First principles simulations of high-entropy materials for energy storage

The transition to clean energy systems relies on advanced energy storage technologies, such as lithium-ion and solid-state batteries. In this realm, high-entropy materials (HEMs) have been attracting significant attention due to their potential to increase the energy density of electrodes and improve the ionic conductivity and stability of solid electrolytes. Atomistic simulations are a powerful tool to understand the fundamental behavior of HEMs and accelerate their design process. However, the complex ionic and electronic structures of these materials introduce substantial computational challenges. This dissertation addresses these challenges by advancing computational methodologies for modeling and understanding of HEMs. The scope of the thesis is to (1) benchmark and apply state-of-the-art electronic structure calculation methods such as the DFT+U method with Wannier functions (WF) as projectors, (2) investigate different techniques for realistic modeling of ionic configurations of HEMs and (3) to understand the performance of HEMs considered for battery applications with joint atomistic simulations and experimental investigation. Firstly, the DFT+U(WF) methodology was benchmarked for conventional electrode materials and extended to simple HEMs. This methodology was shown to improve the accuracy of electronic structure predictions compared to standard DFT+U, providing reliable estimates of oxidation states and band gaps that are comparable to computationally expensive hybrid functionals, while maintaining computational efficiency. Secondly, thermodynamic and electrochemical degradation mechanisms in Li-rich HEM cathodes were investigated together with experiments done by experimental collaborators, revealing critical pathways such as oxygen dimer formation, transition metal migration, and secondary phase formation. These findings highlight the limitations of the high-entropy effect and underscore the influence of local atomic environments in de-stabilizing electrochemically active phases. The research was further extended to garnet-based solid electrolytes, for which the impact of configurational entropy and atomic distributions on ionic conductivity and structural stability was analyzed. Increasing dopant diversity was shown to stabilize cubic phases with higher-ionic conductivity over tetragonal phases. A computationally derived high-entropy garnet with improved ionic conductivity was successfully synthesized by experimental colleagues at reduced synthesis temperatures. Studies were extended to high-entropy fluorides and Prussian white and blue analogs for diverse energy storage and electronic applications, such as sodium-ion batteries and memristors, showcasing the versatility of HEMs. Both computational and experimental results indicate that entropy-driven phase stabilization in these systems enhances their cycling performance. Additionally, we proposed a resistive switching mechanism for non-volatile memristors composed of HEMs, which was verified through atomistic simulations and experiments. Finally, advanced computational techniques were introduced to address challenges in the modeling of ionic configurations in disordered materials. In a pioneering work, quantum annealing was successfully applied to optimize ionic arrangements driven by Coulomb energy. Additionally, the persistence of short-range order (SRO) in disordered materials was revealed by calculating the Warren-Cowley SRO parameters and radial distribution function (RDF) analysis in a well-mixed four-component phosphate system, emphasizing the need to incorporate SRO into structural models of HEMs for more realistic modeling. In summary, this dissertation addresses fundamental challenges in understanding disordered systems at an atomistic level. Computational approaches that have been proposed lead to more accurate descriptions of electronic structures and provide insights into how ionic arrangements influence material properties. The results build a solid basis for accurate computational studies of complex disordered materials for next-generation energy storage applications and could serve as the foundation for further research into accurate and computationally feasible approaches for modeling HEMs and other multi-component systems, contributing to the broader advancement of energy materials research.