Chapter1: Introduction.- Chapter2: Background.- Chapter3: Data-efficient learning of materials' vibrational properties.- Chapter4: Machine learning-assisted parameter retrieval from polarized neutron reflectometry measurements.- Chapter5: Machine learning spectral indicators of topology.- Chapter6: Conclusion and outlook.

Nina Andrejevic obtained her B.S. in Engineering Physics from Cornell University in 2016 and her Ph.D. in Materials Science and Engineering from MIT in 2022. Her research interests are at the intersection of physics-informed machine learning methods and quantum materials characterization. She is currently a Maria Goeppert Mayer Postdoctoral Fellow at Argonne National Laboratory, where she combines machine learning methods with X-ray scattering and spectroscopic measurements for intelligent analysis of materials¿ signatures.