Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials

TL;DR

This study uses machine learning-based molecular dynamics to investigate phase transformations in Mn-rich disordered rocksalt cathodes, revealing atomic mechanisms and electrochemical implications of the transformation process.

Contribution

It introduces a charge-informed machine learning interatomic potential to model phase transformations in Mn-rich cathodes, providing atomic-level insights into their electrochemical behavior.

Findings

Transition metal migration leads to spinel-like ordering.

Transformed structure has more non-transition metal channels, enhancing Li transport.

Solid-solution behavior observed in the δ-phase at low voltage.

Abstract

Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure ($\delta$-phase) during electrochemical cycling. In this computational study, we used charge-informed molecular dynamics with a fine-tuned CHGNet foundation potential to investigate the phase transformation in Li$_{x}$Mn$_{0.8}$Ti$_{0.1}$O$_{1.9}$F$_{0.1}$. Our results indicate that transition metal migration occurs and reorders to form the spinel-like ordering in an FCC anion framework. The transformed structure contains a higher concentration of non-transition metal (0-TM) face-sharing channels, which are known to improve Li transport kinetics. Analysis of the Mn valence distribution suggests that the appearance of tetrahedral Mn$^{2+}$ is a consequence of spinel-like ordering, rather than the trigger for cation migration as previously suggested. Calculated equilibrium intercalation voltage profiles demonstrate that the $\delta$-phase, unlike the ordered spinel, exhibits solid-solution signatures at low voltage. A higher Li capacity is obtained than in the DRX phase. This study provides atomic insights into solid-state phase transformation and its relation to experimental electrochemistry, highlighting the potential of machine learning interatomic potentials for understanding complex oxide materials.