Spectroscopy of Two Dimensional Electron Systems Comprising Exotic Quasiparticles

In this dissertation I present inelastic and elastic light scattering studies of collective states emerging from interactions in electron systems confined to two dimensions. These studies span the first, second and third Landau levels. I report for the first time, high energy excitations of composite fermions in the quantum fluid at v = 1/3. The high energies discovered represent excitations across multiple composite fermion energy levels, demonstrating the topological robustness of the fractional quantum Hall state at v = 1/3. This study sets the ground work for similar measurements of states in the second Landau level, such as those at v = 5/2. I present the first light scattering studies of low energy excitations of quantum fluids in the second Landau level. The study of low energy excitations of the quantum fluid at 3 ≥ v ≥ 5/2 reveals a rapid loss of spin polarization for v ≤ 3, as monitored by the intensity of the spin wave excitation at the Zeeman energy. The emergence of a continuum of low-lying excitations for v ≤ 3 reveals competing quantum phases in the second Landau level with intriguing roles of spin degrees of freedom and phase inhomogeneity. The first light scattering studies of the electron systems in the third Landau level are reported here. Measurements of low energy excitations and their spin degrees of freedom reveal contrasting behavior of states in the second and third Landau levels. I discuss these measurements in the context of the charge density wave phases, that are believed, by some, to dominate the third Landau level, and suggest ways of verifying this belief using light scattering. Distinct behavior in the dispersion of the spin wave at v = 3 is measured for the first time. The study may highlight differences in the first and second Landau levels that are manifested through the electron wavefunctions. In addition to intra-Landau level measurements, inter-Landau level studies are also reported. The results of which reveal roles of spin degrees of freedom and many body interactions in odd denominator integer quantum Hall states

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

Data-Driven Studies of Li-Ion-Battery Materials

Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials