Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential

@article{Eckhoff2020ClosingTG,
  title={Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential},
  author={Marco Eckhoff and Florian Sch{\"o}newald and Marcel Risch and Cynthia A. Volkert and Peter E. Bl{\"o}chl and J{\"o}rg Behler},
  journal={Physical Review B},
  year={2020},
  volume={102},
  pages={174102}
}
Many positive electrode materials in lithium ion batteries include transition metals, which are difficult to describe by electronic structure methods like density functional theory (DFT) due to the presence of multiple oxidation states. A prominent example is the lithium manganese oxide spinel ${\mathrm{Li}}_{x}{\mathrm{Mn}}_{2}{\mathrm{O}}_{4}$ with $0\ensuremath{\le}x\ensuremath{\le}2$. While DFT, employing the local hybrid functional PBE0r, provides a reliable description, the need for… 
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