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.

A lithium-oxygen battery with a long cycle life in an air-like atmosphere.

Lithium-air batteries are considered to be a potential alternative to lithium-ion batteries for transportation applications, owing to their high theoretical specific energy. So far, however, such systems have been largely restricted to pure oxygen environments (lithium-oxygen batteries) and have a limited cycle life owing to side reactions involving the cathode, anode and electrolyte. In the presence of nitrogen, carbon dioxide and water vapour, these side reactions can become even more complex. Moreover, because of the need to store oxygen, the volumetric energy densities of lithium-oxygen systems may be too small for practical applications. Here we report a system comprising a lithium carbonate-based protected anode, a molybdenum disulfide cathode and an ionic liquid/dimethyl sulfoxide electrolyte that operates as a lithium-air battery in a simulated air atmosphere with a long cycle life of up to 700 cycles. We perform computational studies to provide insight into the operation of the system in this environment. This demonstration of a lithium-oxygen battery with a long cycle life in an air-like atmosphere is an important step towards the development of this field beyond lithium-ion technology, with a possibility to obtain much higher specific energy densities than for conventional lithium-ion batteries.

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.

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.

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.

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.

In Situ Formed Ir3Li Nanoparticles as Active Cathode Material in Li-Oxygen Batteries.

Lithium-oxygen (Li-O2) batteries are a promising class of rechargeable Li batteries with a potentially very high achievable energy density. One of the major challenges for Li-O2 batteries is the high charge overpotential, which results in a low energy efficiency. In this work size-selected subnanometer Ir clusters are used to investigate cathode materials that can help control lithium superoxide formation during discharge, which has good electronic conductivity needed for low charge potentials. It is found that Ir particles can lead to lithium superoxide formation as the discharge product with Ir particle sizes of ∼1.5 nm giving the lowest charge potentials. During discharge these 1.5 nm Ir nanoparticles surprisingly evolve to larger ones while incorporating Li to form core-shell structures with Ir3Li shells, which probably act as templates for growth of lithium superoxide during discharge. Various characterization techniques including DEMS, Raman, titration, and HRTEM are used to characterize the LiO2 discharge product and the evolution of the Ir nanoparticles. Density functional calculations are used to provide insight into the mechanism for formation of the core-shell Ir3Li particles. The in situ formed Ir3Li core-shell nanoparticles discovered here provide a new direction for active cathode materials that can reduce charge overpotentials in Li-O2 batteries.

Evolutionary Design of Low Molecular Weight Organic Anolyte Materials for Applications in Nonaqueous Redox Flow Batteries.

The integration of renewable energy sources into the electric grid requires low-cost energy storage systems that mediate the variable and intermittent flux of energy associated with most renewables. Nonaqueous redox-flow batteries have emerged as a promising technology for grid-scale energy storage applications. Because the cost of the system scales with mass, the electroactive materials must have a low equivalent weight (ideally 150 g/(mol·e(-)) or less), and must function with low molecular weight supporting electrolytes such as LiBF4. However, soluble anolyte materials that undergo reversible redox processes in the presence of Li-ion supports are rare. We report the evolutionary design of a series of pyridine-based anolyte materials that exhibit up to two reversible redox couples at low potentials in the presence of Li-ion supporting electrolytes. A combination of cyclic voltammetry of anolyte candidates and independent synthesis of their corresponding charged-states was performed to rapidly screen for the most promising candidates. Results of this workflow provided evidence for possible decomposition pathways of first-generation materials and guided synthetic modifications to improve the stability of anolyte materials under the targeted conditions. This iterative process led to the identification of a promising anolyte material, N-methyl 4-acetylpyridinium tetrafluoroborate. This compound is soluble in nonaqueous solvents, is prepared in a single synthetic step, has a low equivalent weight of 111 g/(mol·e(-)), and undergoes two reversible 1e(-) reductions in the presence of LiBF4 to form reduced products that are stable over days in solution.

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.

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.

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 the redox chemistry of anthraquinone derivatives using density functional theory.

Application of density functional calculations to compute electrochemical properties such as redox windows, effect of substitution by electron donating and electron withdrawing groups on redox windows, and solvation free energies for ∼50 anthraquinone (AQ) derivatives are presented because of their potential as anolytes in all-organic redox flow batteries. Computations suggest that lithium ions can increase (by ∼0.4 V) the reduction potential of anthraquinone due to the lithium ion pairing by forming a Lewis base-Lewis acid complex. To design new redox active species, the substitution by electron donating groups is essential to improve the reduction window of AQ with adequate oxidative stability. For instance, a complete methylation of AQ can improve its reduction window by ∼0.4 V. The quantum chemical studies of the ∼50 AQ derivatives are used to derive a relationship that connects the computed LUMO energy and the reduction potential that can be applied as a descriptor for screening thousands of AQ derivatives. Our computations also suggest that incorporating oxy-methyl dioxolane substituents in the AQ framework can increase its interaction with nonaqueous solvent and improve its solubility. Thermochemical calculations for likely bond breaking decomposition reactions of unsubstituted AQ anions suggest that the dianions are relatively stable in the solution. These studies provide an ideal platform to perform further combined experimental and theoretical studies to understand the electrochemical reversibility and solubility of new quinone molecules as energy storage materials.

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.

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.

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.

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 (kf or kb ) 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.

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.

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.

Glucose and fructose to platform chemicals: understanding the thermodynamic landscapes of acid-catalysed reactions using high-level ab initio methods.

Molecular level understanding of acid-catalysed conversion of sugar molecules to platform chemicals such as hydroxy-methyl furfural (HMF), furfuryl alcohol (FAL), and levulinic acid (LA) is essential for efficient biomass conversion. In this paper, the high-level G4MP2 method along with the SMD solvation model is employed to understand detailed reaction energetics of the acid-catalysed decomposition of glucose and fructose to HMF. Based on protonation free energies of various hydroxyl groups of the sugar molecule, the relative reactivity of gluco-pyranose, fructo-pyranose and fructo-furanose are predicted. Calculations suggest that, in addition to the protonated intermediates, a solvent assisted dehydration of one of the fructo-furanosyl intermediates is a competing mechanism, indicating the possibility of multiple reaction pathways for fructose to HMF conversion in aqueous acidic medium. Two reaction pathways were explored to understand the thermodynamics of glucose to HMF; the first one is initiated by the protonation of a C2-OH group and the second one through an enolate intermediate involving acyclic intermediates. Additionally, a pathway is proposed for the formation of furfuryl alcohol from glucose initiated by the protonation of a C2-OH position, which includes a C-C bond cleavage, and the formation of formic acid. The detailed free energy landscapes predicted in this study can be used as benchmarks for further exploring the sugar decomposition reactions, prediction of possible intermediates, and finally designing improved catalysts for biomass conversion chemistry in the future.

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.