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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structure and Synthesizability of Iron–Sulfur Metal–Organic Frameworks

Sulfur-based metal–organic frameworks (MOFs) and coordination polymers (CPs) are an emerging class of hybrid materials that have received growing attention due to their magnetic, conductive, and catalytic properties with potential applications in electrocatalysis and energy storage. In this work, we report a high-throughput virtual screening protocol to predict the synthesizability of candidate metal–sulfur MOFs/CPs by computing the thermodynamically stable structures resulting from a particular combination of metal cluster, linker, cation, and synthetic conditions. Free energies are computed by using all-atom classical mechanical thermodynamic integration. Low-free-energy structures are refined using ab initio density functional theory, and pair distribution functions and powder X-ray diffraction patterns are calculated to complement and guide experimental structure determination. We validate the computational approach by retrospective predictions of the stable structure produced by experimental syntheses, and a subsequent screen predicts Fe 4 S 4 -BDT–TPP as a new thermodynamically stable one-dimensional (1D) CP comprising a redox-active Fe 4 S 4 cluster, a 1,4-benzenedithiolate (BDT) linker, and a tetraphenylphosphonium (TPP) countercation. Furthermore, this material is experimentally synthesized, and the 1D chain structure of the crystal is confirmed using microcrystal electron diffraction. The computational screening pipeline is generically transferable to neutral and ionic MOFs/CPs comprising arbitrary metal clusters, linkers, cations, and synthetic conditions, and we make it freely available as an open source tool to guide and accelerate the discovery and engineering of novel porous materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Molecular Insights Into the Ionic Assembly of Poly-Galacturonic Acid Oligomers - Impact of Charge, Ionic Radius, and Polymer Functionalization

Pectin, a major class of matrix polysaccharides present in plant cell walls (PCW), contains widespread anionic saccharides that cross-link in the presence of cations. It modulates important functions such as cell-cell adhesion and determines the PCW's biomechanical properties. It is known that mono-, di-, and tri-valent cations facilitate cross-linking; however, significant knowledge gaps remain in understanding the structure and mechanism of pectin cross-linking. In this study, replica-exchange molecular dynamics (REMD) simulations were employed to elucidate the role of ionic charge, ionic radii, and functional groups on the cross-linking of homogalacturonan (HG), the most abundant pectin molecule. Our enhanced sampling approach in fully solvated environments suggests more effective cross-linking with higher-valent and smaller ions, and that the "zipper" conformation is more favorable than the prevalent "egg-box" conformation. These findings advance our fundamental understanding of pectin matrix structure in PCWs and provide a solid foundation to probe structure-property relationships in pectic polysaccharides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Charge Tethering Drives Intermediate-Range Order and Slow Dynamics in Zwitterionic Liquids

We have synthesized and characterized a family of zwitterionic liquids (ZwLs) based on the poly(ethylene oxide) imidazolium cation and the alkyl sulfonate anion. To better rationalize ZwLs′ structural behavior and networkforming properties, we have computationally studied them in contrast to isostructural but chemically untethered ionic liquid (IL) analogues. Perhaps surprisingly, it is the molecular liquids and not the ILs that show the most selfassembly and intermediate-range order associated with scattering prepeaks; the ZwLs display also the slowest dynamics. This is consistent with prior work showing the importance of networks in considering the viscoelastic relaxation of liquids. We find that, because of their significantly large molecular dipoles, these zwitterions are versatile network-building blocks that result in highly viscous liquids. From a structural perspective, networks in these ZwLs sit somewhere in between what would be true chemical connectivity and the Coulombic networks observed in ionic liquids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Atomic layer deposited aluminum nitride for interface protection of lithium germanium thiophosphate solid electrolytes

Lithium germanium thiophosphate (LGPS) is an attractive solid-state electrolyte material due to its exceptionally high ionic conductivity (~1.2 × 10 –2 S cm –1 ), comparable to many organic liquid electrolytes commonly used in batteries. Despite the high conductivity of LGPS, the susceptibility of LGPS to deleterious degradation reactions has impeded its commercial adoption into solid-state batteries. In particular, the poor voltage stability of LGPS with high-voltage cathode or lithium metal potentials often results in dramatically increasing cell impedance during galvanic cycling. Here, we use aluminum nitride (AlN) as a protection layer for Li metal anode, applied directly to the LGPS at 250 °C using plasma-enhanced atomic layer deposition, to enhance cell performance by preventing LGPS-Li metal reactions. We compare the surface chemistry and electrochemical cycling performance of atomic layer-deposited AlN grown using both plasma N 2 and NH 3 precursors. Galvanostatic cycling and electrochemical impedance spectroscopy show that AlN-coated LGPS cells perform better than bare LGPS cells in contact with Li metal anodes, with the AlN able to improve cycling longevity by over a factor of 3 in certain cases. Finally, we utilize x-ray photoelectron spectroscopy (XPS) line scans to highlight the slow room-temperature reactivity between AlN and evaporated lithium metal, and a computational model is built to aid further XPS analysis.

25 ENERGY STORAGE↗
DOE OSTI2026

Integrated Direct Air Capture and H₂-Free CO₂ Valorization

This project advances fundamental understanding of a novel integrated direct air capture (DAC) and CO₂ conversion process that valorizes atmospheric CO₂ without external H₂. The research encompasses four critical components: (1) design of task-specific ionic liquids for efficient CO₂ capture under ambient conditions, (2) development of H₂-free tandem catalytic systems using ethane as a reductant, (3) advanced operando characterization to elucidate capture and conversion mechanisms, and (4) data science-driven predictive computation to accelerate material discovery. Over the project period, we developed five high-performance DAC sorbent systems—including CaO/superbase ionic liquid composites, Ni-MOF/Ionic Liquid (IL) hybrids, fluorinated covalent organic frameworks with ion-pair functional groups, defect-engineered UiO-66, and a validated kinetic model for humid-condition operation, achieving CO₂ capacities up to 1.86 mmol/g at 400 ppm with excellent cycling stability. For H₂-free conversion, we constructed atomically synergistic Zn–O–Cr binuclear catalytic sites that achieve 100% ethylene selectivity, ~9.6% ethane conversion, and 99% CO₂ utilization in equimolar co-conversion of ethane and CO₂. We further demonstrated downstream valorization pathways converting CO and C₂H₄ into polyketones and C₃ chemicals. These advances strengthen the scientific foundation for producing value-added materials from ambient CO₂.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Synthetic Accessibility and Sodium Ion Conductivity of the Na8–x A x P2O9 (NAP) High-Temperature Sodium Superionic Conductor Framework

Advancement of solid-state electrolytes (SSEs) for all solid-state batteries typically focuses on modification of a known structural framework to improve conductivity, e.g., cation substitution for an immobile ion or varying the concentration of the mobile ions. Novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, thereby unlocking optimal conductors with different properties. Herein, we perform a high-throughput survey of a structural framework for sodium ion conduction, Na8–x A x P2O9 (NAP), to understand the family’s thermodynamic stability, synthesizability, and ionic conduction. We show that the parent phase Na4TiP2O9 (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons arising from pseudo-Jahn–Teller mode in the 1D titanium chains. Screening compounds in which Ti is substituted by other metals computationally reveal a number of candidates that are predicted to be low in formation energy and have high predicted ionic conductivities. High-throughput experimental and subsequent methodology optimization trials deliver one new compound, Na4SnP2O9 (NSP). X-ray diffraction (XRD), microscopy, and spectroscopy characterization indicate that the room-temperature structure of NSP is similar to the high-temperature, orthorhombic NTP phase but with some small unresolved structural differences. These uncharacterized structural details are speculated to limit the ion conductivity. Temperature-dependent XRD and electrochemical impedance spectroscopy indicate multiple coupled conductivity–structure transitions at a high temperature. We demonstrate the challenges with synthesis development and a priori identification of promising SSE phases as a major bottleneck in new (energy) materials development.

Chemical reactions↗
DOE OSTI2025

Composite Behavior of Nanopore Array Large Memristors

Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements. Anodic aluminum oxide (AAO) membranes with 5 nm and 20 nm diameter pores and track-etched polycarbonate (PCTE) membranes with 10 nm diameter pores are tested and shown to demonstrate memristive and nonlinear behaviors with approximately 107–1010 pores in parallel when electrolyte concentration across the membranes is asymmetric. Ion diffusion through the large number of channels induces time-dependent electrolyte asymmetry that drives the system through different memristive states. The behaviors of series composite memristors with different configurations are also presented. In addition to helping understand fluidic devices and circuits for neuromorphic computing, the results also shed light on the development of field-assisted ion-selection-membrane filtration techniques as well as the investigations of large neurons and giant synapses. Further work is needed to de-embed parasitic components of the measurement setup to obtain intrinsic large memristor properties.

Chemistry↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ionic Liquids for Direct Air Capture of CO 2 using Electric‐Field‐Mediated Moisture Gradient Process (Final Technical Report)

The final report provides executive summary, a list of publications, and information on training graduate students and postdoctoral researchers. We carried out computational and experimental research on understanding molecular-level mechanism of how CO 2 is absorbed in a solution containing ethylene glycol as the solvent and KOH as the salt in the presence of ionic liquids and under the influence of electric field. In doing so, we developed an automated high-throughput method which allowed us to measure the solubility of CO 2 in a large number of ionic liquids, considerably speeding up the CO 2 solubility measurement. We also demonstrated how varying the concentration of ionic liquids in ethylene glycol can result in a maximum in ionic conductivity. Reaction of CO 2 and subsequent release results in a 50% reduction when the process is operated at an ionic liquid-ethylene glycol concentration yielding maximum ionic conductivity amongst all the ionic liquid-ethylene glycol combinations studied as a part of this research. We utilized machine learning models to identify unique ionic liquid-solvent combinations with ionic conductivity much higher than that measured for ionic liquid-ethylene glycol combinations. We demonstrated that the rate of CO 2 reaction with KOH in ethylene glycol can be optimized with the type of ionic liquid and its concentration. Overall, the research led to publication of 10 peer-reviewed research articles and several presentations at national conferences. We are also in the process of developing additional manuscripts based on the research carried out as a part of this project. Two graduate students and two postdoctoral researchers were supported on the funding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Local Chemical Enhancement and Gating of Organic Coordinated Ionic-Electronic Transport

Superior properties in organic mixed ionic-electronic conductors (OMIECs) over inorganic counterparts have inspired intense interest in biosensing, soft-robotics, neuromorphic computing, and smart medicine. However, slow ion transport relative to charge transport in these materials is a limiting factor. Here, it is demonstrated that hydrophilic molecules local to an interfacial OMIEC nanochannel can accelerate ion transport with ion mobilities surpassing electrophoretic transport by more than an order of magnitude. Furthermore, ion access to this interfacial channel can be gated through local surface energy. This mechanism is applied in a novel sensing device, which electronically detects and characterizes chemical reaction dynamics local to the buried channel. The ability to enhance ion transport at the nanoscale in OMIECs as well as govern ion transport through local chemical signaling enables new functionalities for printable, stretchable, and biocompatible mixed conduction devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Accurate energies for ππ* excited states via exchange scaling: the XS-CASSCF method

The state-averaged complete-active space self-consistent field method (SA-CASSCF) is a widely employed electronic structure method used for studying photochemistry and dynamics owing to its ability to provide a reliable description even of complicated cases while still retaining computational efficiency. However, SA-CASSCF suffers from one Achilles heel, related to the description of ionic ππ* excited states, whose energy is often overestimated by 1–2 eV. In light of this challenge, we present the XS-CASSCF method, a new approach based on the idea of exchange scaling (XS) that screens the involved energy terms to improve the excitation energies of singlet ionic ππ* states. First, we illustrate the power of the XS-CASSCF method using hexatriene and para-quinodimethane as examples, showing that it corrects the targeted ionic states while leaving the other states largely unaffected, giving root-mean-square errors (RMSE) below 0.2 eV for the four lowest states in both cases. Subsequently, XS-CASSCF vertical excitation energies are tested against theoretical best estimates for a set of 11 molecules and 56 excited states. XS-CASSCF performs exceptionally well for the ππ* states of hydrocarbons, reducing the RMSE over 21 excitation energies from 0.96 to 0.27 eV. In the challenging subset of molecules with heteroatoms and a larger number of ππ* and nπ* states, we find that improvements can also be obtained, albeit not as pronounced. We conclude with an outlook into more realistic molecular materials focusing on their singlet–triplet (S 1 /T 1 ) gaps, finding that significant improvements can be obtained along the whole range of S 1 /T 1 gaps studied, going from 0.1 eV to more than 1.5 eV. Owing to notable improvements across significant classes of molecules combined with its conceptual simplicity, we believe that XS-CASSCF is a promising addition to the electronic structure toolbox, serving both as a standalone electronic structure method and as a starting point for further correlated treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2026

Computational Insights into the Salt-Induced Modulation of Electron Transporting Conjugated Polyelectrolytes

The morphological and electronic properties of conjugated polyelectrolytes (CPEs) are highly sensitive to their ionic environment and remain poorly understood. To elucidate structure–property relationships in CPEs, we investigate the role of salt concentration on CPE morphology and hole conductivity using a quantum mechanically informed coarse-grained (CG) model coupled with semiclassical rate theory. Under good solvent conditions, high salt concentration induces torsional disorder along the conjugated backbone, decreasing hole delocalization. In contrast, under poor solvent conditions, high salt concentration promotes CPE aggregation, leading to thicker fibers and increased hole mobilities. Collectively, this work characterizes the competing interactions governing CPE assembly and hole transport as a function of salt concentration, highlighting ion engineering as a powerful strategy for tailoring the properties of mixed-conducting polymers.

diseases↗
DOE OSTI2024

Facilitating Ionic and Electronic Conduction in Radical Polymers through Controlled Assembly

The major objectives, research performed, and significant results associated with this effort agree with the originally proposed work. That is, we have made significant advances in terms of both the experimental and computational thrusts of this effort, and these key results have allowed for us to have significant impact in the materials chemistry community. As is usual, this has led to even greater tangible product generation in the final year of the work relative to the first two years. Importantly, we have observed that the materials created under this effort have promising electronic and ionic conductivity properties along these lines, and we have developed the initial structure-property-performance relationships that offer future promise for these open-shell materials in advanced energy applications. One metric that represents this success is the number of publications that appear in notable journals regarding the work performed. Additionally, the team has been invited to present at many conferences of leading societies and at top academic institutions due to the work associated with this award. Finally, we anticipate that this will be of interest to multiple communities in the materials chemistry realm, and they will have broader impact into related technologies as well.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Recent Improvements to the NWChem COSMO Module

This work presents recent developments in the COSMO solvation model implementation in NWChem. A new cavity construction approach, based on the solvent-excluding surface (SES) and utilizing the well-established GEPOL algorithm, has been introduced. Additionally, a straightforward procedure to merge surface segments that are too close─often a source of numerical artifacts─has been implemented. The available methods for correcting outlying surface charges have also been reviewed and improved. To validate the new implementation, we computed dielectric solvation energies for a chemically diverse set of approximately 100 molecules, including neutral species, small ions, and common ionic liquid components. Results were compared to those from GAMESS using the double-cavity method as a reference. Although the double-cavity approach can be regarded as more accurate, the simpler correction schemes available in NWChem─based on scaling factors or Lagrange multipliers─can achieve excellent agreement if the potential is also properly corrected, with mean unsigned deviations of around 0.15 kcal/mol. Predictions of typical vapor–liquid and liquid–liquid equilibria using a COSMO-SAC variant based on NWChem also showed very promising results.

Cavities↗
DOE OSTI2025

The Role of Water Volume Fraction on Water Adsorption in Anion Exchange Membranes

Water absorption plays a key role in the performance of polymeric anion exchange membranes. It influences important properties such as ionic conductivity and mechanical strength and alters their performance as solid electrolytes in hydrogen electrochemical devices for energy conversion. However, computational approaches that address the relationship between the polymer design and the absorption process are scarce. In this work, we introduce a simple thermodynamic model to predict the water absorption isotherms of polyelectrolyte membranes in contact with a water vapor reservoir that incorporates the specific chemical design of the polymers. The model accurately predicts the water content and macrostructural properties of polynorbornene membranes as a function of the water activity and successfully captures the effect of various polymer design parameters. The energy of pairwise attractive interactions predicted by our model provides a means to interpret the absorption process at the molecular level. The model also reveals the most significant favorable and unfavorable contributions to the free energy and indicates that their balance is solely governed by the water volume fraction, regardless of the polymer design. This universal behavior leads to important implications in the search for better ion exchange membranes.

Zaldivar, Gervasio [New York University, Brooklyn,↗