UID:
edoccha_9960901282002883
Format:
1 online resource (106 pages)
ISBN:
9783031148088
Series Statement:
Springer Theses
Note:
Intro -- Supervisor's Foreword -- Acknowledgments -- Parts of This Thesis Have Been Published in the Following Journal Articles and Preprints -- Contents -- 1 Introduction -- 1.1 Neutron and Photon Scattering and Spectroscopy -- 1.2 Integration of Machine Learning -- 1.3 Thesis Objectives -- References -- 2 Background -- 2.1 Neutron and Photon Scattering and Spectroscopy -- 2.1.1 Inelastic Neutron Scattering -- 2.1.2 Raman Spectroscopy -- 2.1.3 Polarized Neutron Reflectometry -- 2.1.4 X-ray Absorption Spectroscopy -- 2.2 Data-Driven Methods -- 2.2.1 Dimensionality Reduction -- Singular Value Decomposition -- Principal Component Analysis -- Non-negative Matrix Factorization -- 2.2.2 Machine Learning -- Support Vector Machines -- Neural Networks -- References -- 3 Data-Efficient Learning of Materials' Vibrational Properties -- 3.1 Introduction -- 3.2 Materials Data Representations -- 3.3 Euclidean Neural Networks -- 3.3.1 Graph Representation of Crystal Structures -- 3.3.2 Network Operations -- 3.4 Phonon DoS Prediction -- 3.4.1 Data Processing -- 3.4.2 Results -- 3.4.3 Comparison with Experiment -- 3.4.4 High-CV Materials Discovery -- 3.4.5 Partial Phonon Density of States -- 3.4.6 Alloys and Strained Compounds -- 3.5 Unsupervised Representation Learning of Vibrational Spectra -- 3.5.1 Dimensionality Reduction -- 3.5.2 Data Processing Methods -- 3.5.3 Results -- 3.6 Conclusion -- References -- 4 Machine Learning-Assisted Parameter Retrieval from Polarized Neutron Reflectometry Measurements -- 4.1 Introduction -- 4.2 Polarized Neutron Reflectometry -- 4.3 Variational Autoencoder -- 4.3.1 VAE-Based PNR Parameter Retrieval -- 4.3.2 Data Preparation -- 4.3.3 Results -- 4.4 Resolving Interfacial AFM Coupling -- 4.5 Discussion -- 4.6 Conclusion -- References -- 5 Machine Learning Spectral Indicators of Topology -- 5.1 Introduction.
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5.2 Topological Materials Discovery -- 5.3 Data Preparation and Pre-processing -- 5.4 Exploratory Analysis -- 5.5 Results -- 5.6 Conclusion -- References -- 6 Conclusion and Outlook -- 6.1 Thesis Summary -- 6.2 Perspectives and Outlook -- Reference.
Additional Edition:
Print version: Andrejevic, Nina Machine Learning-Augmented Spectroscopies for Intelligent Materials Design Cham : Springer International Publishing AG,c2022 ISBN 9783031148071
Language:
English
