LEADER 03580nam 2200457 450 001 996495164503316 005 20231110212343.0 010 $a9783031148088$b(electronic bk.) 010 $z9783031148071 035 $a(MiAaPQ)EBC7107677 035 $a(Au-PeEL)EBL7107677 035 $a(CKB)24995914400041 035 $a(PPN)265857449 035 $a(EXLCZ)9924995914400041 100 $a20230302d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning-augmented spectroscopies for intelligent materials design /$fNina Andrejevic 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$dİ2022 215 $a1 online resource (106 pages) 225 1 $aSpringer Theses 311 08$aPrint version: Andrejevic, Nina Machine Learning-Augmented Spectroscopies for Intelligent Materials Design Cham : Springer International Publishing AG,c2022 9783031148071 327 $aIntro -- 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. 327 $a5.2 Topological Materials Discovery -- 5.3 Data Preparation and Pre-processing -- 5.4 Exploratory Analysis -- 5.5 Results -- 5.6 Conclusion -- References -- 6 Conclusion and Outlook -- 6.1 Thesis Summary -- 6.2 Perspectives and Outlook -- Reference. 410 0$aSpringer Theses 606 $aMachine learning 606 $aSmart materials 615 0$aMachine learning. 615 0$aSmart materials. 676 $a006.31 700 $aAndrejevic$b Nina$01261314 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996495164503316 996 $aMachine Learning-Augmented Spectroscopies for Intelligent Materials Design$92930735 997 $aUNISA