An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, we report a new method for predicting synthesizability using a simple yet accurate thermochemical descriptor. We introduce Emin, the energy difference between a molecule and its lowest energy constitutional isomer, as a synthesizability predictor that is accurate, physically meaningful, and first-principles based. We apply Emin to 134,000 molecules in the QM9 data set and find that Emin is accurate when used alone and reduces incorrect predictions of "synthesizable" by up to 52% when used to augment commonly used prediction methods. Our work illustrates how first-principles thermochemistry and heuristic approximations for molecular stability are complementary, opening a new direction for synthesizability prediction methods.

Mixed-Anion Contact Ion-Pair Formation Enabling Improved Performance of Halide-Free Mg-Ion Electrolytes

Discovery of stable and efficient electrolytes that are compatible with magnesium metal anodes and high-voltage cathodes is crucial to enabling energy storage technologies that can move beyond existing Li-ion systems. Many promising electrolytes for magnesium anodes have been proposed with chloride-based systems at the forefront; however, Cl-containing electrolytes lack the oxidative stability required by high-voltage cathodes. In this work, we report magnesium trifluoromethanesulfonate (triflate) as a viable coanion for Cl-free, mixed-anion magnesium electrolytes. The addition of triflate to electrolytes containing bis(trifluoromethane sulfonyl) imide (TFSI-) anions yields significantly improved Coulombic efficiency, up to a 100 mV decrease in the plating/stripping overpotential, improved tolerance to trace H2O, and improved oxidative stability (0.35 V improvement compared to that of hybrid TFSI-Cl electrolytes). Based on 19F nuclear magnetic resonance and Raman spectroscopy measurements, we propose that these improvements in performance are driven by the formation of mixed-anion contact ion pairs, where both triflate and TFSI- are coordinated to Mg2+ in the electrolyte bulk. The formation of this mixed-anion magnesium complex is further predicted by the density functional theory to be thermodynamically driven. Collectively, this work outlines the guiding principles for the improved design of next-generation electrolytes for magnesium batteries.

Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries

Organic nonaqueous redox flow batteries (O-NRFBs) are promising energy storage devices due to their scalability and reliance on sourceable materials. However, finding suitable redox-active organic molecules (redoxmers) for these batteries remains a challenge. Using plant-based compounds as precursors for these redoxmers can decrease their costs and environmental toxicity. In this computational study, flavonoid molecules have been examined as potential redoxmers for O-NRFBs. Flavone and isoflavone derivatives were selected as catholyte (positive charge carrier) and anolyte (negative charge carrier) molecules, respectively. To drive their redox potentials to the opposite extremes, in silico derivatization was performed using a novel algorithm to generate a library of > 40000 candidate molecules that penalizes overly complex structures. A multiobjective Bayesian optimization based active learning algorithm was then used to identify best redoxmer candidates in these search spaces. Our study provides methodologies for molecular design and optimization of natural scaffolds and highlights the need of incorporating expert chemistry awareness of the natural products and the basic rules of synthetic chemistry in machine learning.

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528-4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. The quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6-31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6-31+G(2df,p) and ωB97XD/6-311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantum chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening.

Coordination-Dependent Chemical Reactivity of TFSI Anions at a Mg Metal Interface

Charge transfer across the electrode-electrolyte interface is a highly complex and convoluted process involving diverse solvated species with varying structures and compositions. Despite recent advances in in situ and operando interfacial analysis, molecular specific reactivity of solvated species is inaccessible due to a lack of precise control over the interfacial constituents and/or an unclear understanding of their spectroscopic fingerprints. However, such molecular-specific understanding is critical to the rational design of energy-efficient solid-electrolyte interphase layers. We have employed ion soft landing, a versatile and highly controlled method, to prepare well-defined interfaces assembled with selected ions, either as solvated species or as bare ions, with distinguishing molecular precision. Equipped with precise control over interfacial composition, we employed in situ multimodal spectroscopic characterization to unravel the molecular specific reactivity of Mg solvated species comprising (i.e., bis(trifluoromethanesulfonyl)imide, TFSI^-) anions and solvent molecules (i.e., dimethoxyethane, DME/G1) on a Mg metal surface relevant to multivalent Mg batteries. In situ multimodal spectroscopic characterization revealed higher reactivity of the undercoordinated solvated species [Mg-TFSI-G1]⁺ compared to the fully coordinated [Mg-TFSI-(G1)₂]⁺ species or even the bare TFSI^-. These results were corroborated by the computed reaction pathways and energy barriers for decomposition of the TFSI^- within Mg solvated species relative to bare TFSI^-. Finally, we evaluated the TFSI reactivity under electrochemical conditions using Mg(TFSI)₂-DME-based phase-separated electrolytes representing different solvated constituents. Based on our multimodal study, we report a detailed understanding of TFSI^- decomposition processes as part of coordinated solvated species at a Mg-metal anode that will aid the rational design of improved sustainable electrochemical energy technologies.

An SECM-Based Spot Analysis for Redoxmer-Electrode Kinetics: Identifying Redox Asymmetries on Model Graphitic Carbon Interfaces

The fundamental process in non-aqueous redox flow battery (NRFB) operation revolves around electron transfer (ET) between a current collector electrode and redox-active organic molecules (redoxmers) in solution. Here, we present an approach utilizing scanning electrochemical microscopy (SECM) to evaluate interfacial ET kinetics between redoxmers and various electrode materials of interest at desired locations. This spot-analysis method relies on the measurement of heterogeneous electron transfer rate constants (k_f or k_b ) as a function of applied potential (E-E⁰ '). As demonstrated by COMSOL simulations, this method enables the quantification of Butler-Volmer kinetic parameters, the standard heterogeneous rate constant, k⁰ , and the transfer coefficient, α. Our method enabled the identification of inherent asymmetries in the ET kinetics arising during the reduction of ferrocene-based redoxmers, compared to their oxidation which displayed faster rate constants. Similar behavior was observed on a wide variety of carbon electrodes such as multi-layer graphene, highly ordered pyrolytic graphite, glassy carbon, and chemical vapor deposition-grown graphite films. However, aqueous systems and Pt do not exhibit such kinetic effects. Our analysis suggests that differential adsorption of the redoxmers is insufficient to account for our observations. Displaying a greater versatility than conventional electroanalytical methods, we demonstrate the operation of our spot analysis at concentrations up to 100 mM of redoxmer over graphite films. Looking forward, our method can be used to assess non-idealities in a variety of redoxmer/electrode/solvent systems with quantitative evaluation of kinetics for applications in redox-flow battery research.

A KMnO4-Generated Colloidal Electrolyte for Redox Mediation and Anode Protection in a Li-Air Battery

The rechargeable lithium-oxygen (Li-O₂) battery has the highest theoretical specific energy density of any rechargeable batteries and could transform energy storage systems if a practical device could be attained. However, among numerous challenges, which are all interconnected, are polarization due to sluggish kinetics, low cycle life, small capacity, and slow rates. In this study, we report on use of KMnO₄ to generate a colloidal electrolyte made up of MnO₂ nanoparticles. The resulting electrolyte provides a redox mediator for reducing the charge potential and lithium anode protection to increase cycle life. This electrolyte in combination with a stable binary transition metal dichalcogenide alloy, Nb_0.5Ta_0.5S₂, as the cathode enables the operation of a Li-O₂ battery at a current density of 1 mA·cm^-2 and specific capacity ranging from 1000 to 10 000 mA·h·g^-1 (corresponding to 0.1-1 mA·h·cm^-2) in a dry air environment with a cycle life of up to 150. This colloidal electrolyte provides a robust approach for advancing Li-air batteries.

Improving the Accuracy of Composite Methods: A G4MP2 Method with G4-like Accuracy and Implications for Machine Learning

G4MP2 theory has proven to be a reliable and accurate quantum chemical composite method for the calculation of molecular energies using an approximation based on second-order perturbation theory to lower computational costs compared to G4 theory. However, it has been found to have significantly increased errors when applied to larger organic molecules with 10 or more nonhydrogen atoms. We report here on an investigation of the cause of the failure of G4MP2 theory for such larger molecules. One source of error is found to be the "higher-level correction (HLC)", which is meant to correct for deficiencies in correlation contributions to the calculated energies. This is because the HLC assumes that the contribution is independent of the element and the type of bonding involved, both of which become more important with larger molecules. We address this problem by adding an atom-specific correction, dependent on atom type but not bond type, to the higher-level correction. We find that a G4MP2 method that incorporates this modification of the higher-level correction, referred to as G4MP2A, becomes as accurate as G4 theory (for computing enthalpies of formation) for a test set of molecules with less than 10 nonhydrogen atoms as well as a set with 10-14 such atoms, the set of molecules considered here, with a much lower computational cost. The G4MP2A method is also found to significantly improve ionization potentials and electron affinities. Finally, we implemented the G4MP2A energies in a machine learning method to predict molecular energies.

Fluorination Enables Simultaneous Improvements of a Dialkoxybenzene-Based Redoxmer for Nonaqueous Redox Flow Batteries

Redoxmers or redox-active organic materials, are one critical component for nonaqueous redox flow batteries (RFBs), which hold high promise in enabling the time domain of the grid. While tuning redox potentials of redoxmers is a very effective way to enhance energy densities of NRFBs, those improvements often accompany accelerated kinetics of the charged species, undermining stability and cycling performance. Herein, a strategy for designing redoxmers with simultaneous improvements in redox potential and stability is proposed. Specifically, the redoxmer 1,4-di-tert-butyl-2,5-bis(2,2,2-trifluoroethoxy)benzene (ANL-C46) is developed by incorporating fluorinated substitutions into the dialkoxybenzene-based platform. Compared to the non-fluorinated analogue, ANL-C46 demonstrates not only an increased (∼0.41 V) redox potential but also much enhanced stability (1.6 times) and cyclability (4 times) evidenced by electron paramagnetic resonance kinetic study, H-cell and flow cell cycling. In fact, the cycling performance of ANL-C46 is among the best of high potential (>1.0 V vs Ag/Ag⁺) redoxmers ever reported. Density functional theory calculations suggest that while the introduced fluorine substitutions elevate the redox potentials, they also help to depress the decomposition reactions of the charged redoxmers, affording excellent stability. The findings represent an interesting strategy for simultaneously improving energy density and stability, which could further prompt the development of high-performance redoxmers.

Interactions of CO2 Anion Radicals with Electrolyte Environments from First-Principles Simulations

Successful transformation of carbon dioxide (CO₂) into value-added products is of great interest, as it contributes in part to the circular carbon economy. Understanding chemical interactions that stabilize crucial reaction intermediates of CO₂ is important, and in this contribution, we employ atom centered density matrix propagation (ADMP) molecular dynamics simulations to investigate interactions between CO₂ ^- anion radicals with surrounding solvent molecules and electrolyte cations in both aqueous and nonaqueous environments. We show how different cations and solvents affect the stability of the CO₂ ^- anion radical by examining its angle and distance to a coordinating cation in molecular dynamics simulations. We identify that the strength of CO₂ ^- interactions can be tailored through choosing an appropriate cation and solvent combination. We anticipate that this fundamental understanding of cation/solvent interactions can facilitate the optimization of a chemical pathway that results from selective stabilization of a crucial reaction intermediate.

Insights into Spontaneous Solid Electrolyte Interphase Formation at Magnesium Metal Anode Surface from Ab Initio Molecular Dynamics Simulations

Spontaneous chemical reactivity at multivalent (Mg, Ca, Zn, Al) electrode surfaces is critical to solid electrolyte interphase (SEI) formation, and hence, directly affects the longevity of batteries. Here, we report an investigation of the reactivity of 0.5 M Mg(TFSI)₂ in 1,2-dimethoxyethane (DME) solvent at a Mg(0001) surface using ab initio molecular dynamics (AIMD) simulations and detailed Bader charge analysis. Based on the simulations, the initial degradation reactions of the electrolyte strongly depend on the structure of the Mg(TFSI)₂ species near the anode surface. At the surface, the dissociation of Mg(TFSI)₂ species occurs via cleavage of the N-S bond for the solvent separated ion pair (SSIP) and via cleavage of the C-S bond for the contact ion pair (CIP) configuration. In the case of the CIP, both TFSI anions undergo spontaneous bond dissociation reactions to form atomic O, C, S, F, and N species adsorbed on the surface of the Mg anode. These products indicate that the initial SEI layer formed on the surface of the pristine Mg anode consists of a complex mixture of multiple components such as oxides, carbides, sulfides, fluorides, and nitrides. We believe that the atomic-level insights gained from these simulations will lay the groundwork for the rational design of tailored and functional interphases that are critical for the success of multivalent battery technology.

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models

The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications.

Competitive Pi-Stacking and H-Bond Piling Increase Solubility of Heterocyclic Redoxmers

Redoxmers are organic molecules that carry electric charge in flow batteries. In many instances, they consist of heteroaromatic moieties modified with appended groups to prevent stacking of the planar cores and increase solubility in liquid electrolytes. This higher solubility is desired as it potentially allows achieving greater energy density in the battery. However, the present synthetic strategies often yield bulky molecules with low molarity even when they are neat and still lower molarity in liquid solutions. Fortunately, there are exceptions to this rule. Here, we examine one well-studied redoxmer, 2,1,3-benzothiadiazole, which has solubility ∼5.7 M in acetonitrile at 25 °C. We show computationally and prove experimentally that the competition between two packing motifs, face-to-face π-stacking and random N-H bond piling, introduces frustration that confounds nucleation in crowded solutions. Our findings and examples from related systems suggest a complementary strategy for the molecular design of redoxmers for high energy density redox flow cells.

Self-Assembled Solute Networks in Crowded Electrolyte Solutions and Nanoconfinement of Charged Redoxmer Molecules

Redoxmers are electrochemically active organic molecules storing charge and energy in electrolyte fluids circulating through redox flow batteries (RFBs). Such molecules typically have solvent-repelling cores and solvent-attracting pendant groups introduced to increase solubility in liquid electrolytes. These two features can facilitate nanoscale aggregation of the redoxmer molecules in crowded solutions. In some cases, this aggregation leads to the emergence of continuous networks of solute molecules in contact, and the solution becomes microscopically heterogeneous. Here, we use small-angle X-ray scattering (SAXS) and molecular dynamics modeling to demonstrate formation of such networks and examine structural factors controlling this self-assembly. We also show that salt ions become excluded from these solute aggregates into small pockets of electrolytes, where these ions strongly associate. This confinement by exclusion is also likely to occur to charged redoxmer molecules in a "sea" of neutral precursors coexisting in the same solution. Here, we demonstrate that the decay lifetime of the confined charged molecules in such solutions can increase several fold compared to dilute solutions. We attribute this behavior to a "microreactor effect" on reverse reactions of the confined species during their decomposition.

Anion Association Strength as a Unifying Descriptor for the Reversibility of Divalent Metal Deposition in Nonaqueous Electrolytes

Developing next-generation battery chemistries that move beyond traditional Li-ion systems is critical to enabling transformative advances in electrified transportation and grid-level energy storage. In this work, we provide the first evidence for common descriptors for improved reversibility of metal plating/stripping in nonaqueous electrolytes for multivalent ion batteries. Focusing first on the specific role of chloride (Cl^-) in promoting electrochemical reversibility in multivalent systems, rotating disk (RDE) and ring-disk electrode (RRDE) investigations were performed utilizing a variety of divalent cations (Mg²⁺, Zn²⁺, and Cu²⁺) and the bis-(trifluoromethane sulfonyl) imide (TFSI^-) anion. By introducing varying concentrations of Cl^-, a cooperative effect is observed between TFSI^- and Cl^- that yields the more reversible behavior of mixed electrolytes relative to electrolytes containing only TFSI^-. This effect is shown to be general for Mg, Zn, and Cu electrodeposition, and mechanistic understanding of the role of Cl^- in improving reversibility of TFSI-based electrolytes is obtained through the combination of R(R)DE experimental results and density functional theory (DFT) evaluation of the redox activity and thermodynamic stability of various TFSI- and Cl-based solution complexes of metal ions. The cooperative anion effect is further generalized to other mixed-anion systems, where systematic variations in anion association strength predicted from DFT (i.e., Cl^- > OTf^- ≈ TFSI^- > BF₄^- > PF₆^-) yield corresponding trends in redox potentials and improvements of ≥200 mV in the reversibility of metal deposition/dissolution. These results identify anion association strength as a common descriptor for the reversibility of divalent metal anodes and suggest a set of general design principles for developing new electrolytes with improved activity and stability.

Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms

High-fidelity quantum-chemical calculations can provide accurate predictions of molecular energies, but their high computational costs limit their utility, especially for larger molecules. We have shown in previous work that machine learning models trained on high-level quantum-chemical calculations (G4MP2) for organic molecules with one to nine non-hydrogen atoms can provide accurate predictions for other molecules of comparable size at much lower costs. Here we demonstrate that such models can also be used to effectively predict energies of molecules larger than those in the training set. To implement this strategy, we first established a set of 191 molecules with 10-14 non-hydrogen atoms having reliable experimental enthalpies of formation. We then assessed the accuracy of computed G4MP2 enthalpies of formation for these 191 molecules. The error in the G4MP2 results was somewhat larger than that for smaller molecules, and the reason for this increase is discussed. Two density functional methods, B3LYP and ωB97X-D, were also used on this set of molecules, with ωB97X-D found to perform better than B3LYP at predicting energies. The G4MP2 energies for the 191 molecules were then predicted using these two functionals with two machine learning methods, the FCHL-Δ and SchNet-Δ models, with the learning done on calculated energies of the one to nine non-hydrogen atom molecules. The better-performing model, FCHL-Δ, gave atomization energies of the 191 organic molecules with 10-14 non-hydrogen atoms within 0.4 kcal/mol of their G4MP2 energies. Thus, this work demonstrates that quantum-chemically informed machine learning can be used to successfully predict the energies of large organic molecules whose size is beyond that in the training set.

A Long-Cycle-Life Lithium-CO2 Battery with Carbon Neutrality

Lithium-CO₂ batteries are attractive energy-storage systems for fulfilling the demand of future large-scale applications such as electric vehicles due to their high specific energy density. However, a major challenge with Li-CO₂ batteries is to attain reversible formation and decomposition of the Li₂ CO₃ and carbon discharge products. A fully reversible Li-CO₂ battery is developed with overall carbon neutrality using MoS₂ nanoflakes as a cathode catalyst combined with an ionic liquid/dimethyl sulfoxide electrolyte. This combination of materials produces a multicomponent composite (Li₂ CO₃ /C) product. The battery shows a superior long cycle life of 500 for a fixed 500 mAh g^-1 capacity per cycle, far exceeding the best cycling stability reported in Li-CO₂ batteries. The long cycle life demonstrates that chemical transformations, making and breaking covalent CO bonds can be used in energy-storage systems. Theoretical calculations are used to deduce a mechanism for the reversible discharge/charge processes and explain how the carbon interface with Li₂ CO₃ provides the electronic conduction needed for the oxidation of Li₂ CO₃ and carbon to generate the CO₂ on charge. This achievement paves the way for the use of CO₂ in advanced energy-storage systems.

Accurate quantum chemical energies for 133 000 organic molecules

The energies of the 133 000 molecules in the GDB-9 database have been calculated at the G4MP2 level of theory and then were used to calculate their enthalpies of formation.

The energies of the 133 000 molecules in the GDB-9 database have been calculated at the G4MP2 level of theory and then were used to calculate their enthalpies of formation. This database contains organic molecules having nine or less atoms of carbon, nitrogen, oxygen, and fluorine, as well as hydrogen atoms. The accuracy of the G4MP2 energies was investigated on a subset of 459 of the molecules having experimental enthalpies of formation with small uncertainties. On this subset the G4MP2 enthalpies of formation have an accuracy of 0.79 kcal mol^–1, which is similar to its accuracy previously reported for the smaller G3/05 test set. An error analysis of the theoretical enthalpies of formation of the 459 molecules is presented in terms of the size and type of the molecules. Three different density functionals (B3LYP, ωB97X-D, M06-2X) were also assessed on 459 molecules of accurate enthalpy data for comparison with the G4MP2 results. The G4MP2 energies for the 133 K molecules provide a database that can be used to calculate accurate reaction energies as well as to assess new or existing experimental enthalpies of formation. Several examples are given of types of reactions that can be predicted using the G4MP2 database of energies. The G4MP2 energies of the GDB-9 molecules will also be useful in future investigations of applications of machine learning to quantum chemical data.

Effect of the Hydrofluoroether Cosolvent Structure in Acetonitrile-Based Solvate Electrolytes on the Li+ Solvation Structure and Li-S Battery Performance

We evaluate hydrofluoroether (HFE) cosolvents with varying degrees of fluorination in the acetonitrile-based solvate electrolyte to determine the effect of the HFE structure on the electrochemical performance of the Li-S battery. Solvates or sparingly solvating electrolytes are an interesting electrolyte choice for the Li-S battery due to their low polysulfide solubility. The solvate electrolyte with a stoichiometric ratio of LiTFSI salt in acetonitrile, (MeCN)₂-LiTFSI, exhibits limited polysulfide solubility due to the high concentration of LiTFSI. We demonstrate that the addition of highly fluorinated HFEs to the solvate yields better capacity retention compared to that of less fluorinated HFE cosolvents. Raman and NMR spectroscopy coupled with ab initio molecular dynamics simulations show that HFEs exhibiting a higher degree of fluorination coordinate to Li⁺ at the expense of MeCN coordination, resulting in higher free MeCN content in solution. However, the polysulfide solubility remains low, and no crossover of polysulfides from the S cathode to the Li anode is observed.

Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening

Computational screening techniques have been found to be an effective alternative to the trial and error of experimentation for discovery of new materials. With increased interest in development of advanced electrical energy storage systems, it is essential to find new electrolytes that function effectively. This Perspective reviews various methods for screening electrolytes and then describes a hierarchical computational scheme to screen multiple properties of advanced electrical energy storage electrolytes using high-throughput quantum chemical calculations. The approach effectively down-selects a large pool of candidates based on successive property evaluation. As an example, results of screening are presented for redox potentials, solvation energies, and structural changes of ∼1400 organic molecules for nonaqueous redox flow batteries. Importantly, on the basis of high-throughput screening, in silico design of suitable candidate molecules for synthesis and electrochemical testing can be achieved. We anticipate that the computational approach described in this Perspective coupled with experimentation will have a significant role to play in the discovery of materials for future energy needs.

BF3-promoted electrochemical properties of quinoxaline in propylene carbonate

Electrochemical and density functional studies demonstrate that coordination of electrolyte constituents to quinoxalines modulates their electrochemical properties.

Molecular-level insights into the reactivity of siloxane-based electrolytes at a lithium-metal anode

A molecular-level understanding of the reactions that occur at the lithium-metal anode/electrolyte interphase is essential to improve the performance of Li-O(2) batteries. Experimental and computational techniques are applied to explore the reactivity of tri(ethylene glycol)-substituted trimethylsilane (1NM3), a siloxane-based ether electrolyte, at the lithium-metal anode. In situ/ex situ X-ray diffraction and Fourier-transform infrared spectroscopy studies provide evidence of the formation of lithium hydroxide and lithium carbonates at the anode upon gradual degradation of the metallic lithium anode and the solvent molecules in the presence of oxygen. Density functional calculations performed to obtain a mechanistic understanding of the reductive decomposition of 1NM3 indicate that the decomposition does not require any apparent barrier to produce lithium hydroxide and lithium carbonates when the reduced 1NM3 solvent molecules interact with the oxygen crossing over from the cathode. This study indicates that degradation may be more significant in the case of the 1NM3 solvent, compared to linear ethers such as tetraglyme or dioxalone, because of its relatively high electron affinity. Also, both protection of the lithium metal and prevention of oxygen crossover to the anode are essential for minimizing electrolyte and anode decomposition.

Investigation of thermochemistry associated with the carbon-carbon coupling reactions of furan and furfural using ab initio methods

Upgrading furan and small oxygenates obtained from the decomposition of cellulosic materials via formation of carbon-carbon bonds is critical to effective conversion of biomass to liquid transportation fuels. Simulation-driven molecular level understanding of carbon-carbon bond formation is required to design efficient catalysts and processes. Accurate quantum chemical methods are utilized here to predict the reaction energetics for conversion of furan (C4H4O) to C5-C8 ethers and the transformation of furfural (C5H6O2) to C13-C26 alkanes. Furan can be coupled with various C1 to C4 low molecular weight carbohydrates obtained from the pyrolysis via Diels-Alder type reactions in the gas phase to produce C5-C8 cyclic ethers. The computed reaction barriers for these reactions (∼25 kcal/mol) are lower than the cellulose activation or decomposition reactions (∼50 kcal/mol). Cycloaddition of C5-C8 cyclo ethers with furans can also occur in the gas phase, and the computed activation energy is similar to that of the first Diels-Alder reaction. Furfural, obtained from biomass, can be coupled with aldehydes or ketones with α-hydrogen atoms to form longer chain aldol products, and these aldol products can undergo vapor phase hydrocycloaddition (activation barrier of ∼20 kcal/mol) to form the precursors of C26 cyclic hydrocarbons. These thermochemical studies provide the basis for further vapor phase catalytic studies required for upgrading of furans/furfurals to longer chain hydrocarbons.

Reduction potential predictions of some aromatic nitrogen-containing molecules

A descriptive relationship for computing reduction potentials of selected aromatic nitrogen-containing molecules is developed from their computed orbital energies.

Rapid ether and alcohol C-O bond hydrogenolysis catalyzed by tandem high-valent metal triflate + supported Pd catalysts

The thermodynamically leveraged conversion of ethers and alcohols to saturated hydrocarbons is achieved efficiently with low loadings of homogeneous M(OTf)n + heterogeneous Pd tandem catalysts (M = transition metal; OTf = triflate; n = 4). For example, Hf(OTf)4 mediates rapid endothermic ether ⇌ alcohol and alcohol ⇌ alkene equilibria, while Pd/C catalyzes the subsequent, exothermic alkene hydrogenation. The relative C-O cleavage rates scale as 3° > 2° > 1°. The reaction scope extends to efficient conversion of biomass-derived ethers, such as THF derivatives, to the corresponding alkanes.

Magnetism in lithium-oxygen discharge product

Nonaqueous lithium-oxygen batteries have a much superior theoretical gravimetric energy density compared to conventional lithium-ion batteries, and thus could render long-range electric vehicles a reality. A molecular-level understanding of the reversible formation of lithium peroxide in these batteries, the properties of major/minor discharge products, and the stability of the nonaqueous electrolytes is required to achieve successful lithium-oxygen batteries. We demonstrate that the major discharge product formed in the lithium-oxygen cell, lithium peroxide, exhibits a magnetic moment. These results are based on dc-magnetization measurements and a lithium-oxygen cell containing an ether-based electrolyte. The results are unexpected because bulk lithium peroxide has a significant band gap. Density functional calculations predict that superoxide-type surface oxygen groups with unpaired electrons exist on stoichiometric lithium peroxide crystalline surfaces and on nanoparticle surfaces; these computational results are consistent with the magnetic measurement of the discharged lithium peroxide product as well as EPR measurements on commercial lithium peroxide. The presence of superoxide-type surface oxygen groups with spin can play a role in the reversible formation and decomposition of lithium peroxide as well as the reversible formation and decomposition of electrolyte molecules.

Acid-Catalyzed Conversion of Furfuryl Alcohol to Ethyl Levulinate in Liquid Ethanol

Reaction pathways for the acid-catalyzed conversion of furfuryl alcohol (FAL) to ethyl levulinate (EL) in ethanol were investigated using liquid chromatography-mass spectrometry (LC-MS), 1D and 2D nuclear magnetic resonance (NMR) spectroscopy, and ab initio high-level quantum chemical (G4MP2) calculations. Our combined studies show that the production of EL at high yields from FAL is not accompanied by stoichiometric production of diethyl either (DEE), indicating that ethoxymethyl furan (EMF) is not an intermediate in the major reaction pathway. Several intermediates were observed using an LC-MS system, and three of these intermediates were isolated and subjected to reaction conditions. The structures of two intermediates were elucidated using 1D and 2D NMR techniques. One of these intermediates is EMF, which forms EL and DEE in a secondary reaction pathway. The second intermediate identified is 4,5,5-triethoxypentan-2-one, which is analogous to one of the intermediates observed in the conversion of FAL to LA in water (i.e. 4,5,5-trihydroxypentan-2-one). Furthermore, conversion of this intermediate to EL again involves the formation of DEE, indicating that it is also part of a secondary pathway. The primary pathway for production of EL involves solvent-assisted transfer of a water molecule from the partially detached protonated hydroxyl group of FAL to a ring carbon, followed by intra-molecular hydrogen shift, where the apparent reaction barrier for the hydrogen shift is relatively smaller in ethanol (21.1 kcal/mol) than that in water (26.6 kcal/mol).

Metalloenzyme-like catalyzed isomerizations of sugars by Lewis acid zeolites

Isomerization of sugars is used in a variety of industrially relevant processes and in glycolysis. Here, we show that hydrophobic zeolite beta with framework tin or titanium Lewis acid centers isomerizes sugars, e.g., glucose, via reaction pathways that are analogous to those of metalloenzymes. Specifically, experimental and theoretical investigations reveal that glucose partitions into the zeolite in the pyranose form, ring opens to the acyclic form in the presence of the Lewis acid center, isomerizes into the acyclic form of fructose, and finally ring closes to yield the furanose product. The zeolite catalysts provide processing advantages over metalloenzymes such as an ability to work at higher temperatures and in acidic conditions that allow for the isomerization reaction to be coupled with other important conversions.