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
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