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Li-excess cation-disordered rocksalt (DRX) oxides have shown potential as high-energy-density Li-ion cathodes. They typically exploit O redox to achieve high capacity, which can trigger oxygen loss at the surface, thereby affecting the cathode performance. Here, we elucidate the impact that the surface structural evolution has on their electrochemical properties by comparing two prototypical DRX cathodes, Li1.2Ni0.333Ti0.333Mo0.133O2 (LNTMO) and Li1.2Mn0.6Nb0.2O2 (LMNO). Both cathodes achieve high capacity, but oxygen loss leads to significant polarization for LNTMO, whereas LMNO is far less affected. We show that while metal densification at the particle surface occurs for both materials, the resulting surface structure is strikingly different. A spinel phase forms at the surface of LMNO, which effectively alleviates oxygen loss and allows fast Li transport, whereas a densified DRX forms at the LNTMO surface, which impedes Li transport and cannot mitigate oxygen loss. These findings demonstrate the importance of the surface structure of DRX cathode.

Fluorine substitution is a critical enabler for improving the cycle life and energy density of disordered rocksalt (DRX) Li-ion battery cathode materials which offer prospects for high energy density cathodes, without the reliance on limited mineral resources. Due to the strong Li–F interaction, fluorine also is expected to modify the short-range cation order in these materials which is critical for Li-ion transport. In this work, density functional theory and Monte Carlo simulations are combined to investigate the impact of Li–F short-range ordering on the formation of Li percolation and diffusion in DRX materials. The modeling reveals that F substitution is always beneficial at sufficiently high concentrations and can, surprisingly, even facilitate percolation in compounds without Li excess, giving them the ability to incorporate more transition metal redox capacity and thereby higher energy density. It is found that for F levels below 15%, its effect can be beneficial or disadvantageous depending on the intrinsic short-range order in the unfluorinated oxide, while for high fluorination levels the effects are always beneficial. Using extensive simulations, a map is also presented showing the trade-off between transition-metal capacity, Li-transport, and synthetic accessibility, and two of the more extreme predictions are experimentally confirmed.

Structure plays a vital role in determining materials properties. In lithium ion cathode materials, the crystal structure defines the dimensionality and connectivity of interstitial sites, thus determining lithium ion diffusion kinetics. In most conventional cathode materials that are well-ordered, the average structure as seen in diffraction dictates the lithium ion diffusion pathways. Here, we show that this is not the case in a class of recently discovered high-capacity lithium-excess rocksalts. An average structure picture is no longer satisfactory to understand the performance of such disordered materials. Cation short-range order, hidden in diffraction, is not only ubiquitous in these long-range disordered materials, but fully controls the local and macroscopic environments for lithium ion transport. Our discovery identifies a crucial property that has previously been overlooked and provides guidelines for designing and engineering cation-disordered cathode materials.

Amorphous Li-ion conductors are important solid-state electrolytes. However, Li transport in these systems is much less understood than for crystalline materials. We investigate amorphous LiPON electrolytes via ab initio molecular dynamics, providing atomistic-level insight into the mechanisms underlying the Li+ mobility. We find that the latter is strongly influenced by the chemistry and connectivity of phosphate polyanions near Li+. Amorphization generates edge-sharing polyhedral connections between Li(O,N)4 and P(O,N)4, and creates under- and overcoordinated Li sites, which destabilizes the Li+ and enhances their mobility. N substitution for O favors conductivity in two ways: (1) excess Li accompanying 1(N):1(O) substitutions introduces extra carriers; (2) energetically favored N-bridging substitutions condense phosphate units and densify the structure, which, counterintuitively, corresponds to higher Li+ mobility. Finally, bridging N is not only less electronegative than O but also engaged in strong covalent bonds with P. This weakens interactions with neighboring Li+ smoothing the way for their migration. When condensation of PO4 polyhedra leads to the formation of isolated O anions, the Li+ mobility is reduced, highlighting the importance of oxygen partial pressure control during synthesis. This detailed understanding of the structural mechanisms affecting Li+ mobility is the key for optimizing the conductivity of LiPON and other amorphous Li-ion conductors.

The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANNs). We show for the example of the amorphous LiSi alloy that around 1000 first-principles calculations are sufficient for the ANN-potential assisted sampling of low-energy atomic configurations in the entire amorphous LixSi phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of LixSi configurations using molecular dynamics simulations and a general ANN potential trained to ∼45 000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials.

The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANNs). We show for the example of the amorphous LiSi alloy that around 1000 first-principles calculations are sufficient for the ANN-potential assisted sampling of low-energy atomic configurations in the entire amorphous LixSi phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of LixSi configurations using molecular dynamics simulations and a general ANN potential trained to ∼45 000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials.

Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase quadratically with the number of chemical species. In this paper, we demonstrate that such a scaling can be avoided in practice. We show that a mathematically simple and computationally efficient descriptor with constant complexity is sufficient to represent transition-metal oxide compositions and biomolecules containing 11 chemical species with a precision of around 3 meV/atom. This insight removes a perceived bound on the utility of MLPs and paves the way to investigate the physics of previously inaccessible materials with more than ten chemical species.