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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Computational and experimental analysis of structural and Thermophysical properties of LiX-KX (X = chloride, iodide, or bromide) molten salts

Molten salts containing lithium (Li) and potassium (K) halides, including chlorides (Cl), bromides (Br), and iodides (I), are pivotal in advanced technological applications including nuclear reactor technologies, thermal batteries, and industrial pyrochemical processes. Despite their significance, the availability of the temperature-dependent structural and thermophysical properties of these molten salts remain scarce. Here, this study addresses this knowledge gap by predicting and measuring the key properties such as coordination number, radial distribution function, density, heat capacity, and volumetric thermal expansion of molten LiI-KI, LiBr-KBr and LiCl-KCl eutectic mixtures. Utilizing ab initio molecular dynamics (AIMD) simulations, we predict these properties of LiX-KX (X=Br,I, Cl) across various temperatures, with chlorides as a benchmark. Experimental measurements using Archimedes methods and Differential Scanning Calorimetry validate the densities and heat capacities of these molten salts. Our comprehensive analysis of the structural and thermophysical properties provides critical insights into the behavior of these molten salts at various temperatures, enhancing the understanding necessary for their application in advanced technologies.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Ionic Interdiffusion at Cathode|Solid-Electrolyte Interface: A Machine Learning–Assisted Multiscale Investigation and Mitigation Strategies

Future lithium batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. However, most solid electrolytes are thermodynamically unstable against layered oxide cathodes. In this study, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular dynamics (AIMD) and machine learning molecular dynamics (MLMD). The propensity of ionic interdiffusion, formation of a passivating interphase layer, and corresponding decay in cell performance is addressed using a continuum model. Large-scale MLMD simulations confirm that the LCO|LGPS interface permits interdiffusion of cobalt (Co) and other ionic species, leading to the formation and growth of a resistive interphase and to dramatic capacity fade even in the first cycle. We examine the literature evidence that incorporating a thin layer of LiNb0.5Ta0.5O3 (LNTO) between LCO and LGPS prevents the interdiffusion of ions. Atomistic simulations suggest that substituting lithium (Li) in LNTO with Co is thermodynamically unfavorable, thereby inhibiting ionic interdiffusion. The stable Nb5+/Ta5+ states form a rigid metal-oxide framework, which consequently also prevents the substitution of niobium (Nb) or tantalum (Ta). However, continuum-level analysis suggests that the higher mechanical stiffness of LNTO can lead to interfacial delamination between the LCO and LNTO. This phenomenon reduces the effectiveness of the protective layer. This paper, therefore, highlights the need to develop novel interlayers that balance low ionic interdiffusion with low mechanical stiffness.

Ncube, Musawenkosi K.↗
DOE OSTI2025

Investigation of the iodate sorption mechanism by CoAl LDH through experiments and ab initio molecular dynamics simulations

Radioiodine released during the nuclear-fuel cycle constitutes a persistent radiological hazard. In this study, the IO 3 – uptake mechanism of CoAl LDH was resolved by combining pH-controlled sorption experiments, synchrotron XAFS, and DFT-based AIMD simulations. At pH close to 6, approximately 90% of IO 3 – was removed, and the equilibrium distribution coefficient reached about 1.7 × 10 4 mL g –1 . EXAFS analysis indicated an average iodine–oxygen bond length of 1.81 Å and a coordination number near 3, with the fit R-factor equal to 0.002. The simulations faithfully reproduced the experimental spectrum and revealed transient proton hopping events that generated metastable I–O–H species inside the interlayer, thereby confirming nitrate-to-iodate exchange as the controlling capture pathway. Atomic density profiles and radial distribution functions further showed that IO 3 – adopt an end-on orientation perpendicular to the hydroxide sheets, while water molecules mediate proton migration without disturbing the host lattice. In conclusion, the integrated experimental–computational evidence demonstrates that CoAl LDH can rapidly and selectively sequester IO 3 – under near-neutral conditions, offering atomic scale guidance for the rational engineering of layered sorbents for advanced radioactive-waste treatment.

Kang, Jaehyuk [Jeju National Univ. (Korea, Republi↗
DOE OSTI2025

Thermodynamic and Kinetic Mechanisms Governing the Synthesis of Nickel-Poor Cathodes

A deeper understanding of the thermodynamics and kinetics governing the lithiation and layering mechanisms of NMC cathode materials (LiNixMnyCozO 2 , where x + y + z = 1) offers valuable insights for enhancing synthesis methods and improving cathode performance. By employing atomistic and mesoscale approaches informed by in situ powder X-ray diffraction (PXRD) experiments, critical parameters for comprehending lithiation and layering processes and reaction rates were identified. The mesoscale approach captured the evolution of the phases and crystallite size observed in the in situ PXRD, revealing the differences in reaction rates with the use of different lithium salts and starting precursors. Ab initio molecular dynamics (AIMD) underscored the importance of vacancies and structural defects in promoting ion mobility and facilitating the nucleation of a layered domain. This nucleation disrupts the symmetry of disordered phases, ultimately creating a strained phase that serves as a buffer between layered and disordered regions. The lithiation and layering processes reflect a dynamic balance between the thermodynamic drive for a low-energy layered structure and the kinetic of diffusion, which is influenced by temperature and lithium vacancy concentration. Overall, reaction mechanisms are driven by the inherent defects of the intermediate phase that differ for NMC cathode materials. The lithium salts impact the rates of lithiation and layering, with a much slower process for Li 2 CO 3 .

25 ENERGY STORAGE↗
DOE OSTI2025

Liquid–Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl 3 ) Enabled by Machine Learning Interatomic Potentials

Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the AlCl 3 molten salt across varied thermodynamic conditions (T = 473–613 K and P = 2.7–23.4 bar), which allowed us to predict temperature-surface tension correlations and liquid–vapor phase diagram from direct simulations of two-phase coexistence in this molten salt. Two MLIP architectures, a Kernel-based potential and neural network interatomic potential (NNIP), were considered to benchmark their performance for AlCl 3 molten salt using experimental structure and density values. The NNIP potential employed in two-phase equilibrium simulations yields the critical temperature and critical density of AlCl 3 that are within 10 K (∼3%) and 0.03 g/cm 3 (∼7%) of the reported experimental values. An accurate correlation between temperature and viscosities is obtained as well. In doing so, we report that the inclusion of low-density configurations in their training is critical to more accurately represent the AlCl 3 system across a wide phase-space. The MLIP trained using PBE-D3 functional in the ab initio molecular dynamics (AIMD) simulations (120 atoms) also showed close agreement with experimentally determined molten salt structure comprising Al 2 Cl 6 dimers, as validated using Raman spectra and neutron structure factor. Furthermore, the PBE-D3 as well as its trained MLIP showed better liquid density and temperature correlation for AlCl 3 system when compared to several other density functionals explored in this work. Overall, the demonstrated approach to predict temperature correlations for liquid and vapor densities in this study can be employed to screen nuclear reactors-relevant compositions, helping to mitigate safety concerns.

Ab initio molecular dynamics↗
DOE OSTI2025

Temperature-Dependent Speciation of Ni(II) in Molten Chloride, Bromide, and Iodide Salts

Understanding the fundamental speciation and local structure of metal cations─such as nickel, which is present as a corrosion product─in molten salt media is essential for assessing their impact on the thermal and physical properties of salts used for advanced nuclear energy applications. In this study, we employed an integrated approach of combining X-ray absorption spectroscopy (XAS), UV–vis spectroscopy, and ab initio molecular dynamics (AIMD) to investigate the halide-dependent speciation of divalent nickel ions, Ni(II), in LiCl–KCl, LiBr–KBr, and LiI–KI eutectic salt mixtures from room temperature to 600 °C. Our findings show that both halide type and temperature significantly influence the local coordination of nickel in these molten salt systems. A notable aspect of Ni(II) speciation in LiI–KI salts is the formation of a polyiodide species. In conclusion, our findings provide critical insights into metal ion speciation in molten salt systems, enhancing our understanding of the factors that govern the physicochemical properties of these complex fluids.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ab Initio Modeling of Aqueous Methanol Mixtures at DFT-SCAN Level Using Machine Learning Interatomic Potentials

Abstract Methanol–water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood–Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol–water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.

Park, Sanghyun J. [Princeton University , , , ,]↗
DOE OSTI2024

A Molecular View of Methane Activation on Ni(111) through Enhanced Sampling and Machine Learning

A combination of machine learned interatomic potentials (MLIPs) and enhanced sampling simulations is used to investigate the activation of methane on a Ni(111) surface. The work entails the development and iterative refinement of MLIPs, initially trained on a dataset constructed via ab initio molecular dynamics (AIMD) simulations, supplemented by adaptive biasing forces, to enrich the sampling of catalytically relevant configurations. Our results reveal that by incorporating collective variables that capture the behavior of the reactant molecule, as well as additional frames that describe the dynamic response of the catalytic surface, it is possible to enhance considerably the accuracy of predicted energies and forces. By employing enhanced sampling schemes in the refinement of the MLIP, we systematically explore the potential energy surface, leading to a refined MLIP capable of predicting DFT-level energies and forces and replicating key geometric characteristics of the catalytic system. The resulting free energy landscapes at several temperatures provide a detailed view of the thermodynamics and dynamics of methane activation. Specifically, as methane approaches and dissociates on the catalytic surface, the process involves the dynamic interplay of CH 4 and the Ni catalyst that includes both enthalpic and entropic contributions. The progression towards the transition state involves an CH 4 moiety that is increasingly restrained in its ability to rotate or translate, while the stage following the transition state is characterized by a notable rise of the Ni atom that interacts with the cleaved C–H bond. Furthermore, this leads to an increase in the mobility of the adsorbed species, a feature that becomes more pronounced at higher temperatures.

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

Capturing Coupled Structural and Electronic Motions During Excited-State Intramolecular Proton Transfer via Computational Multiedge Resonant Inelastic X-ray Scattering

Proton-transfer processes build the foundation of many chemical processes. In Excited State Intramolecular Proton Transfer (ESIPT) processes the proton transfer process is impulsively started through light. Here, in this study, we explore the changes in coupled atomic and electronic motions during and following ESIPT through computational time-resolved Resonant Inelastic X-ray Scattering (trRIXS). Excited-state Ab Initio Molecular Dynamics (AIMD) simulations combined with Time-Dependent Density Functional Theory (TDDFT) calculations were performed for 10-hydroxybenzo[h]quinoline to obtain trRIXS signatures. RIXS at both the nitrogen and oxygen K-edges were computed to resolve the dynamics electronic structure from both the proton donor and acceptor perspective. The results show how RIXS can reveal the local electronic structure, the coupling between different electronic states and how electronic structure and coupling change during the proton transfer process. Additionally, we observe a strong correlation between spectral changes and structural changes during ESIPT.

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

Reactivity of Carbonate Solvent Electrolytes on Lithium Silicon Anodes

Silicon (Si) is promising for lithium-ion battery (LIB) anodes due to their high theoretical capacity and low electrochemical potential. However, significant challenges remain, including severe volumetric expansion during cycling and the electrochemical instability of electrolytes, which leads to the formation of a nonuniform solid electrolyte interphase (SEI). To investigate SEI formation mechanisms, computational molecular dynamics simulations offer valuable insights. In this work, we examine the trajectories and charge transfer behavior of lithium hexafluorophosphate (LiPF 6 ) salt with various solvent compositions using density functional theory (DFT) and ab initio molecular dynamics (AIMD). Among the tested electrolyte systems, LiPF 6 with vinylene carbonate (VC) added to ethyl methyl carbonate (EMC) exhibits the lowest reactivity with the Si anode. In contrast, the effects of fluoroethylene carbonate (FEC) and VC depend on whether the primary solvent is EMC alone or a mixture of ethylene carbonate (EC) and EMC. Moreover, we show that electrolyte reactivity varies with the degree of lithiation of the Si anode (LiSi vs Li 15 Si 4 ) and under different charge states. To decouple electrolyte reactivity from surface effects, we analyze the dissociation and formation energies of individual species from solvated configurations. Overall, these first-principles-based findings provide a strategic foundation for electrolyte design to improve cycling stability and extend calendar life in LIBs using Si anodes.

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

NH 3 -Mediated Reactive Capture and Conversion: Integrating CO 2 Absorption from Flue Gas with CO Production via NH 4 HCO 3 Electrolysis

Efficient carbon capture and utilization require strategies that minimize energy penalties of CO 2 regeneration and compression. Reactive capture and conversion (RCC) address this challenge by integrating capture with direct electrochemical conversion. Here, we show an NH3-mediated tandem RCC system that couples capture of CO 2 from simulated flue gas (10% v/v CO 2 in N 2 ) with electroreduction of NH 4 HCO 3 to CO over a Ni single-atom catalyst (Ni-SAC). Speciation modeling and capture experiments revealed that a deep CO 2 capture with C/N ratio of 0.65 was achieved using 2.5 M NH 3 from simulated flue gas. Electrolysis of the resulting NH 4 HCO 3 on the Ni- SAC delivered an 85% CO Faradaic efficiency at 100 mA/cm 2 with excellent tolerance to NH 3 /NH 4 + as confirmed by DFT calculations and ab initio molecular dynamics (AIMD) simulations. Further, the technoeconomic analysis established a levelized total cost of CO manufacturing of $25.43/kmol, gauging the practical viability. Overall, this study holds great potential to decarbonize the chemical manufacturing industry while reducing synthetic production costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Porous and Amorphous Mn x Mo 3 S 13 Chalcogel Electrode for High-Capacity Conversion-Based Lithium-Ion Batteries

While Li-ion batteries (LIBs) are a leading energy storage technology, their energy densities are limited by the low capacity of conventional intercalation cathodes, driving interest in high energy-density Li–S batteries that make use of conversion chemistry. Achieving high capacity, reversibility, and cycle stability, and controlling volume changes in conversion batteries during the charge–discharge process, however, remains challenging. Here, in this study, we present a porous, amorphous, sulfide-based Mn x Mo 3 S 13 chalcogel, which concurrently offers high capacity and cycle stability. The solution-processable room temperature synthesized Mn x Mo 3 S 13 (x = 0.25) chalcogel exhibits a local structure that resembles the Mo 3 S 13 cluster with Mn 2+ distributed across the Mo 3 S 13 matrix, as determined by synchrotron X-ray pair distribution function (PDF) and extended X-ray absorption fine structure (EXAFS). Ab initio molecular dynamics (AIMD) simulations reveal that Mn 2+ incorporation shortens the polysulfide chain in the gel matrix compared to the Mo 3 S 13 chalcogel, while forming a coordination environment with disulfide groups, analogous to the experimental findings. A Li/Mn 0.25 Mo 3 S 13 half-cell delivers 897 mAh g –1 capacity during the first discharge and retains 571 mAh g –1 capacity after 100 cycles at a C/3 rate. Distribution of relaxation time (DRT) unveils a stable solid–electrolyte interphase (SEI) formation upon cycling that enables charge–discharge reversibility. Here, the enhanced capacity retention and cycle stability compared to those of the Li/Mo 3 S 13 cell are attributed to the reduced dissolution of active mass into the electrolyte, facilitated by the formation of shorter polysulfide chains within the Mn 0.25 Mo 3 S 13 structure and the strong affinity of Lewis-acidic Mn 2+ for polysulfide anions generated during the charge–discharge process of the Li/Mn 0.25 Mo 3 S 13 cell. Thus, this work illustrates a design principle of material for high-capacity and cycle-stable Li-metal sulfide batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Versatile Cell Design for Molten Fluoride Salt Spectroscopy: Investigating Metal-Ion Speciation in Molten Fluoride Salts

Fluoride-based molten salts are widely used in industrial applications including aluminum production, thermal energy storage, optical crystal growth, and advanced nuclear reactor designs. Despite the wide range of uses, fundamental understandings of coordination chemistry and methods for probing molten fluorides are scarce, likely due to the difficulty of probing fluoride melts with spectroscopic techniques. Performing spectroscopic measurements of fluoride-based salts is challenging due to the highly corrosive nature of these salts, which can degrade many common optical materials. Here, in this work, we present a versatile optical cell design that enables spectroscopic measurements of corrosive melts. This innovative cell design overcomes the challenges posed by the corrosive nature of the salts, allowing for an accurate and consistent spectroscopic analysis. This work reports temperature-dependent absorption measurements for Co 2+ , Ni 2+ , and Cr 3+ analytes in LiF-NaF-KF eutectic salt (i.e., FLiNaK), which are common corrosion products originating from structural alloys in molten-fluoride handling. Absorption spectra were used to understand interactions of these analytes with FLiNaK, particularly ligand field coordination. The analysis of absorption spectra was complemented by structural analyses using ab initio molecular dynamics (AIMD) simulations, providing deeper insights into the behavior of the analytes in FLiNaK. Our findings indicate that the analytes studied in this work exist in octahedral or near-octahedral coordination states that remain stable across the temperature range of 500–600 °C. This work not only highlights an applied solution to performing optical spectroscopy in corrosive, high-temperature melts but also provides important fundamental insight on coordination behavior of transition-metal species in molten fluorides.

Fluoride salt spectroscopy↗
DOE OSTI2024

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

Abstract The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å −1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

Science & Technology - Other Topics↗
DOE OSTI2025

Controlling solvation in conducting redox polymers for selective electrochemical separation of nitrate from wastewater

Selective capture of nitrate from wastewater is crucial for ensuring safe drinking water and promoting resource circularity. This study investigated alkylated polyaniline redox polymers as highly-selective electrosorbents to address this challenge. By controlling polymer solvation properties through synthetic functionalization, poly(N-methylaniline) (PNMA) achieves a nitrate uptake of up to 1.38 mmol g −1 -polymer and a separation factor of 7 over chloride. Poly(N-butylaniline) (PNBA) further enhances selectivity, achieving a separation factor beyond 14 due to increased hydrophobicity. The mechanisms underlying this selectivity are investigated using ab initio molecular dynamics (AIMD) and in-situ electrochemical quartz crystal microbalance (EQCM) studies, which reveal that hydrophobicity reduces chloride binding. A technoeconomic analysis indicates that methylation on PANI reduces nitrate removal costs by 50% compared to non-functionalized PANI, due to enhanced selectivity and uptake, and decreased energy consumption. PNMA electrodes demonstrate practical nitrate selectivity over 20 versus chloride in real wastewater, while avoiding sulfate binding. This study highlights the potential of controlling solvation at electroactive polymers to enhance nitrate selectivity, offering a promising design path for redox-mediated electrochemical separations.

chemical engineering↗
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

Superionic-like diffusion in yttrium dihydride

For the next-generation high temperature microreactors, yttrium dihydride (YH 2 ) is an attractive solid state neutron moderator. Despite a number of recent investigations, the mechanism of hydrogen transport remains poorly understood. Experimental evaluations of diffusivity are inconclusive with large variations in diffusivities and activation energies. In this work, we perform ab initio molecular dynamics (AIMD) simulations on YH 2 for temperatures spanning 300 K to 1200 K. Our main finding is that YH 2 shows a superionic-like behavior with hydrogen atoms hopping from one native site to another above a characteristic temperature of 800 K. This correlated motion results in quasi-one-dimensional string-like displacements that enable the hydrogen atoms to diffuse rapidly. We confirm that the octahedral sites are mostly unoccupied, although channeling through them is the most favored pathway between lattice hops above 800 K. At the highest temperature of 1200 K, the string relaxation time is merely of the order of a few picoseconds, which indicates a liquid-like diffusive behavior. Based on the formation of spontaneous thermal vacancies, an order-disorder crossover temperature T α ~ 800 K is established for YH 2 with an activation energy of 0.83 eV for hydrogen diffusion in the superionic-like state.

Superionic-like Diffusion↗