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

TL;DR

This paper develops a high-dimensional neural network potential based on DFT to accurately simulate lithium manganese oxide spinels, enabling large-scale modeling that bridges the gap between theoretical predictions and experimental observations.

Contribution

The authors introduce a DFT-based HDNNP capable of modeling complex oxidation states and distortions in lithium manganese oxide spinels with high accuracy and reduced computational cost.

Findings

HDNNP accurately predicts lattice parameters and phase transitions.

HDNNP reproduces lithium diffusion barriers and phonon frequencies.

Large-scale simulations align well with experimental data.

Abstract

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 Li$_x$Mn$_2$O$_4$ with $0\leq x\leq2$. While DFT, employing the local hybrid functional PBE0r, provides a reliable description, the need for extended computer simulations of large structural models remains a significant challenge. Here, we close this gap by constructing a DFT-based high-dimensional neural network potential (HDNNP) providing accurate energies and forces at a fraction of the computational costs. As different oxidation states and the resulting Jahn-Teller distortions represent a new level of complexity for HDNNPs, the potential is carefully validated by performing X-ray diffraction experiments. We demonstrate that the HDNNP provides atomic level details and is able to predict a series of properties like the lattice parameters and expansion with increasing Li content or temperature, the orthorhombic to cubic transition, the lithium diffusion barrier, and the phonon frequencies. We show that for understanding these properties access to large time and length scales as enabled by the HDNNP is essential to close the gap between theory and experiment.