The challenges and opportunities of battery-powered flight.

Aircraft, and the aviation ecosystem in which they operate, are shaped by complex trades among technical requirements, economics and environmental concerns, all built on a foundation of safety. This Perspective explores the requirements of battery-powered aircraft and the chemistries that hold promise to enable them. The difference between flight and terrestrial needs and chemistries are highlighted. Safe, usable specific energy rather than cost is the major constraint for aviation. We conclude that battery packs suitable for flight with specific energy approaching 600 kilowatt hours per kilogram may be achievable in the next decade given sufficient investment targeted at aeronautical applications.

Tomographic reconstruction of oxygen orbitals in lithium-rich battery materials.

The electrification of heavy-duty transport and aviation will require new strategies to increase the energy density of electrode materials1,2. The use of anionic redox represents one possible approach to meeting this ambitious target. However, questions remain regarding the validity of the O2-/O- oxygen redox paradigm, and alternative explanations for the origin of the anionic capacity have been proposed3, because the electronic orbitals associated with redox reactions cannot be measured by standard experiments. Here, using high-energy X-ray Compton measurements together with first-principles modelling, we show how the electronic orbital that lies at the heart of the reversible and stable anionic redox activity can be imaged and visualized, and its character and symmetry determined. We find that differential changes in the Compton profile with lithium-ion concentration are sensitive to the phase of the electronic wave function, and carry signatures of electrostatic and covalent bonding effects4. Our study not only provides a picture of the workings of a lithium-rich battery at the atomic scale, but also suggests pathways to improving existing battery materials and designing new ones.

Twisto-Electrochemical Activity Volcanoes in Trilayer Graphene.

In this work, we develop a twist-dependent electrochemical activity map, combining a low-energy continuum electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer graphene. We identify a counterintuitive rate enhancement region spanning the magic angle curve and incommensurate twists in the system geometry. We find a broad activity peak with a ruthenium hexamine redox couple in regions corresponding to both magic angles and incommensurate angles, a result qualitatively distinct from the twisted bilayer case. Flat bands and incommensurability offer new avenues for reaction rate enhancements in electrochemical transformations.

The promise of energy-efficient battery-powered urban aircraft.

Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 mi of range with payload equivalents of up to seven passengers. Novel UAM aircraft consume between 130 Wh/passenger-mi and ∼ 1,200 Wh/passenger-mi depending on the design and utilization, compared to an expected consumption of over 220 Wh/passenger-mi and 1,000 Wh/passenger-mi for terrestrial electric vehicles and combustion engine vehicles, respectively. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power and energy, while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.

Combining pressure and electrochemistry to synthesize superhydrides.

Recently, superhydrides have been computationally identified and subsequently synthesized with a variety of metals at very high pressures. In this work, we evaluate the possibility of synthesizing superhydrides by uniquely combining electrochemistry and applied pressure. We perform computational searches using density functional theory and particle swarm optimization calculations over a broad range of pressures and electrode potentials. Using a thermodynamic analysis, we construct pressure-potential phase diagrams and provide an alternate synthesis concept, pressure-potential ([Formula: see text]), to access phases having high hydrogen content. Palladium-hydrogen is a widely studied material system with the highest hydride phase being Pd3H4 Most strikingly for this system, at potentials above hydrogen evolution and ∼ 300 MPa pressure, we find the possibility to make palladium superhydrides (e.g., PdH10). We predict the generalizability of this approach for La-H, Y-H, and Mg-H with 10- to 100-fold reduction in required pressure for stabilizing phases. In addition, the [Formula: see text] strategy allows stabilizing additional phases that cannot be done purely by either pressure or potential and is a general approach that is likely to work for synthesizing other hydrides at modest pressures.

Direct correlation between void formation and lithium dendrite growth in solid-state electrolytes with interlayers.

Solid-state Li-ion batteries with lithium anodes offer higher energy densities and are safer than conventional liquid electrolyte-based Li-ion batteries. However, the growth of lithium dendrites across the solid-state electrolyte layer leads to the premature shorting of cells and limits their practical viability. Here, using solid-state Li half-cells with metallic interlayers between a garnet-based lithium-ion conductor and lithium, we show that interfacial void growth precedes dendrite nucleation and growth. Specifically, void growth was observed at a current density of around two-thirds of the critical current density for dendrite growth. Computational calculations reveal that interlayer materials with higher critical current densities for dendrite growth also have the largest thermodynamic and kinetic barriers for lithium vacancy accumulation at their interfaces with lithium. Our results suggest that interfacial modification with suitable metallic interlayers decreases the tendency for void growth and improves dendrite growth tolerance in solid-state electrolytes, even in the absence of high stack pressures.

Design rules for liquid crystalline electrolytes for enabling dendrite-free lithium metal batteries.

Dendrite-free electrodeposition of lithium metal is necessary for the adoption of high energy-density rechargeable lithium metal batteries. Here, we demonstrate a mechanism of using a liquid crystalline electrolyte to suppress dendrite growth with a lithium metal anode. A nematic liquid crystalline electrolyte modifies the kinetics of electrodeposition by introducing additional overpotential due to its bulk-distortion and anchoring free energy. By extending the phase-field model, we simulate the morphological evolution of the metal anode and explore the role of bulk-distortion and anchoring strengths on the electrodeposition process. We find that adsorption energy of liquid crystalline molecules on a lithium surface can be a good descriptor for the anchoring energy and obtain it using first-principles density functional theory calculations. Unlike other extrinsic mechanisms, we find that liquid crystals with high anchoring strengths can ensure smooth electrodeposition of lithium metal, thus paving the way for practical applications in rechargeable batteries based on metal anodes.

Design principles for self-forming interfaces enabling stable lithium-metal anodes.

The path toward Li-ion batteries with higher energy densities will likely involve use of thin lithium (Li)-metal anode (<50 µm thickness), whose cyclability today remains limited by dendrite formation and low coulombic efficiency (CE). Previous studies have shown that the solid-electrolyte interface (SEI) of the Li metal plays a crucial role in Li-electrodeposition and -stripping behavior. However, design rules for optimal SEIs are not well established. Here, using integrated experimental and modeling studies on a series of structurally similar SEI-modifying model compounds, we reveal the relationship between SEI compositions, Li deposition morphology, and CE and identify two key descriptors for the fraction of ionic compounds and compactness, leading to high-performance SEIs. We further demonstrate one of the longest cycle lives to date (350 cycles for 80% capacity retention) for a high specific-energy Li||LiCoO2 full cell (projected >350 watt hours [Wh]/kg) at practical current densities. Our results provide guidance for rational design of the SEI to further improve Li-metal anodes.

Low-Pressure Electrochemical Synthesis of Complex High-Pressure Superconducting Superhydrides.

There is great current interest in multicomponent superhydrides due to their unique quantum properties under pressure. A remarkable example is the ternary superhydride Li_{2}MgH_{16} computationally identified to have an unprecedented high superconducting critical temperature T_{c} of ∼470 K at 250 GPa. However, the very high synthesis pressures required remains a significant hurdle for detailed study and potential applications. In this Letter, we evaluate the feasibility of synthesizing ternary Li-Mg superhydrides by the recently proposed pressure-potential (P^{2}) method that uniquely combines electrochemistry and applied pressure to control synthesis and stability. The results indicate that it is possible to synthesize Li-Mg superhydrides at modest pressures by applying suitable electrode potentials. Using pressure alone, no Li-Mg ternary hydrides are predicted to be thermodynamically stable, but in the presence of electrode potentials, both Li_{2}MgH_{16} and Li_{4}MgH_{24} can be stabilized at modest pressures. Three polymorphs are predicted as ground states of Li_{2}MgH_{16} below 300 GPa, with transitions at 33 and 160 GPa. The highest pressure phase is superconducting, while the two at lower pressures are not. Our findings point out the potentially important role of the P^{2} method in controlling phase stability of complex multicomponent superhydrides.

Universal chemomechanical design rules for solid-ion conductors to prevent dendrite formation in lithium metal batteries.

Dendrite formation during electrodeposition while charging lithium metal batteries compromises their safety. Although high-shear-modulus (Gs) solid-ion conductors (SICs) have been prioritized to resolve the pressure-driven instabilities that lead to dendrite propagation and cell shorting, it is unclear whether these or alternatives are needed to guide uniform lithium electrodeposition, which is intrinsically density-driven. Here, we show that SICs can be designed within a universal chemomechanical paradigm to access either pressure-driven dendrite-blocking or density-driven dendrite-suppressing properties, but not both. This dichotomy reflects the competing influence of the SIC's mechanical properties and the partial molar volume of Li+ ([Formula: see text]) relative to those of the lithium anode (GLi and VLi) on plating outcomes. Within this paradigm, we explore SICs in a previously unrecognized dendrite-suppressing regime that are concomitantly 'soft', as is typical of polymer electrolytes, but feature an atypically low [Formula: see text] that is more reminiscent of 'hard' ceramics. Li plating (1 mA cm-2; T = 20 °C) mediated by these SICs is uniform, as revealed using synchrotron hard X-ray microtomography. As a result, cell cycle life is extended, even when assembled with thin Li anodes (~30 µm) and either high-voltage NMC-622 cathodes (1.44 mAh cm-2) or high-capacity sulfur cathodes (3.02 mAh cm-2).

Not All Fluorination Is the Same: Unique Effects of Fluorine Functionalization of Ethylene Carbonate for Tuning Solid-Electrolyte Interphase in Li Metal Batteries.

Li metal batteries (LMBs) are crucial for electrifying transportation and aviation. Engineering electrolytes to form desired solid-electrolyte interphase (SEI) is one of the most promising approaches to enable stable long-lasting LMBs. Among the liquid electrolytes explored, fluoroethylene carbonate (FEC) has seen great success in leading to desirable SEI properties for enabling stable cycling of LMBs. Given the many facets to desirable SEI properties, numerous descriptors and mechanisms have been proposed. To build a detailed mechanistic understanding, we analyze varying degrees of fluorination of the same prototype molecule, chosen to be ethylene carbonate (EC) to tease out the interfacial reactivity at the Li metal/electrolyte. Using density functional theory (DFT) calculations, we study the effect of mono-, di-, tri-, and tetra-fluorine substitutions of EC on its reactivity with Li surface facets in the presence and absence of Li salt. We find that the formation of LiF at the early stage of SEI formation, posited as a desirable SEI component, depends on the F-abstraction mechanism rather than the number of fluorine substitution. The best illustrations of this are cis- and trans-difluoro ECs, where F-abstraction is spontaneous with the trans case, while the cis case needs to overcome a nonzero energy barrier. Using a Pearson correlation map, we find that the extent of initial chemical decomposition quantified by the associated reaction free energy is linearly correlated with the charge transferred from the Li surface and the number of covalent-like bonds formed at the surface. The effect of salt and the surface facet have a much weaker role in determining the decompositions at the immediate electrolyte/electrode interfaces. Putting all of this together, we find that tetra-FEC could act as a high-performing SEI modifier as it leads to a more homogeneous, denser LiF-containing SEI. Using this methodology, future investigations will explore -CF3 functionalization and other backbone molecules (linear carbonates).

Marcus-Hush-Chidsey kinetics at electrode-electrolyte interfaces.

Electrochemical kinetics at electrode-electrolyte interfaces limit the performance of devices including fuel cells and batteries. While the importance of moving beyond Butler-Volmer kinetics and incorporating the effect of electronic density of states of the electrode has been recognized, a unified framework that incorporates these aspects directly into electrochemical performance models is still lacking. In this work, we explicitly account for the density functional theory-calculated density of states numerically in calculating electrochemical reaction rates for a variety of electrode-electrolyte interfaces. We first show the utility of this for two cases related to Li metal electrodeposition and stripping on a Li surface and a Cu surface (anode-free configuration). The deviation in reaction rates is minor for cases with flat densities of states such as Li, but is significant for Cu due to nondispersive d-bands creating large variation. Finally, we consider a semiconducting case of a solid-electrolyte interphase consisting of LiF and Li2CO3 and note the importance of the Fermi level at the interface pinned by the redox reaction occurring there. We identify the asymmetry in reaction rates as a function of discharge/charge naturally within this approach.

An accurate machine-learning calculator for optimization of Li-ion battery cathodes.

There is significant interest in improving the performance of batteries to increase electrification of transportation and aviation. Recently, performance improvements have been in large part due to changes in the composition of the cathode material family, LiNixMnyCo(1-x-y)O2 (e.g., 111-622-811). Despite the importance of these materials and tremendous progress with density functional theory (DFT) calculations in understanding basic design principles, it is computationally prohibitively expensive to make this problem tractable. Specifically, predicting the open circuit voltage for any cathode material in this family requires evaluation of stability in a quaternary phase space. In this work, we develop machine-learning potentials using fingerprinting based on atom-centered symmetry functions, used with a neural network model, trained on DFT calculations with a prediction accuracy of 3.7 meV/atom and 0.13 eV/Å for energy and force, respectively. We perform hyperparameter optimization of the fingerprinting parameters using Bayesian optimization through the Dragonfly package. Using this ML calculator, we first test its performance in predicting thermodynamic properties within the Debye-Grüneisen model and find good agreement for most thermodynamic properties, including the Gibbs free energy and entropy. Then, we use this to calculate the Li-vacancy ordering as a function of Li composition to simulate the process of discharging/charging of the cathode using grand canonical Monte Carlo simulations. The predicted voltage profiles are in good agreement with the experimental ones and provide an approach to rapidly perform design optimization in this phase space. This study serves as a proof-point of machine-learned DFT surrogates to enable battery materials optimization.

Kinetics of lithium electrodeposition and stripping.

Electrodeposition and stripping are fundamental electrochemical processes for metals and have gained importance in rechargeable Li-ion batteries due to lithium metal electrodes. The electrode kinetics associated with lithium metal electrodeposition and stripping is crucial in determining the performance at fast discharge and charge, which is important for electric vertical takeoff and landing (eVTOL) aircraft and electric vehicles (EV). In this work, we show the use of Marcus-Hush-Chidsey (MHC) kinetics to accurately predict the Tafel curve data from the work of Boyle et al. [ACS Energy Lett. 5(3), 701 (2020)]. We discuss the differences in predictions of reorganization energies from the Marcus-Hush and the MHC models for lithium metal electrodes in four solvents. The MHC kinetic model is implemented and open-sourced within Cantera. Using the reaction kinetic model in a pseudo-2D battery model with a lithium anode paired with a LiFePO4 cathode, we show the importance of accounting for the MHC kinetics and compare it to the use of Butler-Volmer and Marcus-Hush kinetic models. We find significant deviation in the limiting currents associated with reaction kinetics for the three different rate laws for conditions of fast charge and discharge relevant for eVTOL and EV, respectively.

Enhanced strength and temperature dependence of mechanical properties of Li at small scales and its implications for Li metal anodes.

Most next-generation Li ion battery chemistries require a functioning lithium metal (Li) anode. However, its application in secondary batteries has been inhibited because of uncontrollable dendrite growth during cycling. Mechanical suppression of dendrite growth through solid polymer electrolytes (SPEs) or through robust separators has shown the most potential for alleviating this problem. Studies of the mechanical behavior of Li at any length scale and temperature are limited because of its extreme reactivity, which renders sample preparation, transfer, microstructure characterization, and mechanical testing extremely challenging. We conduct nanomechanical experiments in an in situ scanning electron microscope and show that micrometer-sized Li attains extremely high strengths of 105 MPa at room temperature and of 35 MPa at 90 °C. We demonstrate that single-crystalline Li exhibits a power-law size effect at the micrometer and submicrometer length scales, with the strengthening exponent of -0.68 at room temperature and of -1.00 at 90 °C. We also report the elastic and shear moduli as a function of crystallographic orientation gleaned from experiments and first-principles calculations, which show a high level of anisotropy up to the melting point, where the elastic and shear moduli vary by a factor of ∼4 between the stiffest and most compliant orientations. The emergence of such high strengths in small-scale Li and sensitivity of this metal's stiffness to crystallographic orientation help explain why the existing methods of dendrite suppression have been mainly unsuccessful and have significant implications for practical design of future-generation batteries.

Electron Transport in Multidimensional Fuzzy Graphene Nanostructures.

Atomically thin two-dimensional (2D) materials offer a range of superlative electronic and electrochemical properties that facilitate applications in sensing, energy conversion, and storage. Graphene, a 2D allotrope of carbon, has exceptional surface area per unit mass and highly catalytic edges. To leverage these properties, efforts have been made to synthesize complex three-dimensional (3D) geometries of graphene, with an eye toward integration into functional electronic devices. However, the electronic transport properties of such complex 3D structures are not well understood at a microscopic level. Here, we report electron transport in a 3D arrangement of free-standing 2D graphene flakes along an isolated one-dimensional Si nanowire. We show that transport through the free-standing graphene network is dominated by variable-range hopping and leads to negative magnetoresistance, from cryogenic conditions up to room temperature. Our findings lay the foundation for studying transport mechanisms in 2D material-based multidimensional nanostructures.

Uncertainty quantification of DFT-predicted finite temperature thermodynamic properties within the Debye model.

Density functional theory (DFT) calculations are routinely used to screen for functional materials for a variety of applications. This screening is often carried out with a few descriptors, which use ground-state properties that typically ignore finite temperature effects. Finite-temperature effects can be included by calculating the vibration properties, and this can greatly improve the fidelity of computational screening. An important challenge for DFT-based screening is the sensitivity of the predictions to the choice of the exchange correlation function. In this work, we rigorously explore the sensitivity of finite temperature thermodynamic properties to the choice of the exchange correlation functional using the built-in error estimation capabilities within the Bayesian Error Estimation Functional-van der Waals (BEEF-vdW). The vibrational properties are estimated using the Debye model, and we quantify the uncertainty associated with finite-temperature properties for a diverse collection of materials. We find good agreement with experiment and small spread in predictions over different exchange correlation functionals for Mg, Al2O3, Al, Ca, and GaAs. In the case of Li, Li2O, and NiO, however, we find a large spread in predictions as well as disagreement between experiment and functionals due to complex bonding environments. While the energetics generated by the BEEF-vdW ensemble is typically normal, the complex mapping through the Debye model leads to the derived finite temperature properties having non-Gaussian behavior. We test a wide variety of probability distributions that best represent the finite temperature distribution and find that properties such as specific heat, Gibbs free energy, entropy, and thermal expansion coefficient are well described by normal or transformed normal distributions, while the prediction spread of volume at a given temperature does not appear to be drawn from a single distribution. Given the computational efficiency of the approach, we believe that uncertainty quantification should be routinely incorporated into finite-temperature predictions. In order to facilitate this, we have open-sourced the code base under the name dePye.

Quantifying robustness of DFT predicted pathways and activity determining elementary steps for electrochemical reactions.

Density functional theory calculations are being routinely used to screen for new catalysts. Typically, this involves invoking scaling relations leading to the Sabatier-type volcano relationship for the catalytic activity, where each leg represents a unique potential determining an elementary step. The success of such screening efforts relies heavily not only on the prediction robustness of the activity determining step, but also on the choice of the descriptor. This becomes even more important as these methods are being applied to determine selectivity between a variety of possible reaction products. In this work, we develop a framework to quantify the confidence in the classification problem of identifying the potential determining step for material candidates and subsequently the pathway selectivity toward different reaction products. We define a quantity termed as the classification efficiency, which is a quantitative metric to rank descriptors on the basis of robustness of predictions for identifying selectivity toward different reaction products and the limiting step for the corresponding pathway. We demonstrate this approach for the reactions of oxygen reduction and oxygen evolution, and identify that ΔGOOH* is the optimal descriptor to classify between 2e- and 4e- oxygen reduction. We further show that ΔGOH* and ΔGOOH* have comparable performance in identifying the limiting step for 4e- oxygen reduction reaction. In the case of oxygen evolution, we study all possible 2 descriptor models and identify that {ΔGOOH*,ΔGO* } and {ΔGOH* ,ΔGO* } both are highly efficient at classifying between 2e- and 4e- water oxidation. The presented methodology can directly be applied to other multi-electron electrochemical reactions such as CO2 and N2 reduction for improved mechanistic insights.

Quantifying Confidence in DFT-Predicted Surface Pourbaix Diagrams of Transition-Metal Electrode-Electrolyte Interfaces.

Density functional theory (DFT) calculations have been widely used to predict the activity of catalysts based on the free energies of reaction intermediates. The incorporation of the state of the catalyst surface under the electrochemical operating conditions while constructing the free-energy diagram is crucial, without which even trends in activity predictions could be imprecisely captured. Surface Pourbaix diagrams indicate the surface state as a function of the pH and the potential. In this work, we utilize error-estimation capabilities within the Bayesian ensemble error functional with van der Waals correlations exchange correlation functional as an ensemble approach to propagate the uncertainty associated with the adsorption energetics in the construction of Pourbaix diagrams. Within this approach, surface-transition phase boundaries are no longer sharp and are therefore associated with a finite width. We determine the surface phase diagram for several transition metals under reaction conditions and electrode potentials relevant for the oxygen reduction reaction. We observe that our surface phase predictions for most predominant species are in good agreement with cyclic voltammetry experiments and prior DFT studies. We use the OH* intermediate for comparing adsorption characteristics on Pt(111), Pt(100), Pd(111), Ir(111), Rh(111), and Ru(0001) since it has been shown to have a higher prediction efficiency relative to O*, and find the trend Ru > Rh > Ir > Pt > Pd for (111) metal facets, where Ru binds OH* the strongest. We robustly predict the likely surface phase as a function of reaction conditions by associating confidence values for quantifying the confidence in predictions within the Pourbaix diagram. We define a confidence quantifying metric, using which certain experimentally observed surface phases and peak assignments can be better rationalized. The probabilistic approach enables a more accurate determination of the surface structure and can readily be incorporated in computational studies for better understanding the catalyst surface under operating conditions.