Theses Doctoral

Quantum-Mechanistic-Based and Data-Driven Prediction of Surface Degradation and Stacking Faults in Battery Cathode Materials

Li, Xinhao

Batteries play a pivotal role in the modern world, powering everything from portable electronics to electric vehicles, and are critical in the shift towards renewable energy sources by enabling efficient energy storage. This thesis presents new computational strategies to understand and predict surface degradation and stacking faults in battery cathodes, phenomena that have crucial impact on the battery lifetime.

The starting point is a detailed first-principles analysis of LiNiO₂ surface degradation, assessing the thermodynamics of oxygen release and its impact on the surface integrity of this prospective cathode material. This research led to the development of a method for the automated enumeration of surface reconstructions and the development of a Python software package implementing the methodology, thereby greatly accelerating the computational surface characterization of electrode materials. The methodology made it feasible to extend the investigation to LiCoO₂ surfaces, comparing their oxygen retention and surface stability with LiNiO₂ and identifying the unique properties of the two transition metals that control their behavior during battery operation.

In addition to surface phase changes, stacking faults are another important class of two-dimensional defects that can affect the properties of cathode materials. Combining information from first principles calculations with 17O nuclear magnetic resonance (NMR) spectroscopy provided by collaborators, we uncovered how stacking faults affect the capacity and cyclability of Li₂MnO₃ cathodes, a prototypical lithium-rich material with oxygen redox activity. Although automated first-principles calculations are, in principle, an ideal tool for understanding atomic-scale degradation phenomena in batteries, they are computationally demanding and, therefore, limited to materials with simple compositions. In the final chapter, we explore the application of machine learning for further accelerating computational battery degradation simulations by leveraging existing data first-principles calculations for predicting the stability of new surface reconstructions. This chapter points toward a new direction that should be further explored in the future.

The research presented in this thesis not only advances the understanding of lithium-ion battery cathode materials but also introduces more-widely applicable computational methodologies that lay a foundation for the development of advanced materials for energy storage applications. This work demonstrates the benefits of integrating traditional computational methods with machine learning, contributing to ongoing progress in materials science and opening up new possibilities for advancements in energy technology and material engineering.


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More About This Work

Academic Units
Chemical Engineering
Thesis Advisors
Urban, Alexander
Ph.D., Columbia University
Published Here
June 5, 2024