Superionic lithium conductivity has only been discovered in a few classes of materials, mostly found in thiophosphates and rarely in oxides. Herein, we reveal that corner-sharing connectivity of the oxide crystal structure framework promotes superionic conductivity which we rationalize from their distorted lithium environment and reduced interaction between lithium and non-Li cations. By performing a high-throughput search for materials with this feature, we discover 10 novel oxide frameworks predicted to exhibit superionic conductivity-from which we experimentally demonstrate LiGa(SeO3)2 with a bulk ionic conductivity of 0.11 mS/cm and activation energy of 0.17 eV. Our findings provide new insight into the factors that govern fast lithium mobility in oxide materials and will accelerate the development of novel oxide electrolytes for all solid-state batteries.

The key component in lithium solid‐state batteries (SSBs) is the solid electrolyte composed of lithium superionic conductors (SICs). Lithium oxide SICs offer improved electrochemical and chemical stability compared with sulfides, and their recent advancements have largely been achieved using materials in the garnet‐ and NASICON (sodium superionic conductor)‐ structured families. In this work, using the ion‐conduction mechanisms in garnet and NASICON as inspiration, a common pattern of an “activated diffusion network” and three structural features that are beneficial for superionic conduction: a 3D percolation Li diffusion network, short distances between occupied Li sites, and the “homogeneity” of the transport path are identified. A high‐throughput computational screening is performed to search for new lithium oxide SICs that share these features. From this search, seven candidates are proposed exhibiting high room‐temperature ionic conductivity evaluated using ab initio molecular dynamics simulations. Their structural frameworks including spinel, oxy‐argyrodite, sodalite, and LiM(SeO3)2 present new opportunities for enriching the structural families of lithium oxide SICs.

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Revealing the local structure of solid electrolytes (SEs) with electron microscopy is critical for the fundamental understanding of the performance of solid-state batteries (SSBs). However, the intrinsic structural information in the SSB can be misleading if the sample’s interactions with the electron beams are not fully understood. In this work, we systematically investigate the effect of electron beams on Al-doped lithium lanthanum zirconium oxide (LLZO) under different imaging conditions. Li metal is observed to grow directly on the clean surface of LLZO. The Li metal growth kinetics and the morphology obtained are found to be heavily influenced by the temperature, accelerating voltage, and electron beam intensity. We prove that the lithium growth is due to the LLZO delithiation activated by a positive charging effect under electron beam emission. Our results deepen the understanding of the electron beam impact on SEs and provide guidance for battery material characterization using electron microscopy.

We present a comprehensive DFT study on modeling of ferromagnetic and paramagnetic states of uranium hydride, which can facilitate the design of multiscale modeling of uranium hydride.

Amorphous Li–P–S materials have been widely used as solid-state electrolytes for all-solid-state batteries because of their high ionic conductivity (10–4 to 10–3 S cm–1) as well as good synthetic accessibility and processability. Despite the potential of these materials, their amorphous structures have made it challenging to quantify the relation between the structure and conductivity. In this paper, we use ab initio molecular dynamics simulations to investigate the role of the local structure and density in determining the conductivity of amorphous Li–P–S structures with different polyanion units. We observe similar rates for Li-ion hopping regardless of the local P–S polyanion environment in these amorphous materials, indicating that the path connectivity at a larger length scale may be controlling the overall Li conductivity. This finding will serve as an important guideline in the continued development of amorphous solid electrolytes for advanced all-solid-state batteries.

Thermodynamics has strong predictive power for materials synthesis by identifying the stability region of target phases, but does not give explicit information about the relative competitiveness of undesired byproduct phases in synthesis. In this work, we propose a quantitative and computable measure to guide the selection of synthesis conditions. Defining thermodynamic competition as the difference in driving force between a target phase and its competing phases, we hypothesize that phase-pure synthesis becomes more likely when this thermodynamic competition is minimized. We systematically validate this hypothesis with two approaches: (1) we analyze large-scale solution synthesis procedures as text-mined from the literature, and show that experimentally-optimized synthesis conditions are near the predicted thermodynamic optimum point, and (2) direct experimental evaluation of synthesis in LiIn(IO3)4 and LiFePO4, which show that phase-pure synthesis occurs only when thermodynamic competition is minimized. Our work demonstrates that a quantitative assessment of thermodynamic competition is an effective descriptor for synthesis optimization and a promising tool for optimizing aqueous solution-based experimental synthesis conditions.

The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials. Although classical force-fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab-initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study many technologically relevant phenomena, such as reactions, ion migrations, phase transformations, and degradation.In this work, we present the Crystal Hamiltonian Graph neural Network (CHGNet) as a novel machine-learning interatomic potential (MLIP), using a graph-neural-network-based force-field to model a universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses, and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory static and relaxation trajectories of ∼ 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in LixMnO2, the finite temperature phase diagram for LixFePO4 and Li diffusion in garnet conductors. We critically analyze the significance of including charge information for capturing appropriate chemistry, and we provide new insights into ionic systems with additional electronic degrees of freedom that can not be observed by previous MLIPs.