Supporting Data for: Insights into the Degradation Mechanism of Aqueous LiMn2O4-Cathode Interfaces by Computational Vibrational Spectroscopy

Still, the atomistic link between lithiation state and interfacial degradation remains elusive. Here, we investigate aqueous LMO interfaces across varying lithiation states by atomistic simulations using an ab initio-quality machine learning potential. We demonstrate that the local Li content determines surface Mn oxidation states and reveal their decisive role in interfacial acid--base chemistry.

By analyzing the vibrational features of interfacial species, we identify the O-H stretching band as a sensitive spectroscopic probe of the surface electronic structure. Distinct, oxidation-state-dependent shifts reveal a fundamental vulnerability of the mixed-valence spinel surface: the coexistence of Lewis-acidic Mn(4+) centers and oxygen sites neighboring Mn(3+), which are susceptible to electrophilic attack. These findings provide a compelling atomistic rationale extending the conventional Mn(3+)-centric view of acid-promoted Mn dissolution toward a dual-site mechanism driven by LMO’s mixed-valent nature.

This dataset is part of the Dataverse "Insights into the Degradation Mechanism of Aqueous LiMn2O4-Cathode Interfaces by Computational Vibrational Spectroscopy". It contains the analysis code, simulation setups, and machine learning models supporting the findings reported in the manuscript detailed below. For detailed dataset descriptions and usage instructions, refer to the main README.md and the README.md files located in each subdirectory.