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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A [FeFe] Hydrogenase–Rubrerythrin Chimeric Enzyme Functions to Couple H 2 Oxidation to Reduction of H 2 O 2 in the Foodborne Pathogen Clostridium perfringens

[FeFe] hydrogenases are a diverse class of H 2 -activating enzymes with a wide range of utilities in nature. As H 2 is a promising renewable energy carrier, exploration of the increasingly realized functional diversity of [FeFe] hydrogenases is instrumental for understanding how these remarkable enzymes can benefit society and inspire new technologies. In this work, we uncover the properties of a highly unusual natural chimera composed of a [FeFe] hydrogenase and rubrerythrin as a single polypeptide. The unique combination of [FeFe] hydrogenase with rubrerythrin, an enzyme that functions in H 2 O 2 detoxification, raises the question of whether catalytic reactions, such as H 2 oxidation and H 2 O 2 reduction, are functionally linked. Herein, we express and purify a representative chimera from Clostridium perfringens (termed Cper HydR) and apply various electrochemical and spectroscopic approaches to determine its activity and confirm the presence of each of the proposed metallocofactors. The cumulative data demonstrate that the enzyme contains a surprising array of metallocofactors: the catalytic site of [FeFe] hydrogenase termed the H-cluster, two [4Fe-4S] clusters, two rubredoxin Fe(Cys) 4 centers, and a hemerythrin-like diiron site. The absence of an H 2 -evolution current in protein film voltammetry highlights an exceptional bias of this enzyme toward H 2 oxidation to the greatest extent that has been observed for a [FeFe] hydrogenase. Here, we demonstrate that Cper HydR uses H 2 , catalytically split by the hydrogenase domain, to reduce H 2 O 2 by the diiron site. Structural modeling suggests a homodimeric nature of the protein. Overall, this study demonstrates that Cper HydR is an H 2 -dependent H 2 O 2 reductase. Equipped with this information, we discuss the possible role of this enzyme as a part of the oxygen-stress response system, proposing that Cper HydR constitutes a new pathway for H 2 O 2 mitigation.

08 HYDROGEN↗
DOE OSTI2025

Copper-dependent halogenase catalyses unactivated C−H bond functionalization

Carbon–hydrogen (C–H) bonds are the foundation of essentially every organic molecule, making them an ideal place to do chemical synthesis. The key challenge is achieving selectivity for one particular C(sp 3 )−H bond. In recent years, metalloenzymes have been found to perform C(sp 3 )−H bond functionalization. Despite substantial progresses in the past two decades, enzymatic halogenation and pseudohalogenation of unactivated C(sp 3 )−H—providing a functional handle for further modification—have been achieved with only non-haem iron/α-ketoglutarate-dependent halogenases, and are therefore limited by the chemistry possible with these enzymes. Here, in this work, we report the discovery and characterization of a previously unknown halogenase ApnU, part of a protein family containing domain of unknown function 3328 (DUF3328). ApnU uses copper in its active site to catalyse iterative chlorinations on multiple unactivated C(sp 3 )−H bonds. By taking advantage of the softer copper centre, we demonstrate that ApnU can catalyse unprecedented enzymatic C(sp 3 )−H bond functionalization such as iodination and thiocyanation. Using biochemical characterization and proteomics analysis, we identified the functional oligomeric state of ApnU as a covalently linked homodimer, which contains three essential pairs—one interchain and two intrachain—of disulfide bonds. The metal-coordination active site in ApnU consists of binuclear type II copper centres, as revealed by electron paramagnetic resonance spectroscopy. This discovery expands the enzymatic capability of C(sp 3 )−H halogenases and provides a foundational understanding of this family of binuclear copper-dependent oxidative enzymes.

biocatalysis↗
DOE OSTI2026

Metal Identity and Coordination Environment Modulate Single-Atom Catalyst Stability During Electrocatalysis

A major hurdle to the implementation of single-atom catalysts (SACs) in real-world systems is a poor understanding of their stability under operating conditions, which is particularly relevant due to the high surface free energy of SACs. Here, we evaluated the aggregation behavior of a suite of SACs varied by metal identity (Fe, Co, Ni, and Cu) during electrocatalytic nitrate reduction using in situ X-ray absorption spectroscopy. The metal center had significant influence on reconstruction, where under identical applied reductive potentials, SACs underwent varying levels of reconstruction, ranging from no discernible change to complete reduction into metallic nanoparticles. Such in situ experiments revealed Cu SACs to be the most susceptible to aggregation, prompting a deeper investigation into how coordination environment (O-, B-, and N-graphene) affected Cu SAC aggregation. We further conducted density functional theory calculations to elucidate the relationship between Cu SAC structure and stability. This work deconvolutes the relationship between SAC architecture and stability, which is essential to evaluate and explain for the realization of SACs for electrocatalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Sharp front tracking with geometric interface reconstruction

Here, this paper presents a novel sharp front-tracking method designed to address limitations in classical front-tracking approaches, specifically their reliance on smooth interpolation kernels and extended stencils for coupling the front and fluid mesh. In contrast, the proposed method employs exclusively sharp, localized interpolation and spreading kernels, restricting the coupling to the interfacial fluid cells–those containing the interface/front. This localized coupling is achieved by integrating a divergence-preserving velocity interpolation method with a piecewise parabolic interface calculation (PPIC) and a polyhedron intersection algorithm to compute the indicator function and local interface curvature. Surface tension is computed using the Continuum Surface Force (CSF) method, maintaining consistency with the sharp representation. Additionally, we propose an efficient local roughness smoothing implementation to account for surface mesh undulations, which is easily applicable to any triangulated surface mesh. Building on our previous work, the primary innovation of this study lies in the localization of the coupling for both the indicator function and surface tension calculations. By reducing the interface thickness on the fluid mesh to a single cell, as opposed to the 4–5 cell spans typical in classical methods, the proposed sharp front-tracking method achieves a highly localized and accurate representation of the interface. This sharper representation mitigates parasitic currents and improves force balancing, making it particularly suitable for scenarios where the interface plays a critical role, such as microfluidics, fluid-fluid interactions, and fluid-structure interactions. The proposed method is comprehensively validated and tested on canonical interfacial flow problems, including stationary and translating Laplace equilibria, oscillating droplets, and rising bubbles. The presented results demonstrate that the sharp front-tracking method significantly outperforms the classical approach in terms of accuracy, stability, and computational efficiency. Notably, parasitic currents are reduced by approximately two orders of magnitude and stable results are obtained for parameter ranges where classical front tracking fails to converge.

42 ENGINEERING↗
DOE OSTI2024

Design principles for energy transfer in the photosystem II supercomplex from kinetic transition networks

Photosystem II (PSII) has the unique ability to perform water-splitting. With light-harvesting complexes, it forms the PSII supercomplex (PSII-SC) which is a functional unit that can perform efficient energy conversion, as well as photoprotection, allowing photosynthetic organisms to adapt to the naturally fluctuating sunlight intensity. Achieving these functions requires a collaborative energy transfer network between all subunits of the PSII-SC. In this work, we perform kinetic analyses and characterise the energy landscape of the PSII-SC with a structure-based energy transfer model. With first passage time analyses and kinetic Monte Carlo simulations, we are able to map out the overall energy transfer network. We also investigate how energy transfer pathways are affected when individual protein complexes are removed from the network, revealing the functional roles of the subunits of the PSII-SC. In addition, we provide a quantitative description of the flat energy landscape of the PSII-SC. We show that it is a unique landscape that produces multiple kinetically relevant pathways, corresponding to a high pathway entropy. These design principles are crucial for balancing efficient energy conversion and photoprotection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Connecting relativistic density functional theory to microscopic calculations

The development of systematic effective field theories (EFTs) for nuclear forces and advances in solving the nuclear many-body problem have greatly improved our understanding of dense nuclear matter and the structure of finite nuclei. For global nuclear calculations, density functional theories (DFTs) have been developed to reduce the complexity and computational cost required in describing nuclear systems. However, DFT often makes approximations and assumptions about terms included in the functional, which may introduce systematic uncertainties compared to microscopic calculations using EFTs. In this work, we investigate possible avenues of improving nuclear DFT using nonlinear relativistic mean-field (RMF) theory. We explore the impact of RMF model extensions by fitting the nonlinear RMF model to predictions of nuclear matter and selected closed-shell nuclei using four successful chiral EFT Hamiltonians. We find that these model extensions are impactful and important in capturing the physics present within chiral Hamiltonians, particularly for charge radii and neutron skins of closed-shell nuclei. However, there are additional effects that are not captured within the RMF model, particularly within the isoscalar sector of RMF theory. Additional model extensions and the reliability of the nonlinear RMF model are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Peripheral positions encode transport specificity in the small multidrug resistance exporters

In secondary active transporters, a relatively limited set of protein folds have evolved diverse solute transport functions. Because of the conformational changes inherent to transport, altering substrate specificity typically involves remodeling the entire structural landscape, limiting our understanding of how novel substrate specificities evolve. In the current work, we examine a structurally minimalist family of model transport proteins, the small multidrug resistance (SMR) transporters, to understand the molecular basis for the emergence of a novel substrate specificity. We engineer a selective SMR protein to promiscuously export quaternary ammonium antiseptics, similar to the activity of a clade of multidrug exporters in this family. Using combinatorial mutagenesis and deep sequencing, we identify the necessary and sufficient molecular determinants of this engineered activity. Using X-ray crystallography, solid-supported membrane electrophysiology, binding assays, and a proteoliposome-based quaternary ammonium antiseptic transport assay that we developed, we dissect the mechanistic contributions of these residues to substrate polyspecificity. We find that substrate preference changes not through modification of the residues that directly interact with the substrate but through mutations peripheral to the binding pocket. Our work provides molecular insight into substrate promiscuity among the SMRs and can be applied to understand multidrug export and the evolution of novel transport functions more generally.

Science & Technology - Other Topics↗
DOE OSTI2026

Tailored polyMOFs for ion transport in lithium-based battery electrolyte

Owing to their low flammability, solid-state and quasi-solid-state electrolytes are safer alternatives to liquid organic electrolytes for energy storage applications. Metal–organic frameworks (MOFs), with facile functional tunability, long-range order, and rich host–guest interactions, have been implemented as electrolyte materials in a wide range of energy storage applications. In this work, we investigate a class of MOFs called polyMOFs as quasi-solid-state electrolyte materials. Unlike MOF–polymer composites, which are physical mixtures of MOF particles and polymers, polyMOFs are composed of polymeric linkers and metal ion nodes that self-assembled into crystalline and porous framework materials. PolyMOFs thus marry the ionic transport properties of liquid electrolyte and polymers with the synthetic versatility and host–guest interactions of MOFs. We demonstrate that the functionality of the polymer backbone of the polyMOF linker can improve room-temperature ion transport in the material. The polyMOF based on poly(ethylene glycol) (PEG) exhibits greater ionic conductivity, lithium transference number, and lower activation energy than its polyethylene (PE) analog. Supported by solid-state 7 Li nuclear magnetic resonance spectroscopy, we propose these improvements are due to stronger coordination of Li + to oxide sites in PEG, allowing for dissociation of Li and its associated anion. DFT studies further reveal that the confined solvent molecule mediates Li + transport in PEG-functionalized UiO-66 via a metastable adsorption and hopping mechanism. This work lies at the interface of inorganic and polymer electrolytes, unveiling fundamental insights into the design of next-generation ion conductive materials for energy technologies.

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

Design of intrinsically disordered protein variants with diverse structural properties

Intrinsically disordered proteins (IDPs) perform a broad range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Computational design of IDPs with specific conformational properties has, however, been difficult because of their substantial dynamics and structural complexity. We describe a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs that differ in compaction, long-range contacts, and propensity to phase separate. We experimentally tested and validated our designs and analyzed the sequence features that determine conformations. We show how our results are captured by a machine learning model, enabling us to speed up the algorithm. Our work expands the toolbox for computational protein design and will facilitate the design of proteins whose functions exploit the many properties afforded by protein disorder.

Science & Technology - Other Topics↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup

The research presented in this report describes our progress in applying stochastic methods and uncertainty quantification, parametric study, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated with Griffin, with the goal of developing a reduced order (surrogate) model which can be rapidly sampled while perturbing multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums, which are controlled by a hybrid proportional, integral and derivative (PID) controller, actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time and $\beta$ from a transient Griffin simulation executed with the improved quasi-static method to provide the kinetic parameters) as inputs to functions which control the CD rotation angle. Using a number of stochastic method approaches, we developed a dual purpose training-surrogate model of the NTP system using polynomial regression. The trained model can be rapidly sampled while simultaneously perturbing various input parameters of the model, such as coefficients on the PID control, or temperature (directly affect the neutron cross section). The surrogate model delivers accurate results orders-of-magnitude faster (minutes, not days) than the base model. Once the base model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs and their effect on the output. For example, coefficients used in the PID control system may vary due to some physical interference, or there may be uncertainty in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters and operational boundaries can be determined. The goal of this work is to support development of an advanced control system to operate CDs in a functioning NTP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Parallelizing autotuning for HPC applications: Unveiling the potential of the speculation strategy in Bayesian optimization

In the exascale computing era, tuning High-Performance Computing (HPC) applications has become a significant computational challenge. Although Bayesian optimization (BO) has emerged as a promising tool for HPC performance tuning, the BO workflow is inherently sequential (i.e., one function evaluation at a time) and cannot leverage the huge amount of parallel resources present in modern supercomputers, resulting in a considerable underutilization of their computational capabilities. This paper explores the trade-off between search quality and parallelism in BO, investigating a diverse set of methods. Building upon both previous approaches from the literature and novel methodologies introduced in this work, our study provides a deep analysis to accelerate BO performance tuning. By examining a set of synthetic functions and practical HPC applications, our exploration analyzes the interaction among various BO methods for parallelization, the quantity of parallel resources, the runtime distribution of target HPC applications, and the costs associated with different search orchestration mechanisms that have been overlooked in previous studies. Compared to sequential BO, our novel methodology achieves comparable quality while demonstrating robust scalability in search time as the amount of parallel resources increases; it also outperforms a state-of-the-art tuner, which supports parallelization, achieving up to 3.67x faster search time. We provide high-value insights for practitioners seeking to leverage the power of parallel computing for efficient HPC application tuning. Additionally, to further assist researchers in accelerating the performance tuning of their HPC applications, we provide an extension of an existing open-source tuning framework that incorporates our methods.

Bayesian optimization↗
DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Direct Fabrication of Atomically Defined Pores in MXenes Using Feedback-Driven STEM

Controlled fabrication of nanopores in 2D materials offer the means to create robust membranes needed for ion transport and nanofiltration. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation; however, aberration-corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a sub-Å sized electron beam for atomic manipulation along with atomic resolution imaging. Here, for this work, a method for automated nanopore fabrication is utilized with real-time atomic visualization to enhance the mechanistic understanding of beam-induced transformations. Additionally, an electron beam simulation technique, Electron-Beam Simulator (E-BeamSim) is developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the MXene Ti 3 C 2 T x , the influence of temperature on nanopore fabrication is explored by tracking atomic transformations and find that at room temperature the electron beam irradiation induces random displacement and results in titanium pileups at the nanopore edge, which is confirmed by E-BeamSim. At elevated temperatures, after removal of the surface functional groups and with the increased mobility of atoms results in atomic transformations that lead to the selective removal of atoms layer by layer. This work can lead to the development of defect engineering techniques within functionalized MXene layers and other 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Observable measurement-induced transitions

One of the main postulates of quantum mechanics is that measurements destroy quantum coherence (wave function collapse). Recently it was discovered that in a many-body system dilute local measurements still preserve some coherence across the entire system. As the measurement density is increased, a phase transition occurs that is characterized by the disentanglement of different parts of the system. Unfortunately, this transition is impossible to observe experimentally for macroscopic systems because it requires an exponentially costly full tomography of the many-body wave function or a comparison with the simulation on an oracle classical computer. In this work we report the discovery of another measurement-induced phase transition that can be observed experimentally if quantum dynamics can be reversed. On one side of this phase transition the quantum information encoded in some part of the Hilbert space is fully recovered after the time inversion. On the other side, all quantum information is corrupted. This transition also manifests itself as the change in the behavior of the probability to observe the same measurement outcome in the process that consists of identical blocks repeated many times. In each block the unitary evolution is followed by the measurement. On one side of the transition the probability decreases exponentially with the number of repetitions, on the other it tends to a constant as the number of repetitions is increased. We confirm the existence of the proposed phase transition through numerical simulations of realistic quantum circuits and analytical calculations using an effective random-matrix theory model.

Measurement-induced phase transitions↗
DOE OSTI2024

Unraveling the multi-step crystallization mechanism of polytetrafluoroethylene, modified polytetrafluoroethylene, and their nanocomposites with boron nitride nanobarbs: Experimental insights and theoretical analysis

The non-isothermal crystallization behavior and kinetics of polytetrafluoroethylene (PTFE) composites with boron nitride nanobarb (BNNB), a new generation nanostructure with unique surface morphology and mechanical “barbs” have been analyzed, understanding these properties is essential for their high-end applications as thermal interface materials (TIM) for microwave, 5G and microelectronic devices. The analysis of the crystallization parameters includes crystallization onset, peak and end temperatures, crystallization half-life and overall crystallinity of PTFE, modified PTFE and their BNNB composites. The results were further analyzed using theoretical models such as the combined Avrami-Ozawa model. It was found that BNNB supports crystallization in the modified PTFE but shows minimal effect on the crystallization of PTFE. Due to the limitation of the classical theoretical models used above in fully characterizing the multi-step crystallization process of PTFE, an in-depth analysis using the model-free advanced isoconversional computation was used to characterize the PTFE crystallization based on the evolution of activation energy with fractional crystallinity and for the first time with temperature. Three kinetic regions were identified in the crystallization mechanism. Here, this study investigated the molecular organization and microstructural evolution of PTFE, modified PTFE and their composites during non-isothermal crystallization using advanced X-ray scattering measurements. An insight into the changes undergone by the material's microstructural units including crystallite size and morphology, lamellar thickness and lamellar interfacial layer thickness, and crystallographic phase dynamics during non-isothermal cooling from the melt, was provided here in this work. The effect of copolymer modification of PTFE and the inclusion of pristine and functionalized BNNB (a thermally conductive and electrically insulating ceramic) are both new investigations that provide valuable knowledge for the development of materials with strong matrix-nanofiller interaction and guidance for optimizing sintering and cooling cycles, two key steps in PTFE processing that largely affect the material microstructural features. Overall, the result of the three-part investigation demonstrates that BNNB supports crystallization in the modified PTFE up to 20 wt% concentration and at low and high cooling rates typically used in the industrial processing of PTFE.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Zeta potential measurements of SiO 2 and TiO 2 particles in anionic and cationic surfactant solutions

Zeta potential measurements can provide valuable insights into colloidal stability and may reflect changes in the electrochemical double layer that influence electrocatalysis. In this study, we used zeta potential measurements to monitor and investigate the adsorption behavior of anionic sodium dodecyl sulfate (SDS), cationic dodecyl trimethyl ammonium bromide (DTAB), and cationic cetyl trimethyl ammonium bromide (CTAB) surfactants on SiO 2 and TiO 2 particles in DI water. The zeta potentials of SiO 2 and TiO 2 particles were measured as a function of particle concentration in 1.0 mM, 5.0 mM, and 10.0 mM surfactant solutions. Results indicate that there is no significant adsorption of DS - ions on SiO 2 particles across all SDS concentrations investigated in this study. The zeta potential of TiO 2 particles shifts from negative to positive as particle concentration increases at 1.0 mM SDS, indicating a decrease in DS - ions adsorption per TiO 2 particle. Adsorption plateaus at 5.0 mM SDS for all TiO 2 concentrations studied. In contrast, DTA + and CTA + ions showed clear adsorption on SiO 2 particles, but DTA + exhibited no apparent adsorption on TiO 2 particles. Interestingly, CTA + adsorption on TiO 2 was only evident at higher surfactant concentrations. This study presents a systematic investigation of zeta potential behavior as a function of particle and surfactant concentration, highlighting adsorption saturation and electrostatic reversal. Unlike prior work that was limited to single particle concentration measurements, our approach reveals how surfactant adsorption per particle decreases with increasing surface area. Despite the structural similarity between DTAB and CTAB, this study captured the different adsorption behaviors of both surfactants on SiO 2 and TiO 2 , emphasizing the role of the overall structure of the surfactant on surfactant adsorption behavior. These insights refine the interpretation of zeta potential data and advance understanding of surfactant adsorption on oxide particles. The insight provided in this study about surfactant adsorption on SiO 2 and TiO 2 particles could have important implications for the design of more effective colloidal and catalytic systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li7P3S11

Lithium metal batteries offer superior volumetric and gravimetric specific capacities compared to those based on traditional graphite anodes. Although advancements in solid-state electrolytes address safety concerns, challenges remain, particularly regarding interphase formation in lithium metal anodes. This work presents a computational framework based on high-throughput first-principles density functional theory and machine-learning interatomic potentials (MLIPs) including automated iterative, active learning to enable robust computational exploration of interphase formation between lithium metal anodes and an inorganic solid-state electrolyte. As a demonstration, we apply the framework to a Li/Li7P3S11 interface and find that it accurately identifies the experimentally observed, thermodynamically stable interphase products as well as their overall spatial arrangement within a heterogeneous, amorphous layered structure, with Li2S domains of nanocrystallinity. Our simulations show two stages, a fast and slow diffusion reaction regime, that corroborate the relative phase formation rate of Li x P, Li2S, and Li3P. Using the Onsager transport theory, we capture time-dependent ionic diffusion within the reacting interface, including cross-correlation effects. We found that cross-correlation effects between Li-P and P-S ionic motion significantly influence P-ion diffusion, making it highly sensitive to the local environment and potentially leading to "kinetic trapping" of Li-P phases. The passivation of the interface is shown as the ionic fluxes all approach zero, effectively halting interphase growth.

Diffusion↗
DOE OSTI2025

An improved guess for the variational calculation of charge-transfer excitations in large systems

Ab initio quantum-chemical methods that perform well for computing the electronic ground state are not straightforwardly transferable to electronically excited states, particularly in large molecular systems. Wave function theory offers high accuracy, but is often prohibitively expensive. Methods based on time-dependent density functional theory (TD-DFT) are crucially sensitive to the chosen exchange-correlation functional (XCF) parameterization, and system-specific tuning protocols were therefore proposed to address the method's robustness. Methods based on the variational relaxation of the excited-state electron density showcased promising results for the calculation of charge-transfer excitations, but the complex shape of the electronic hypersurface makes convergence to a specific excited state much more difficult than for the ground state when standard variational techniques are applied. We address the latter aspect by providing suitable initial guesses, which we obtain by two separate constrained algorithms. Combined with the squared-gradient minimization algorithm for all-electrons relaxation in a freeze-and-release scheme (FRZ-SGM), we demonstrate that orbital-optimized density functional theory (OO-DFT) calculations can reliably converge to the charge-transfer states of interest even for large molecular systems. We test the FRZ-SGM method on a phenothiazine-anthraquinone CT excitation in a supramolecular Pd(II) coordination cage complex as a function of the cage conformation. This compound has been studied experimentally prior to our work. We compare this freeze-and-release scheme to two XCF reparameterizations, which were recently proposed as low-cost TD-DFT-based alternatives to variational methods. Two dye-semiconductor complexes, which were previously investigated in the context of photovoltaic applications, serve as a second example to investigate the convergence and stability of the FRZ-SGM approach. Our results demonstrate that FRZ-SGM provides reliable convergence for charge-transfer excited states and avoids variational collapse to lower-lying electronic states, whereas time-dependent DFT calculations with an adequate tuning procedure for the range-separation parameter provide a computationally efficient initial estimate of the corresponding energies, with a computational cost comparable to that of configuration-interaction singles (CIS) calculations.

Bogo, Nicola↗