Sulfurized Two-Dimensional Conductive Metal-Organic Framework as a High-Performance Cathode Material for Rechargeable Mg Batteries.

Rechargeable Mg batteries are a promising energy storage technology to overcome the limitations inherent to Li ion batteries. A critical challenge in advancing Mg batteries is the lack of suitable cathode materials. In this work, we report a cathode design that incorporates S functionality into two-dimensional metal-organic-frameworks (2D-MOFs). This new cathode material enables good Mg2+ storage capacity and outstanding cyclability. It was found that upon the initial Mg2+ insertion and disinsertion, there is an apparent structural transformation that crumbles the layered 2D framework, leading to amorphization. The resulting material serves as the active material to host Mg2+ through reduction and/or oxidation of S and, to a limited extent, O. The reversible nature of S and O redox chemistry was confirmed by spectroscopic characterizations and validated by density functional calculations. Importantly, during the Mg2+ insertion and disinsertion process, the 2D nature of the framework was maintained, which plays a key role in enabling the high reversibility of the MOF cathode.

PW-SMD: A Plane-Wave Implicit Solvation Model Based on Electron Density for Surface Chemistry and Crystalline Systems in Aqueous Solution.

Electron density-based implicit solvation models are a class of techniques for quantifying solvation effects and calculating free energies of solvation without an explicit representation of solvent molecules. Integral to the accuracy of solvation modeling is the proper definition of the solvation shell separating the solute molecule from the solvent environment, allowing for a physical partitioning of the free energies of solvation. Unlike state-of-the-art implicit solvation models for molecular quantum chemistry calculations, e.g., the solvation model based on solute electron density (SMD), solvation models for systems under periodic boundary conditions with plane-wave (PW) basis sets have been limited in their accuracy. Furthermore, a unified implicit solvation model with both homogeneous solution-phase and heterogeneous interfacial structures treated on equal footing is needed. In order to address this challenge, we developed a high-accuracy solvation model for periodic PW calculations that is applicable to molecular, ionic, interfacial, and bulk-phase chemistry. Our model, PW-SMD, is an extension of the SMD molecular solvation model to periodic systems in water. The free energy of solvation is partitioned into the electrostatic and cavity-dispersion-solvent structure (CDS) contributions. The electrostatic contributions of the solvation shell surrounding solute structures are parametrized based on their geometric and physical properties. In addition, the nonelectrostatic contribution to the solvation energy is accounted for by extending the CDS formalism of SMD to incorporate periodic boundary conditions. We validate the accuracy and robustness of our solvation model by comparing predicted solvation free energies against experimental data for molecular and ionic systems, carved-cluster composite energetic models of solvated reaction energies and barriers on surface systems, and deep-learning-accelerated ab initio molecular dynamics (AIMD). Our developed periodic implicit solvation model shows significantly improved accuracy compared to previous work (namely, solvation models in aqueous solution) and can be applied to simulate solvent effects in a wide range of surface and crystalline materials.

Physical Prior Mean Function-Driven Gaussian Processes Search for Minimum-Energy Reaction Paths with a Climbing-Image Nudged Elastic Band: A General Method for Gas-Phase, Interfacial, and Bulk-Phase Reactions.

The climbing-image nudged elastic band (CI-NEB) method serves as an indispensable tool for computational chemists, offering insight into minimum-energy reaction paths (MEPs) by delineating both transition states (TSs) and intermediate nonstationary structures along reaction coordinates. However, executing CI-NEB calculations for reactions with extensive reaction coordinate spans necessitates a large number of images to ensure a reliable convergence of the MEPs and TS structures, presenting a computationally demanding optimization challenge, even with mildly costly electronic-structure methods. In this study, we advocate for the utilization of physically inspired prior mean function-based Gaussian processes (GPs) to expedite MEP exploration and TS optimization via the CI-NEB method. By incorporating reliable prior physical approximations into potential energy surface (PES) modeling, we demonstrate enhanced efficiency in multidimensional CI-NEB optimization with surrogate-based optimizers. Our physically informed GP approach not only outperforms traditional nonsurrogate-based optimizers in optimization efficiency but also on-the-fly learns the reaction path valley during optimization, culminating in significant advancements. The surrogate PES derived from our optimization exhibits high accuracy compared to true PES references, aligning with our emphasis on leveraging reliable physical priors for robust and efficient posterior mean learning in GPs. Through a systematic benchmark study encompassing various reaction pathways, including gas-phase, bulk-phase, and interfacial/surface reactions, our physical GPs consistently demonstrate superior efficiency and reliability. For instance, they outperform the popular fast inertial relaxation engine optimizer by approximately a factor of 10, showcasing their versatility and efficacy in exploring reaction mechanisms and surface reaction PESs.

Polysulfides in Magnesium-Sulfur Batteries.

Mg-S batteries hold great promise as a potential alternative to Li-based technologies. Their further development hinges on solving a few key challenges, including the lower capacity and poorer cycling performance when compared to Li counterparts. At the heart of the issues is the lack of knowledge on polysulfide chemical behaviors in the Mg-S battery environment. In this Review, a comprehensive overview of the current understanding of polysulfide behaviors in Mg-S batteries is provided. First, a systematic summary of experimental and computational techniques for polysulfide characterization is provided. Next, conversion pathways for Mg polysulfide species within the battery environment are discussed, highlighting the important role of polysulfide solubility in determining reaction kinetics and overall battery performance. The focus then shifts to the negative effects of polysulfide shuttling on Mg-S batteries. The authors outline various strategies for achieving an optimal balance between polysulfide solubility and shuttling, including the use of electrolyte additives, polysulfide-trapping materials, and dual-functional catalysts. Based on the current understanding, the directions for further advancing knowledge of Mg polysulfide chemistry are identified, emphasizing the integration of experiment with computation as a powerful approach to accelerate the development of Mg-S battery technology.

Exploring torsional conformer space with physical prior mean function-driven meta-Gaussian processes.

We present a novel approach for systematically exploring the conformational space of small molecules with multiple internal torsions. Identifying unique conformers through a systematic conformational search is important for obtaining accurate thermodynamic functions (e.g., free energy), encompassing contributions from the ensemble of all local minima. Traditional geometry optimizers focus on one structure at a time, lacking transferability from the local potential-energy surface (PES) around a specific minimum to optimize other conformers. In this work, we introduce a physics-driven meta-Gaussian processes (meta-GPs) method that not only enables efficient exploration of target PES for locating local minima but, critically, incorporates physical surrogates that can be applied universally across the optimization of all conformers of the same molecule. Meta-GPs construct surrogate PESs based on the optimization history of prior conformers, dynamically selecting the most suitable prior mean function (representing prior knowledge in Bayesian learning) as a function of the optimization progress. We systematically benchmarked the performance of multiple GP variants for brute-force conformational search of amino acids. Our findings highlight the superior performance of meta-GPs in terms of efficiency, comprehensiveness of conformer discovery, and the distribution of conformers compared to conventional non-surrogate optimizers and other non-meta-GPs. Furthermore, we demonstrate that by concurrently optimizing, training GPs on the fly, and learning PESs, meta-GPs exhibit the capacity to generate high-quality PESs in the torsional space without extensive training data. This represents a promising avenue for physics-based transfer learning via meta-GPs with adaptive priors in exploring torsional conformer space.

Characterizing Grain Boundary Effects on Mg2+ Conduction in Metal-Organic Frameworks.

Next-generation materials for fast ion conduction have the potential to revolutionize battery technology. Metal-organic frameworks (MOFs) are promising candidates for achieving this goal. Given their structural diversity, the design of efficient MOF-based conductors can be accelerated by a detailed understanding and accurate prediction of ion conductivity. However, the polycrystalline nature of solid-state materials requires consideration of grain boundary effects, which is complicated by challenges in characterizing grain boundary structures and simulating ensemble transport processes. To address this, we have developed an approach for modeling ion transport at grain boundaries and predicting their contribution to conductivity. Mg2+ conduction in the Mg-MOF-74 thin film was studied as a representative system. Using computational techniques and guided by experiments, we investigated the structural details of MOF grain boundary interfaces to determine accessible Mg2+ transport pathways. Computed transport kinetics were input into a simplified MOF nanocrystal model, which combines ion transport in the bulk structure and at grain boundaries. The model predicts Mg2+ conductivity in the MOF-74 film within chemical accuracy (<1 kcal/mol activation energy difference), validating our approach. Physically, Mg2+ conduction in MOF-74 is inhibited by strong Mg2+ binding at grain boundaries, such that only a small fraction of grain boundary alignments allow for fast Mg2+ transport. This results in a 2-3 order-of-magnitude reduction in conductivity, illustrating the critical impact of the grain boundary contribution. Overall, our work provides a computation-aided platform for molecular-level understanding of grain boundary effects and quantitative prediction of ion conductivity. Combined with experimental measurements, it can serve as a synergistic tool for characterizing the grain boundary composition of MOF-based conductors.

A spur to molecular geometry optimization: Gradient-enhanced universal kriging with on-the-fly adaptive ab initio prior mean functions in curvilinear coordinates.

We present a molecular geometry optimization algorithm based on the gradient-enhanced universal kriging (GEUK) formalism with ab initio prior mean functions, which incorporates prior physical knowledge to surrogate-based optimization. In this formalism, we have demonstrated the advantage of allowing the prior mean functions to be adaptive during geometry optimization over a pre-fixed choice of prior functions. Our implementation is general and flexible in two senses. First, the optimizations on the surrogate surface can be in both Cartesian coordinates and curvilinear coordinates. We explore four representative curvilinear coordinates in this work, including the redundant Coulombic coordinates, the redundant internal coordinates, the non-redundant delocalized internal coordinates, and the non-redundant hybrid delocalized internal Z-matrix coordinates. We show that our GEUK optimizer accelerates geometry optimization as compared to conventional non-surrogate-based optimizers in internal coordinates. We further showcase the power of the GEUK with on-the-fly adaptive priors for efficient optimizations of challenging molecules (Criegee intermediates) with a high-accuracy electronic structure method (the coupled-cluster method). Second, we present the usage of internal coordinates under the complete curvilinear scheme. A complete curvilinear scheme performs both surrogate potential-energy surface (PES) fitting and structure optimization entirely in the curvilinear coordinates. Our benchmark indicates that the complete curvilinear scheme significantly reduces the cost of structure minimization on the surrogate compared to the incomplete curvilinear scheme, which fits the surrogate PES in curvilinear coordinates partially and optimizes a structure in Cartesian coordinates through curvilinear coordinates via the chain rule.

Earth-abundant photocatalyst for H2 generation from NH3 with light-emitting diode illumination.

Catalysts based on platinum group metals have been a major focus of the chemical industry for decades. We show that plasmonic photocatalysis can transform a thermally unreactive, earth-abundant transition metal into a catalytically active site under illumination. Fe active sites in a Cu-Fe antenna-reactor complex achieve efficiencies very similar to Ru for the photocatalytic decomposition of ammonia under ultrafast pulsed illumination. When illuminated with light-emitting diodes rather than lasers, the photocatalytic efficiencies remain comparable, even when the scale of reaction increases by nearly three orders of magnitude. This result demonstrates the potential for highly efficient, electrically driven production of hydrogen from an ammonia carrier with earth-abundant transition metals.

Dual-Level Training of Gaussian Processes with Physically Inspired Priors for Geometry Optimizations.

Gaussian process (GP) regression has been recently developed as an effective method in molecular geometry optimization. The prior mean function is one of the crucial parts of the GP. We design and validate two types of physically inspired prior mean functions: force-field-based priors and posterior-type priors. In this work, we implement a dual-level training (DLT) optimizer for the posterior-type priors. The DLT optimizers can be considered as a class of optimization algorithms that belong to the delta-machine learning paradigm but with several major differences compared to the previously proposed algorithms in the same paradigm. In the first level of the DLT, we incorporate the classical mechanical descriptions of the equilibrium geometries into the prior function, which enhances the performance of the GP optimizer as compared to the one using a constant (or zero) prior. In the second level, we utilize the surrogate potential energy surfaces (PESs), which incorporate the physics learned in the first-level training, as the prior function to refine the model performance further. We find that the force-field-based priors and posterior-type priors reduce the overall optimization steps by a factor of 2-3 when compared to the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimizer as well as the constant-prior GP optimizer proposed in previous works. We also demonstrate the potential of recovering the real PESs with GP with a force-field prior. This work shows the importance of including domain knowledge as an ingredient in the GP, which offers a potentially robust learning model for molecular geometry optimization and for exploring molecular PESs.

Reaction of SO3 with HONO2 and Implications for Sulfur Partitioning in the Atmosphere.

Sulfur trioxide is a critical intermediate for the sulfur cycle and the formation of sulfuric acid in the atmosphere. The traditional view is that sulfur trioxide is removed by water vapor in the troposphere. However, the concentration of water vapor decreases significantly with increasing altitude, leading to longer atmospheric lifetimes of sulfur trioxide. Here, we utilize a dual-level strategy that combines transition state theory calculated at the W2X//DF-CCSD(T)-F12b/jun'-cc-pVDZ level, with variational transition state theory with small-curvature tunneling from direct dynamics calculations at the M08-HX/MG3S level. We also report the pressure-dependent rate constants calculated using the system-specific quantum Rice-Ramsperger-Kassel (SS-QRRK) theory. The present findings show that falloff effects in the SO3 + HONO2 reaction are pronounced below 1 bar. The SO3 + HONO2 reaction can be a potential removal reaction for SO3 in the stratosphere and for HONO2 in the troposphere, because the reaction can potentially compete well with the SO3 + 2H2O reaction between 25 and 35 km, as well as the OH + HONO2 reaction. The present findings also suggest an unexpected new product from the SO3 + HONO2 reaction, which, although very short-lived, would have broad implications for understanding the partitioning of sulfur in the stratosphere and the potential for the SO3 reaction with organic acids to generate organosulfates without the need for heterogeneous chemistry.

Geometry meta-optimization.

Recent work has demonstrated the promise of using machine-learned surrogates, in particular, Gaussian process (GP) surrogates, in reducing the number of electronic structure calculations (ESCs) needed to perform surrogate model based (SMB) geometry optimization. In this paper, we study geometry meta-optimization with GP surrogates where a SMB optimizer additionally learns from its past "experience" performing geometry optimization. To validate this idea, we start with the simplest setting where a geometry meta-optimizer learns from previous optimizations of the same molecule with different initial-guess geometries. We give empirical evidence that geometry meta-optimization with GP surrogates is effective and requires less tuning compared to SMB optimization with GP surrogates on the ANI-1 dataset of off-equilibrium initial structures of small organic molecules. Unlike SMB optimization where a surrogate should be immediately useful for optimizing a given geometry, a surrogate in geometry meta-optimization has more flexibility because it can distribute its ESC savings across a set of geometries. Indeed, we find that GP surrogates that preserve rotational invariance provide increased marginal ESC savings across geometries. As a more stringent test, we also apply geometry meta-optimization to conformational search on a hand-constructed dataset of hydrocarbons and alcohols. We observe that while SMB optimization and geometry meta-optimization do save on ESCs, they also tend to miss higher energy conformers compared to standard geometry optimization. We believe that further research into characterizing the divergence between GP surrogates and potential energy surfaces is critical not only for advancing geometry meta-optimization but also for exploring the potential of machine-learned surrogates in geometry optimization in general.

Large Pressure Effects Caused by Internal Rotation in the s-cis-syn-Acrolein Stabilized Criegee Intermediate at Tropospheric Temperature and Pressure.

Criegee intermediates are important atmospheric oxidants, and quantitative kinetics for stabilized Criegee intermediates are key parameters for atmospheric modeling but are still limited. Here we report barriers and rate constants for unimolecular reactions of s-cis-syn-acrolein oxide (scsAO), in which the vinyl group makes it a prototype for Criegee intermediates produced in the ozonolysis of isoprene. We find that the MN15-L and M06-2X density functionals have CCSD(T)/CBS accuracy for the unimolecular cyclization and stereoisomerization of scsAO. We calculated high-pressure-limit rate constants by the dual-level strategy that combines (a) high-level wave function-based conventional transition-state theory (which includes coupled-cluster calculations with quasiperturbative inclusion of quadruple excitations because of the strongly multiconfigurational character of the electronic wave function) and (b) canonical variational transition-state theory with small-curvature tunneling based on a validated density functional. We calculated pressure-dependent rate constants both by system-specific quantum Rice-Ramsperger-Kassel theory and by solving the master equation. We report rate constants for unimolecular reactions of scsAO over the full range of atmospheric temperature and pressure. We found that the unimolecular reaction rates of this larger-than-previously studied Criegee intermediate depend significantly on pressure. Particularly, we found that falloff effects decrease the effective unimolecular cyclization rate constant of scsAO by about a factor of 3, but the unimolecular reaction is still the dominant atmospheric sink for scsAO at low altitudes. The large falloff caused by the inclusion of the stereoisomerization channel in the master equation calculations has broad implications for mechanistic analysis of reactions with competitive internal rotations that can produce stable rotamers.

Molecular-Level Insights into Selective Transport of Mg2+ in Metal-Organic Frameworks.

Metal-organic frameworks (MOF) are promising media for achieving solid-state Mg2+ conduction and developing a magnesium-based battery. To this end, the chemical behavior and transport properties of an Mg(TFSI)2/DME electrolyte system inside Mg-MOF-74 were studied by density functional theory (DFT). We found that inside the MOF chemical environment, solvent and anion molecules occupy the coordinatively unsaturated open metal sites of Mg-MOF-74, while Mg2+ ions adsorb directly onto the carboxylate group of the MOF organic linker. These predicted binding geometries were further corroborated by IR spectroscopy. We computed the free energies of desolvation of Mg2+ ions inside MOF to investigate the capacity of Mg-MOF-74 thin film to act as a separator for selective Mg2+ transport. We showed that Mg-MOF-74 could facilitate partial, but not full, desolvation of Mg2+. We found that the dominant minimum-energy pathway (MEP) for Mg2+ conduction inside Mg-MOF-74 corresponds to a "solvent hopping" mechanism, with an energy barrier of 4.4 kcal/mol. The molar conductivity of Mg2+ associated with the idealized solvent hopping mechanism along the MOF one-dimensional channel was predicted to be 2.4 × 10-3 S cm-1 M-1, which is one to two orders of magnitude greater than the experimentally measured value of 1.2 × 10-4 S cm-1 M-1 (with an estimated Mg2+ concentration). We have discussed several possible factors contributing to this apparent discrepancy. The current work demonstrates the validity of the computational strategies applied and the structural models constructed for the understanding of fast and selective Mg2+ transport in Mg-MOF-74, which serves as a cornerstone for studying transport of multivalent ions in MOFs. Furthermore, it provides detailed molecular-level insights that are not yet accessible experimentally.

Hot carrier multiplication in plasmonic photocatalysis.

Light-induced hot carriers derived from the surface plasmons of metal nanostructures have been shown to be highly promising agents for photocatalysis. While both nonthermal and thermalized hot carriers can potentially contribute to this process, their specific role in any given chemical reaction has generally not been identified. Here, we report the observation that the H2-D2 exchange reaction photocatalyzed by Cu nanoparticles is driven primarily by thermalized hot carriers. The external quantum yield shows an intriguing S-shaped intensity dependence and exceeds 100% for high light intensities, suggesting that hot carrier multiplication plays a role. A simplified model for the quantum yield of thermalized hot carriers reproduces the observed kinetic features of the reaction, validating our hypothesis of a thermalized hot carrier mechanism. A quantum mechanical study reveals that vibrational excitations of the surface Cu-H bond is the likely activation mechanism, further supporting the effectiveness of low-energy thermalized hot carriers in photocatalyzing this reaction.

Atmospheric Kinetics: Bimolecular Reactions of Carbonyl Oxide by a Triple-Level Strategy.

Criegee intermediates in the atmosphere serve as oxidizing agents to initiate aerosol formation, which are particularly important for atmospheric modeling, and understanding their kinetics is one of the current outstanding challenges in climate change modeling. Because experimental kinetics are still limited, we must rely on theory for the complete picture, but obtaining absolute rates from theory is a formidable task. Here, we report the bimolecular reaction kinetics of carbonyl oxide with ammonia, hydrogen sulfide, formaldehyde, and water dimer by designing a triple-level strategy that combines (i) benchmark results close to the complete-basis limit of coupled-cluster theory with the single, double, triple, and quadruple excitations (CCSDTQ/CBS), (ii) a new hybrid meta density functional (M06CR) specifically optimized for reactions of Criegee intermediates, and (iii) variational transition-state theory with both variable rection coordinates and optimized reaction paths, with multidimensional tunneling, and with pressure effects. For (i) we have found that quadruple excitations are required to obtain quantitative reaction barriers, and we designed new composite methods and strategies to reach CCSDTQ/CBS accuracy. The present findings show that (i) the CH2OO + HCHO reaction can make an important contribution to the sink of HCHO under wide atmospheric conditions in the gas phase and that (ii) CH2OO + (H2O)2 dominates over the CH2OO + H2O reaction below 10 km.

Enabling Lithium Metal Anode in Nonflammable Phosphate Electrolyte with Electrochemically Induced Chemical Reactions.

Lithium metal anode holds great promises for next-generation battery technologies but is notoriously difficult to work with. The key to solving this challenge is believed to lie in the ability of forming stable solid-electrolyte interphase (SEI) layers. To further address potential safety issues, it is critical to achieve this goal in nonflammable electrolytes. Building upon previous successes in forming stable SEI in conventional carbonate-based electrolytes, here we report that reversible Li stripping/plating could be realized in triethyl phosphate (TEP), a known flame retardant. The critical enabling factor of our approach was the introduction of oxygen, which upon electrochemical reduction induces the initial decomposition of TEP and produces Li3 PO4 and poly-phosphates. Importantly, the reaction was self-limiting, and the resulting material regulated Li plating by limiting dendrite formation. In effect, we obtained a functional SEI on Li metal in a nonflammable electrolyte. When tested in a symmetric Li∥Li cell, more than 300 cycles of stripping/plating were measured at a current density of 0.5 mA cm-2 . Prototypical Li-O2 and Li-ion batteries were also fabricated and tested to further support the effectiveness of this strategy. The mechanism by which the SEI forms was studied by density functional theory (DFT), and the predictions were corroborated by the successful detection of the intermediates and products.

First-Principles Insights into Plasmon-Induced Catalysis.

The size- and shape-controlled enhanced optical response of metal nanoparticles (NPs) is referred to as a localized surface plasmon resonance (LSPR). LSPRs result in amplified surface and interparticle electric fields, which then enhance light absorption of the molecules or other materials coupled to the metallic NPs and/or generate hot carriers within the NPs themselves. When mediated by metallic NPs, photocatalysis can take advantage of this unique optical phenomenon. This review highlights the contributions of quantum mechanical modeling in understanding and guiding current attempts to incorporate plasmonic excitations to improve the kinetics of heterogeneously catalyzed reactions. A range of first-principles quantum mechanics techniques has offered insights, from ground-state density functional theory (DFT) to excited-state theories such as multireference correlated wavefunction methods. Here we discuss the advantages and limitations of these methods in the context of accurately capturing plasmonic effects, with accompanying examples.

Predicting Bond Dissociation Energies and Bond Lengths of Coordinatively Unsaturated Vanadium-Ligand Bonds.

Understanding the electronic structure of coordinatively unsaturated transition-metal compounds and predicting their physical properties are of great importance for catalyst design. Bond dissociation energy De and bond length re are two of the fundamental quantities for which good predictions are important for a successful design strategy. In the present work, recent experimentally measured bond energies and bond lengths of VX diatomic molecules (X = C, N, S) are used as a gauge to consider the utility of a number of electronic structure methods. Single-reference methods are one focus because of their efficiency and utility in practical calculations, and multireference configuration interaction (MRCISD) methods and a composite coupled cluster (CCC) method are a second focus because of their potential high accuracy. The comparison is especially challenging because of the large multireference M diagnostics of these molecules, in the range 0.15-0.19. For the single-reference methods, Kohn-Sham density functional theory (KS-DFT) has been tested with a variety of approximate exchange-correlation functionals. Of these, MOHLYP provides the bond dissociation energies in best agreement with experiments, and BLYP provides the bond lengths that are in best agreement with experiments; but by requiring good performance for both the De and re of the vanadium compounds, MOHLYP, MN12-L, MGGA_MS1, MGGA_MS0, O3LYP, and M06-L are the most highly recommended functionals. The CCC calculations include up to connected pentuple excitations for the valence electrons and up to connected quadruple excitations for the core-valence terms; this results in highly accurate dissociation energies and good bond lengths. Averaged over the three molecules, the mean unsigned deviation of CCC bond energies from experimental ones is only 0.4 kcal/mol, demonstrating excellent convergence of theory and experiments.

Salt-rich solid electrolyte interphase for safer high-energy-density Li metal batteries with limited Li excess.

We propose a carbonate-based electrolyte optimized with dual cations and ionic liquid for high-efficiency Li metal batteries with a high-voltage cathode. An average coulombic efficiency of Li deposition of 99.6% is achieved due to the salt-rich solid electrolyte interphase and Na guided uniform Li plating. The Li||NCM811 cells can be cycled with limited Li (N/P = 1) over 90 cycles. An additional advantage is that it improves the thermal stability of the NCM811 cathode.