Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability. Here, we demonstrate how this vision became reality by combining state-of-the-art machine learning (ML) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the NaxLi3-xYCl6 (0≤ x≤ 3) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. The showcased screening of millions of materials candidates highlights the transformative potential of advanced ML and HPC methodologies, propelling materials discovery into a new era of efficiency and innovation.

From Bulk to Interface: Solvent Exchange Dynamics and Their Role in Ion Transport and the Interfacial Model of Rechargeable Magnesium Batteries.

Multivalent battery chemistries have been explored in response to the increasing demand for high-energy rechargeable batteries utilizing sustainable resources. Solvation structures of working cations have been recognized as a key component in the design of electrolytes; however, most structure-property correlations of metal ions in organic electrolytes usually build upon favorable static solvation structures, often overlooking solvent exchange dynamics. We here report the ion solvation structures and solvent exchange rates of magnesium electrolytes in various solvents by using multimodal nuclear magnetic resonance (NMR) analysis and molecular dynamics/density functional theory (MD/DFT) calculations. These magnesium solvation structures and solvent exchange dynamics are correlated to the combined effects of several physicochemical properties of the solvents. Moreover, Mg2+ transport and interfacial charge transfer efficiency are found to be closely correlated to the solvent exchange rate in the binary electrolytes where the solvent exchange is tunable by the fraction of diluent solvents. Our primary findings are (1) most battery-related solvents undergo ultraslow solvent exchange coordinating to Mg2+ (with time scales ranging from 0.5 µs to 5 ms), (2) the cation transport mechanism is a mixture of vehicular and structural diffusion even at the ultraslow exchange limit (with faster solvent exchange leading to faster cation transport), and (3) an interfacial model wherein organic-rich regions facilitate desolvation and inorganic regions promote Mg2+ transport is consistent with our NMR, electrochemistry, and cryogenic X-ray photoelectron spectroscopy (cryo-XPS) results. This observed ultraslow solvent exchange and its importance for ion transport and interfacial properties necessitate the judicious selection of solvents and informed design of electrolyte blends for multivalent electrolytes.

In Situ Characterization of Metastable Pb3O5 and Pb2O3 Phases During Thermal Decomposition of PbO2 to PbO.

Nonstoichiometric lead oxides play a key role in the formation and cycling of the positive electrodes in a lead acid battery. These phases have been linked to the underutilization of the positive active material but also play a key role in the battery's cycle life, providing interparticle adhesion and the connection to the underlying lead grid. Similar phases have previously been identified by mass loss or color change during thermal annealing of PbO2 to PbO, suggesting that at least two intermediate PbOx phases exist. Using multiple, in situ analysis techniques (powder diffraction, X-ray absorption, X-ray photoelectron spectroscopy) and ex situ nuclear magnetic resonance measurements, the structural conversion and changes in the lead oxidation state were identified during this process. Isolation of the PbOx phases enabled confirmation of Pb3O5 and Pb2O3 by diffraction and the first 207Pb NMR measurement of these intermediates. The thermodynamic and kinetic stability of these intermediates and other reported polymorphs were determined by density functional theory, providing key insight into their origins and variation of PbOx structures found in previous studies.

Electrochemical Imaging of Precisely-Defined Redox and Reactive Interfaces.

Understanding the diverse electrochemical reactions occurring at electrode-electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular-level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely-defined EEIs and visualizing the reactivity of their individual components using single-entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state-of-the-art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well-defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications.

A New Sodium Thioborate Fast Ion Conductor: Na3 B5 S9.

We report a new sodium fast-ion conductor, Na3 B5 S9 , that exhibits a high Na ion total conductivity of 0.80 mS cm-1 (sintered pellet; cold-pressed pellet=0.21 mS cm-1 ). The structure consists of corner-sharing B10 S20 supertetrahedral clusters, which create a framework that supports 3D Na ion diffusion channels. The Na ions are well-distributed in the channels and form a disordered sublattice spanning five Na crystallographic sites. The combination of structural elucidation via single crystal X-ray diffraction and powder synchrotron X-ray diffraction at variable temperatures, solid-state nuclear magnetic resonance spectra and ab initio molecular dynamics simulations reveal high Na-ion mobility (predicted conductivity: 0.96 mS cm-1 ) and the nature of the 3D diffusion pathways. Notably, the Na ion sublattice orders at low temperatures, resulting in isolated Na polyhedra and thus much lower ionic conductivity. This highlights the importance of a disordered Na ion sublattice-and existence of well-connected Na ion migration pathways formed via face-sharing polyhedra-in dictating Na ion diffusion.

Concentration-dependent ion correlations impact the electrochemical behavior of calcium battery electrolytes.

Ion interactions strongly determine the solvation environments of multivalent electrolytes even at concentrations below that required for practical battery-based energy storage. This statement is particularly true of electrolytes utilizing ethereal solvents due to their low dielectric constants. These solvents are among the most commonly used for multivalent batteries based on reactive metals (Mg, Ca) due to their reductive stability. Recent developments in multivalent electrolyte design have produced a variety of new salts for Mg2+ and Ca2+ that test the limits of weak coordination strength and oxidative stability. Such electrolytes have great potential for enabling full-cell cycling of batteries based on these working ions. However, the ion interactions in these electrolytes exhibit significant and non-intuitive concentration relationships. In this work, we investigate a promising exemplar, calcium tetrakis(hexafluoroisopropoxy)borate (Ca(BHFIP)2), in the ethereal solvents 1,2-dimethoxyethane (DME) and tetrahydrofuran (THF) across a concentration range of several orders of magnitude. Surprisingly, we find that effective salt dissociation is lower at relatively dilute concentrations (e.g. 0.01 M) than at higher concentrations (e.g. 0.2 M). Combined experimental and computational dielectric and X-ray spectroscopic analyses of the changes occurring in the Ca2+ solvation environment across these concentration regimes reveals a progressive transition from well-defined solvent-separated ion pairs to de-correlated free ions. This transition in ion correlation results in improvements in both conductivity and calcium cycling stability with increased salt concentration. Comparison with previous findings involving more strongly associating salts highlights the generality of this phenomenon, leading to important insight into controlling ion interactions in ether-based multivalent battery electrolytes.

In Situ Probing Potassium-Ion Intercalation-Induced Amorphization in Crystalline Iron Phosphate Cathode Materials.

Na-ion and K-ion batteries are promising alternatives for large-scale energy storage due to their abundance and low cost. Intercalation of these large ions could cause irreversible structural deformation and partial to complete amorphization in the crystalline electrodes. A lack of understanding of the dynamic changes in the amorphous nanostructure during battery operation is the bottleneck for further developments. Here, we report the utilization of in-operando digital image correlation and XRD techniques to probe dynamic changes in the amorphous phase of iron phosphate during potassium ion intercalation. In-operando XRD demonstrates amorphization in the electrode's nanostructure during the first charge and discharge cycle. Additionally, ex situ HR-TEM further confirms the amorphization after potassium-ion intercalation. An in situ strain analysis detects reversible deformations associated with redox reactions in the amorphous phases. Our approach offers new insights into the mechanism of ion intercalation in the amorphous nanostructure which are highly potent for the development of next-generation batteries.

Graphical Gaussian process regression model for aqueous solvation free energy prediction of organic molecules in redox flow batteries.

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using the Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model for electrostatic interaction, the nonpolar interaction contribution of the solvent and the conformational entropy of the solute in the solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of the conformational entropy of the solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal mol-1 for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

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.

Coupled Experimental-Theoretical Characterization of a Carbon Electrode in Vanadium Redox Flow Batteries using X-ray Absorption Spectroscopy.

Vanadium redox flow batteries (VRFBs) have emerged as promising solutions for stationary grid energy storage due to their high efficiency, scalability, safety, near room-temperature operation conditions, and the ability to independently size power and energy capacities. The performance of VRFBs heavily relies on the redox couple reactions of V2+/V3+ and VO2+/VO2+ on carbon electrodes. Therefore, a thorough understanding of the surface functionality of carbon electrodes and their propensity for degradation during electrochemical cycles is crucial for designing VRFBs with extended lifespans. In this study, we present a coupled experimental-theoretical approach based on carbon K edge X-ray absorption spectroscopy (XAS) to characterize carbon electrodes prepared under different conditions and identify relevant functional groups that contribute to unique spectroscopic features. Atomic models were created to represent functional groups, such as hydroxyl, carboxyl, methyl, and aldehyde, bonded to carbon atoms in either sp2 or sp3 environments. The interactions between functionalized carbon and various solvated vanadium complexes were modeled using density functional theory. A library of carbon K-edge XAS spectra was generated for distinct carbon atoms in different functional groups, both before and after interacting with solvated vanadium complexes. We demonstrate how these simulated spectra can be used to deconvolve ex situ experimental spectra measured from carbon electrodes and to track changes in the electrode composition following immersion in different electrolytes or extended cycling within a functional VRFB. By doing so, we identify the active species present on the carbon electrodes, which play a crucial role in determining their electrochemical performance.

Uptake of Pb and the Formation of Mixed (Ba,Pb)SO4 Monolayers on Barite During Cyclic Exposure to Lead-Containing Sulfuric Acid.

Barite (BaSO4) is a common additive in lead-acid batteries, where it acts as a nucleating agent to promote the reversible formation and dissolution of PbSO4 during battery cycling. However, little is known about the molecular-scale mechanisms that control the nucleation and cyclic evolution of PbSO4 over a battery's lifetime. In this study, we explore the responses of a barite (001) surface to cycles of high and low lead concentrations in 100 mM sulfuric acid solution using in situ atomic force microscopy and high-resolution X-ray reflectivity. We find that PbSO4 epitaxial films readily nucleate on the barite surface, even from solutions that are undersaturated relative to bulk PbSO4. Despite this, barite (001) proves to be an ineffective nucleator of bulk PbSO4, as multilayer growth is suppressed even in highly supersaturated solutions. Instead, we find evidence that Pb2+ ions can directly exchange with Ba2+ to create mixed (Ba,Pb)SO4 surfaces. These chemically mixed surfaces do not host PbSO4 monolayers as readily as pristine barite, and the original reactivity is not regained until a fresh surface is re-established by aggressive etching. Our results can be partly explained by traditional models of thin-film growth, which predict a Stranski-Krastanov (S-K) growth mode, where monolayer films are stabilized by a reduction in surface energy, but multilayer growth is inhibited by epitaxial strain. Complementary density functional theory calculations confirm the basic energetic terms of the model but also show evidence for thickness-dependent energetics that are more complex than would be predicted from traditional models. The experimental results are better understood by extending the model to consider the formation of mixed surfaces and films, which have reduced strain and interfacial energies relative to pure films while also being stabilized by entropy of mixing. These insights into nonstoichiometric heteroepitaxy will enable better predictions of how barite affects PbSO4 nucleation in battery environments.

Probing the Formation of Cathode-Electrolyte Interphase on Lithium Iron Phosphate Cathodes via Operando Mechanical Measurements.

Interfacial instabilities in electrodes control the performance and lifetime of Li-ion batteries. While the formation of the solid-electrolyte interphase (SEI) on anodes has received much attention, there is still a lack of understanding the formation of the cathode-electrolyte interphase (CEI) on the cathodes. To fill this gap, we report on dynamic deformations on LiFePO4 cathodes during charge/discharge by utilizing operando digital image correlation, impedance spectroscopy, and cryo X-ray photoelectron spectroscopy. LiFePO4 cathodes were cycled in either LiPF6, LiClO4, or LiTFSI-containing organic liquid electrolytes. Beyond the first cycle, Li-ion intercalation results in a nearly linear correlation between electrochemical strains and the state of (dis)-charge, regardless of the electrolyte chemistry. However, during the first charge in the LiPF6-containing electrolyte, there is a distinct irreversible positive strain evolution at the onset of anodic current rise as well as current decay at around 4.0 V. Impedance studies show an increase in surface resistance in the same potential window, suggesting the formation of CEI layers on the cathode. The chemistry of the CEI layer was characterized by X-ray photoelectron spectroscopy. LiF is detected in the CEI layer starting as early as 3.4 V and LixPOyFz appeared at voltages higher than 4.0 V during the first charge. Our approach offers insights into the formation mechanism of CEI layers on the cathode electrodes, which is crucial for the development of robust cathodes and electrolyte chemistries for higher-performance batteries.

Electrolyte Reactivity on the MgV2O4 Cathode Surface.

Predictive understanding of the molecular interaction of electrolyte ions and solvent molecules and their chemical reactivity on electrodes has been a major challenge but is essential for addressing instabilities and surface passivation that occur at the electrode-electrolyte interface of multivalent magnesium batteries. In this work, the isolated intrinsic reactivities of prominent chemical species present in magnesium bis(trifluoromethanesulfonimide) (Mg(TFSI)2) in diglyme (G2) electrolytes, including ionic (TFSI-, [Mg(TFSI)]+, [Mg(TFSI):G2]+, and [Mg(TFSI):2G2]+) as well as neutral molecules (G2) on a well-defined magnesium vanadate cathode (MgV2O4) surface, have been studied using a combination of first-principles calculations and multimodal spectroscopy analysis. Our calculations show that nonsolvated [Mg(TFSI)]+ is the strongest adsorbing species on the MgV2O4 surface compared with all other ions while partially solvated [Mg(TFSI):G2]+ is the most reactive species. The cleavage of C-S bonds in TFSI- to form CF3- is predicted to be the most desired pathway for all ionic species, which is followed by the cleavage of C-O bonds of G2 to yield CH3+ or OCH3- species. The strong stabilization and electron transfer between ionic electrolyte species and MgV2O4 is found to significantly favor these decomposition reactions on the surface compared with intrinsic gas-phase dissociation. Experimentally, we used state-of-the-art ion soft landing to selectively deposit mass-selected TFSI-, [Mg(TFSI):G2]+, and [Mg(TFSI):2G2]+ on a MgV2O4 thin film to form a well-defined electrolyte-MgV2O4 interface. Analysis of the soft-landed interface using X-ray photoelectron, X-ray absorption near-edge structure, electron energy-loss spectroscopies, as well as transmission electron microscopy confirmed the presence of decomposition species (e.g., MgFx, carbonates) and the higher amount of MgFx with [Mg(TFSI):G2]+ formed in the interfacial region, which corroborates the theoretical observation. Overall, these results indicate that Mg2+ desolvation results in electrolyte decomposition facilitated by surface adsorption, charge transfer, and the formation of passivating fluorides on the MgV2O4 cathode surface. This work provides the first evidence of the primary mechanisms leading to electrolyte decomposition at high-voltage oxide surfaces in multivalent batteries and suggests that the design of new, anodically stable electrolytes must target systems that facilitate cation desolvation.

Non-polar ether-based electrolyte solutions for stable high-voltage non-aqueous lithium metal batteries.

The electrochemical instability of ether-based electrolyte solutions hinders their practical applications in high-voltage Li metal batteries. To circumvent this issue, here, we propose a dilution strategy to lose the Li+/solvent interaction and use the dilute non-aqueous electrolyte solution in high-voltage lithium metal batteries. We demonstrate that in a non-polar dipropyl ether (DPE)-based electrolyte solution with lithium bis(fluorosulfonyl) imide salt, the decomposition order of solvated species can be adjusted to promote the Li+/salt-derived anion clusters decomposition over free ether solvent molecules. This selective mechanism favors the formation of a robust cathode electrolyte interphase (CEI) and a solvent-deficient electric double-layer structure at the positive electrode interface. When the DPE-based electrolyte is tested in combination with a Li metal negative electrode (50 µm thick) and a LiNi0.8Co0.1Mn0.1O2-based positive electrode (3.3 mAh/cm2) in pouch cell configuration at 25 °C, a specific discharge capacity retention of about 74% after 150 cycles (0.33 and 1 mA/cm2 charge and discharge, respectively) is obtained.

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.

Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system strings, molecular graphs, and three-dimensional atomic coordinates using four different neural network architectures-fully connected neural networks, recurrent neural networks, graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about the molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.

An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions.

Identifying stable speciation in multi-component liquid solutions is fundamentally important to areas from electrochemistry to organic chemistry and biomolecular systems. Here we introduce a fully automated, high-throughput computational framework for the accurate prediction of stable species in liquid solutions by computing the nuclear magnetic resonance (NMR) chemical shifts. The framework automatically extracts and categorizes hundreds of thousands of atomic clusters from classical molecular dynamics simulations, identifies the most stable species in solution and calculates their NMR chemical shifts via density functional theory calculations. Additionally, the framework creates a database of computed chemical shifts for liquid solutions across a wide chemical and parameter space. We compare our computational results to experimental measurements for magnesium bis(trifluoromethanesulfonyl)imide Mg(TFSI)2 salt in dimethoxyethane solvent. Our analysis of the Mg2+ solvation structural evolutions reveals key factors that influence the accuracy of NMR chemical shift predictions in liquid solutions. Furthermore, we show how the framework reduces the performance of over 300 13C and 600 1H density functional theory chemical shift predictions to a single submission procedure.

SOMAS: a platform for data-driven material discovery in redox flow battery development.

Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database "Solubility of Organic Molecules in Aqueous Solution" (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

Understanding the Solvation-Dependent Properties of Cyclic Ether Multivalent Electrolytes Using High-Field NMR and Quantum Chemistry.

Efforts to expand the technological capability of batteries have generated increased interest in divalent cationic systems. Electrolytes used for these electrochemical applications often incorporate cyclic ethers as electrolyte solvents; however, the detailed solvation environments within such systems are not well-understood. To foster insights into the solvation structures of such electrolytes, Ca(TFSI)2 and Zn(TFSI)2 dissolved in tetrahydrofuran (THF) and 2-methyl-tetrahydrofuran were investigated through multi-nuclear magnetic resonance spectroscopy (17O, 43Ca, and 67Zn NMR) combined with quantum chemistry modeling of NMR chemical shifts. NMR provides spectroscopic fingerprints that readily couple with quantum chemistry to identify a set of most probable solvation structures based on the best agreement between the theoretically predicted and experimentally measured values of chemical shifts. The multi-nuclear approach significantly enhances confidence that the correct solvation structures are identified due to the required simultaneous agreement between theory and experiment for multiple nuclear spins. Furthermore, quantum chemistry modeling provides a comparison of the solvation cluster formation energetics, allowing further refinement of the preferred solvation structures. It is shown that a range of solvation structures coexist in most of these electrolytes, with significant molecular motion and dynamic exchange among the structures. This level of solvation diversity correlates with the solubility of the electrolyte, with Zn(TFSI)2/THF exhibiting the lowest degree of each. Comparisons of analogous Ca2+ and Zn2+ solvation structures reveal a significant cation size effect that is manifested in significantly reduced cation-solvent bond lengths and thus stronger solvent bonding for Zn2+ relative to Ca2+. The strength of this bonding is further reduced by methylation of the cyclic ether ring. Solvation shells containing anions are energetically preferred in all the studied electrolytes, leading to significant quantities of contact ion pairs and consequently neutrally charged clusters. It is likely that the transport and interfacial de-solvation/re-solvation properties of these electrolytes are directed by these anion interactions. These insights into the detailed solvation structures, cation size, and solvent effects, including the molecular dynamics, are fundamentally important for the rational design of electrolytes in multivalent battery electrolyte systems.