190-5053 Series E-ISSN 2190-5061 |
| issn_series | 2190-5053 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 19:12:18
