Influence of Interlayer Cation Ordering on Na Transport in P2-Type Na0.67-xLiy Ni0.33-zMn0.67+zO2 for Sodium-Ion Batteries.

P2-type Na2/3Ni1/3Mn2/3O2 (PNNMO) has been extensively studied because of its desirable electrochemical properties as a positive electrode for sodium-ion batteries. PNNMO exhibits intralayer transition-metal ordering of Ni and Mn and intralayer Na+/vacancy ordering. The Na+/vacancy ordering is often considered a major impediment to fast Na+ transport and can be affected by transition-metal ordering. We show by neutron/X-ray diffraction and density functional theory (DFT) calculations that Li doping (Na2/3Li0.05Ni1/3Mn2/3O2, LFN5) promotes ABC-type interplanar Ni/Mn ordering without disrupting the Na+/vacancy ordering and creates low-energy Li-Mn-coordinated diffusion pathways. A structure model is developed to quantitatively identify both the intralayer cation mixing and interlayer cationic stacking fault densities. Quasielastic neutron scattering reveals that the Na+ diffusivity in LFN5 is enhanced by an order of magnitude over PNNMO, increasing its capacity at a high current. Na2/3Ni1/4Mn3/4O2 (NM13) lacks Na+/vacancy ordering but has diffusivity comparable to that of LFN5. However, NM13 has the smallest capacity at a high current. The high site energy of Mn-Mn-coordinated Na compared to that of Ni-Mn and higher density of Mn-Mn-coordinated Na+ sites in NM13 disrupts the connectivity of low-energy Ni-Mn-coordinated diffusion pathways. These results suggest that the interlayer ordering can be tuned through the control of composition, which has an equal or greater impact on Na+ diffusion than the Na+/vacancy ordering.

Enhanced Cycling Stability of All-Solid-State Lithium-Sulfur Battery through Nonconductive Polar Hosts.

All-solid-state lithium-sulfur batteries (ASSLSBs) are promising next-generation battery technologies with a high energy density and excellent safety. Because of the insulating nature of sulfur/Li2S, conventional cathode designs focus on developing porous hosts with high electronic conductivities such as porous carbon. However, carbon hosts boost the decomposition of sulfide electrolytes and suffer from sulfur detachment due to their weak bonding with sulfur/Li2S, resulting in capacity decays. Herein, we propose a counterintuitive design concept of host materials in which nonconductive polar mesoporous hosts can enhance the cycling life of ASSLSBs through mitigating the decomposition of adjacent electrolytes and bonding sulfur/Li2S steadily to avoid detachment. By using a mesoporous SiO2 host filled with 70 wt % sulfur as the cathode, we demonstrate steady cycling in ASSLSBs with a capacity reversibility of 95.1% in the initial cycle and a discharge capacity of 1446 mAh/g after 500 cycles at C/5 based on the mass of sulfur.

Healable and conductive sulfur iodide for solid-state Li-S batteries.

Solid-state Li-S batteries (SSLSBs) are made of low-cost and abundant materials free of supply chain concerns. Owing to their high theoretical energy densities, they are highly desirable for electric vehicles1-3. However, the development of SSLSBs has been historically plagued by the insulating nature of sulfur4,5 and the poor interfacial contacts induced by its large volume change during cycling6,7, impeding charge transfer among different solid components. Here we report an S9.3I molecular crystal with I2 inserted in the crystalline sulfur structure, which shows a semiconductor-level electrical conductivity (approximately 5.9 × 10-7 S cm-1) at 25 °C; an 11-order-of-magnitude increase over sulfur itself. Iodine introduces new states into the band gap of sulfur and promotes the formation of reactive polysulfides during electrochemical cycling. Further, the material features a low melting point of around 65 °C, which enables repairing of damaged interfaces due to cycling by periodical remelting of the cathode material. As a result, an Li-S9.3I battery demonstrates 400 stable cycles with a specific capacity retention of 87%. The design of this conductive, low-melting-point sulfur iodide material represents a substantial advancement in the chemistry of sulfur materials, and opens the door to the practical realization of SSLSBs.

Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3.

Oxide solid electrolytes (OSEs) have the potential to achieve improved safety and energy density for lithium-ion batteries, but their high grain-boundary (GB) resistance generally is a bottleneck. In the well-studied perovskite oxide solid electrolyte, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of grain boundaries is about three orders of magnitude lower than that of the bulk. In contrast, the related Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low grain boundary resistance for reasons yet unknown. Here, we use aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the atomic scale structure and composition of LSTZ0.75 grain boundaries. Vibrational electron energy loss spectroscopy is applied for the first time to reveal atomically resolved vibrations at grain boundaries of LSTZ0.75 and to characterize the otherwise unmeasurable Li distribution therein. We find that Li depletion, which is a major reason for the low grain boundary ionic conductivity of LLTO, is absent for the grain boundaries of LSTZ0.75. Instead, the low grain boundary resistivity of LSTZ0.75 is attributed to the formation of a nanoscale defective cubic perovskite interfacial structure that contained abundant vacancies. Our study provides new insights into the atomic scale mechanisms of low grain boundary resistivity.

Oxygen-Loss-Induced Structural Degradation in ε-LiVOPO4.

The ε-LiVOPO4 cathode for Li-ion batteries has attracted wide attention with its multivalent electronic states and improved discharge capacity of over 300 mAh/g. Oxygen loss stands as a potential cause for structural degradations of the ε-LiVOPO4 cathode and its derivatives but has been barely studied. Through in situ environmental transmission electron microscopy, we probe lattice oxygen loss and the associated structural degradations by spatially and temporally resolving the atomic-scale structural dynamics and phase transformation pathways in ε-LiVOPO4. We demonstrate that the mild oxygen loss at 400 °C induces a topotactic phase transformation of ε-LiVOPO4 → α-Li3V2(PO4)3 in the particle surface via a nucleation and growth mechanism, leading to the formation of a core-shell configuration. The phase transformation can be reversed by switching to an oxidizing environment, in which the α-Li3V2(PO4)3 is reoxidized to ε-LiVOPO4. By contrast, oxygen loss at higher temperatures of 500 and 600 °C results in a high concentration of oxygen vacancies that subsequently induces irreversible structural damages including lattice amorphization and formation of nanocavities. This work illustrates the fundamental mechanisms governing the structural failure of oxide cathodes and underlines possible strategies to overcome such issues by exploiting environmental constraints.

Thermodynamics and Kinetics of the Cathode-Electrolyte Interface in All-Solid-State Li-S Batteries.

Lithium-sulfur batteries (LSBs) are among the most promising energy storage technologies due to the low cost and high abundance of S. However, the issue of polysulfide shuttling with its corresponding capacity fading is a major impediment to its commercialization. Replacing traditional liquid electrolytes with solid-state electrolytes (SEs) is a potential solution. Here, we present a comprehensive study of the thermodynamics and kinetics of the cathode-electrolyte interface in all-solid-state LSBs using density functional theory based calculations and a machine learning interatomic potential. We find that among the major solid electrolyte chemistries (oxides, sulfides, nitrides, and halides), sulfide SEs are generally predicted to be the most stable against the S8 cathode, while the other SE chemistries are predicted to be highly electrochemically unstable. If the use of other SE chemistries is desired for other reasons, several binary and ternary sulfides (e.g., LiAlS2, Sc2S3, Y2S3) are predicted to be excellent buffer layers. Finally, an accurate moment tensor potential to study the S8|ß-Li3PS4 interface was developed using an active learning approach. Molecular dynamics (MD) simulations of large interface models (>1000s atoms) revealed that the most stable Li3PS4(100) surface tends to form interfaces with S8 with 2D channels and lower activation barriers for Li diffusion. These results provide critical new insights into the cathode-electrolyte interface design for next-generation all-solid-state LSBs.

Electrochemically induced amorphous-to-rock-salt phase transformation in niobium oxide electrode for Li-ion batteries.

Intercalation-type metal oxides are promising negative electrode materials for safe rechargeable lithium-ion batteries due to the reduced risk of Li plating at low voltages. Nevertheless, their lower energy and power density along with cycling instability remain bottlenecks for their implementation, especially for fast-charging applications. Here, we report a nanostructured rock-salt Nb2O5 electrode formed through an amorphous-to-crystalline transformation during repeated electrochemical cycling with Li+. This electrode can reversibly cycle three lithiums per Nb2O5, corresponding to a capacity of 269 mAh g-1 at 20 mA g-1, and retains a capacity of 191 mAh g-1 at a high rate of 1 A g-1. It exhibits superb cycling stability with a capacity of 225 mAh g-1 at 200 mA g-1 for 400 cycles, and a Coulombic efficiency of 99.93%. We attribute the enhanced performance to the cubic rock-salt framework, which promotes low-energy migration paths. Our work suggests that inducing crystallization of amorphous nanomaterials through electrochemical cycling is a promising avenue for creating unconventional high-performance metal oxide electrode materials.

A universal graph deep learning interatomic potential for the periodic table.

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past ten years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million materials from a screening of 31 million hypothetical crystal structures were identified to be potentially stable against existing Materials Project crystals based on M3GNet energies. Of the top 2,000 materials with the lowest energies above the convex hull, 1,578 were verified to be stable using density functional theory calculations. These results demonstrate a machine learning-accelerated pathway to the discovery of synthesizable materials with exceptional properties.

Inherent stochasticity during insulator-metal transition in VO2.

Vanadium dioxide (VO2), which exhibits a near-room-temperature insulator-metal transition, has great potential in applications of neuromorphic computing devices. Although its volatile switching property, which could emulate neuron spiking, has been studied widely, nanoscale studies of the structural stochasticity across the phase transition are still lacking. In this study, using in situ transmission electron microscopy and ex situ resistive switching measurement, we successfully characterized the structural phase transition between monoclinic and rutile VO2 at local areas in planar VO2/TiO2 device configuration under external biasing. After each resistive switching, different VO2 monoclinic crystal orientations are observed, forming different equilibrium states. We have evaluated a statistical cycle-to-cycle variation, demonstrated a stochastic nature of the volatile resistive switching, and presented an approach to study in-plane structural anisotropy. Our microscopic studies move a big step forward toward understanding the volatile switching mechanisms and the related applications of VO2 as the key material of neuromorphic computing.

Multiprincipal Component P2-Na0.6(Ti0.2Mn0.2Co0.2Ni0.2Ru0.2)O2 as a High-Rate Cathode for Sodium-Ion Batteries.

Mixing transition metal cations in nearly equiatomic proportions in layered oxide cathode materials is a new strategy for improving the performances of Na-ion batteries. The mixing of cations not only offers entropic stabilization of the crystal structure but also benefits the diffusion of Na ions with tuned diffusion activation energy barriers. In light of this strategy, a high-rate Na0.6(Ti0.2Mn0.2Co0.2Ni0.2Ru0.2)O2 cathode was designed, synthesized, and investigated, combining graph-based deep learning calculations and complementary experimental characterizations. This new cathode material delivers high discharge capacities of 164 mA g-1 at 0.1 C and 68 mAh g-1 at a very high rate of 86 C, demonstrating an outstanding high rate capability. Ex situ and operando synchrotron X-ray diffraction were used to reveal the detailed structural evolution of the cathode upon cycling. Using the climbing-image nudged elastic-band calculation and Ab initio molecular dynamics simulations, we show that the optimal transition metal composition enables a percolating network of low barrier pathways for fast, macroscopic Na diffusion, resulting in the observed high rate performance.

Atomistic simulations of dislocation mobility in refractory high-entropy alloys and the effect of chemical short-range order.

Refractory high-entropy alloys (RHEAs) are designed for high elevated-temperature strength, with both edge and screw dislocations playing an important role for plastic deformation. However, they can also display a significant energetic driving force for chemical short-range ordering (SRO). Here, we investigate mechanisms underlying the mobilities of screw and edge dislocations in the body-centered cubic MoNbTaW RHEA over a wide temperature range using extensive molecular dynamics simulations based on a highly-accurate machine-learning interatomic potential. Further, we specifically evaluate how these mechanisms are affected by the presence of SRO. The mobility of edge dislocations is found to be enhanced by the presence of SRO, whereas the rate of double-kink nucleation in the motion of screw dislocations is reduced, although this influence of SRO appears to be attenuated at increasing temperature. Independent of the presence of SRO, a cross-slip locking mechanism is observed for the motion of screws, which provides for extra strengthening for refractory high-entropy alloy system.

A framework for quantifying uncertainty in DFT energy corrections.

In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.

Database of ab initio L-edge X-ray absorption near edge structure.

The L-edge X-ray Absorption Near Edge Structure (XANES) is widely used in the characterization of transition metal compounds. Here, we report the development of a database of computed L-edge XANES using the multiple scattering theory-based FEFF9 code. The initial release of the database contains more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput computational workflow. The data is disseminated through the Materials Project and addresses a critical need for L-edge XANES spectra among the research community.

A stable cathode-solid electrolyte composite for high-voltage, long-cycle-life solid-state sodium-ion batteries.

Rechargeable solid-state sodium-ion batteries (SSSBs) hold great promise for safer and more energy-dense energy storage. However, the poor electrochemical stability between current sulfide-based solid electrolytes and high-voltage oxide cathodes has limited their long-term cycling performance and practicality. Here, we report the discovery of the ion conductor Na3-xY1-xZrxCl6 (NYZC) that is both electrochemically stable (up to 3.8 V vs. Na/Na+) and chemically compatible with oxide cathodes. Its high ionic conductivity of 6.6 × 10-5 S cm-1 at ambient temperature, several orders of magnitude higher than oxide coatings, is attributed to abundant Na vacancies and cooperative MCl6 rotation, resulting in an extremely low interfacial impedance. A SSSB comprising a NaCrO2 + NYZC composite cathode, Na3PS4 electrolyte, and Na-Sn anode exhibits an exceptional first-cycle Coulombic efficiency of 97.1% at room temperature and can cycle over 1000 cycles with 89.3% capacity retention at 40 °C. These findings highlight the immense potential of halides for SSSB applications.

Learning properties of ordered and disordered materials from multi-fidelity data.

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties, their practical utility is limited by the scarcity of high-fidelity data. Here, we develop multi-fidelity graph networks as a universal approach to achieve accurate predictions of materials properties with small data sizes. As a proof of concept, we show that the inclusion of low-fidelity Perdew-Burke-Ernzerhof band gaps greatly enhances the resolution of latent structural features in materials graphs, leading to a 22-45% decrease in the mean absolute errors of experimental band gap predictions. We further demonstrate that learned elemental embeddings in materials graph networks provide a natural approach to model disorder in materials, addressing a fundamental gap in the computational prediction of materials properties.

Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure.

Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.

A disordered rock salt anode for fast-charging lithium-ion batteries.

Rechargeable lithium-ion batteries with high energy density that can be safely charged and discharged at high rates are desirable for electrified transportation and other applications1-3. However, the sub-optimal intercalation potentials of current anodes result in a trade-off between energy density, power and safety. Here we report that disordered rock salt4,5 Li3+xV2O5 can be used as a fast-charging anode that can reversibly cycle two lithium ions at an average voltage of about 0.6 volts versus a Li/Li+ reference electrode. The increased potential compared to graphite6,7 reduces the likelihood of lithium metal plating if proper charging controls are used, alleviating a major safety concern (short-circuiting related to Li dendrite growth). In addition, a lithium-ion battery with a disordered rock salt Li3V2O5 anode yields a cell voltage much higher than does a battery using a commercial fast-charging lithium titanate anode or other intercalation anode candidates (Li3VO4 and LiV0.5Ti0.5S2)8,9. Further, disordered rock salt Li3V2O5 can perform over 1,000 charge-discharge cycles with negligible capacity decay and exhibits exceptional rate capability, delivering over 40 per cent of its capacity in 20 seconds. We attribute the low voltage and high rate capability of disordered rock salt Li3V2O5 to a redistributive lithium intercalation mechanism with low energy barriers revealed via ab initio calculations. This low-potential, high-rate intercalation reaction can be used to identify other metal oxide anodes for fast-charging, long-life lithium-ion batteries.