In this study, a machine learning based computational approach has been developed to investigate the cation distribution in spinel crystals. The computational approach integrates the construction of datasets consisting of the energies calculated from density functional theory, the training of machine learning models to derive the relationship between system energy and structural features, and atomistic Monte Carlo simulations to sample the thermodynamic equilibrium structures of spinel crystals. It is found that the support vector machine model yields excellent performance in energy predictions based on spinel crystal structures. Furthermore, the developed computational approach has been applied to predict the cation distribution in single spinel MgAl2O4 and MgFe2O4 and double spinel MgAl2-aFeaO4. Agreeing with the available experimental data, the computational approach correctly predicts that the equilibrium degree of inversion of MgAl2O4 increases with temperature, whereas the degree of inversion of MgFe2O4 decreases with temperature. Additionally, it is predicted that the equilibrium occupancy of Mg cations at the tetrahedral and octahedral sites in MgAl2-aFeaO4 could be tuned as a function of chemical composition. Therefore, this study presents a reliable computational approach that can be extended to study the variation of cation distribution with processing temperature and chemical composition in a wide range of complex multi-cation spinel oxides with numerous applications.
Elucidating the Factors That Cause Cation Diffusion Shutdown in Spinel-Based Electrodes
We report on a systematic study of guest cation (i.e., Li, Na, or Mg) diffusion within spinel intercalation compounds, a promising class of materials for Li-, Na-, and Mg-ion batteries. Using kinetic Monte Carlo simulations, we identify factors that are responsible for a strong concentration dependence of the cation diffusion coefficient. We focus on spinels in which the guest cations prefer the octahedral sites and where diffusion is mediated by vacancy clusters. Starting with MgyTiS2, we predict an abrupt drop in the Mg diffusion coefficient that spans several orders of magnitude around y ≈ 0.5 due to the onset of highly correlated Mg diffusion. The prediction is consistent with previous experimental studies that are only able to achieve half the theoretical capacity of MgyTiS2. We next perform a parametric study of diffusion in spinels using kinetic Monte Carlo simulations applied to lattice model Hamiltonians and identify a critical topological weakness of the spinel crystal structure that makes it prone to highly correlated cation diffusion at intermediate-to-high guest cation concentrations. We find that the onset of this highly correlated diffusion becomes more pronounced as the nearest-neighbor repulsion between pairs of guest cations becomes stronger, since this increases the dependence of long-range cation diffusion on triple-vacancy clusters. The results of this study provide guidance with which the concentration dependence of cation diffusion coefficients in spinel can be tailored to reduce the onset of sluggish diffusion at high cation concentrations. The conclusions drawn from this study also apply to other close-packed anion hosts such as disordered rocksalt electrodes and partially ordered spinels.
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- Award ID(s):
- 2004693
- PAR ID:
- 10323250
- Date Published:
- Journal Name:
- Chemistry of materials
- Volume:
- 33
- Issue:
- 16
- ISSN:
- 0897-4756
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.chemmater.1c01668
