Machine learning-augmented spectroscopies for intelligent materials design / / Nina Andrejevic

• Autore: Andrejevic Nina
• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (106 pages)
• Disciplina: 006.31
• Soggetto topico: Machine learning; Smart materials
• Nota di contenuto: 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. ; 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.
• Titolo autorizzato: Machine Learning-Augmented Spectroscopies for Intelligent Materials Design
• ISBN: 9783031148088; 9783031148071
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996495164503316
• Lo trovi qui: Univ. di Salerno
• Serie: Springer Theses