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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration

Abstract Motivation Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein–protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a “big data” science requiring more efficient analysis tools. Results This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. Availability and implementation ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

Lai, Jhih-Siang (ORCID:0000000156775890)↗
DOE OSTI2026

Tailoring Crosslinks through Time─A Paradigm for Tough Hydrogels

The multidisciplinary applications of hydrogels have motivated a thorough exploration of the relationship between their structural and mechanical properties, i.e., spatial structure–function relationships, with particular regard to strength and toughness. While this approach has driven fundamental advancements in the design of robust hydrogel structures, further complementary perspectives are needed to enable holistic, rational design schemes that integrate considerations such as fabrication and advanced functions like response and adaptation. To these ends, this review focuses on the dynamics of temporal-function relationships and their fundamental bases in order to highlight how the dynamic regulation of polymer interactions programs: 1) polymer assembly and material structure; 2) response to deformation and fracture behavior; 3) dynamic modulation of properties and structural remodeling/self-healing. Here, by exploring this intersection of hydrogel formation, function, and remodeling, this review seeks to shed light on the fundamental relationship between molecular structure, material assembly, and performance in order to connect the emerging area of bioinspired materials processing with tough hydrogel design, and further provides a lasting inspiration and impetus for future hydrogel development that enables valuable scientific and technological advancements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of structural distortions on the correlated electronic structure of orbital-selective Mott insulating Na 3 ⁢Co 2 ⁢SbO 6 under strain

Na 3 Co 2 SbO 6 is a promising candidate to realize the Kitaev spin liquid phase since the large Kitaev spin exchange interaction is tunable via the change in electronic structure, such as the trigonal crystal field splitting (Δ TCF ). Here, in this study, we show that the uncorrelated electronic structure of Na 3 Co 2 SbO 6 is rather insensitive to the strain effect due to the low crystal symmetry accompanied by oxygen displacements and the presence of Sb s orbitals. Using density functional theory plus dynamical mean field theory, we find that the correlated electronic structure of Na 3 Co 2 SbO 6 is an orbital-selective Mott insulating state where the trigonal a 1g orbital is insulating due to the nearly full occupation, while other d orbitals behave as typical Mott insulators, resulting in the effective tunability of Δ TCF under the strain effect. The sign change of Δ TCF can occur as the in-plane tensile strain is applied, and the Kitaev spin liquid phase could possibly be realized due to the strongly suppressed Δ TCF under tensile strain. Our results show that the local Co-site symmetry and dynamical correlation effects will play an important role in engineering the novel magnetic phase in this and related materials.

Nguyen, Nam [Univ. of Illinois, Chicago, IL (Unite↗
DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

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

Molecular basis for human respiratory syncytial virus transcriptional regulator NS1 interactions with MED25

The Mediator complex facilitates interactions between transcription factors and RNA polymerase II, a process that is required for host gene transcription, including in response to viral infections. Among the many subunits in the Mediator complex, the MED25 subunit has been shown to be a target for viral activators during infection. Here we provide the molecular basis for the interaction between human respiratory syncytial virus (hRSV) nonstructural 1 protein (NS1) and the activator interaction domain (ACID) of MED25. The X-ray crystal structure of the complex revealed that NS1 straddles and binds two faces of MED25 ACID. This interaction is distinct from previously known viral activators. Importantly, our data support the conformational flexibility of viral transcriptional regulators. Furthermore, ChIP-seq and RNA-seq analysis identified the ATF3 transcription factor and a role for NS1/Mediator/ATF3 interaction in host gene regulation in hRSV infections. Our findings provide a molecular basis for hRSV NS1-based regulation of host gene transcription and reveal how viruses exploit the conformational heterogeneity at fuzzy transcription activator interfaces.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

De novo design of buttressed loops for sculpting protein functions

In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9–14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.

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

Blocking C-terminal processing of KRAS4b via a direct covalent attack on the CaaX-box cysteine

RAS is the most frequently mutated oncogene in cancer. RAS proteins show high sequence similarities in their G-domains but are significantly different in their C-terminal hypervariable regions (HVR). These regions interact with the cell membrane via lipid anchors that result from posttranslational modifications (PTM) of cysteine residues. KRAS4b is unique as it has only one cysteine that undergoes PTM, C185. Small molecule covalent modification of C185 would block any form of prenylation and subsequently inhibit attachment of KRAS4b to the cell membrane, blocking its biological activity. We translated this concept to the discovery and development of disulfide tethering screen hits into irreversible covalent modifiers of C185. These compounds inhibited proliferation of KRAS4b-driven mouse embryonic fibroblasts, but not cells driven by N-myristoylated KRAS4b that harbor a C185S mutation and are not dependent on C185 prenylation. Top–down proteomics was used to confirm target engagement in cells. These compounds bind in a pocket formed when the HVR folds back between helix 3 and 4 in the G-domain (HVR-α3-α4). This interaction can happen in the absence of small molecules as predicted by molecular dynamics simulations and is stabilized in the presence of C185 binders as confirmed by small-angle X-ray scattering and solution NMR. NOESY-HSQC, an NMR approach that measures internuclear distances of 6 Å or less, and structure analysis identified the critical residues and interactions that define the HVR-α3-α4 pocket. Further development of compounds that bind to this pocket could be the basis of a new approach to targeting KRAS cancers.

C185↗
DOE OSTI2025

Integrated structural model of the palladin–actin complex using XL ‐ MS , docking, NMR , and SAXS

Abstract Palladin is an actin‐binding protein that accelerates actin polymerization and is linked to the metastasis of several types of cancer. Previously, three lysine residues in an immunoglobulin‐like domain of palladin have been identified as essential for actin binding. However, it is still unknown where palladin binds to F‐actin. Evidence that palladin binds to the sides of actin filaments to facilitate branching is supported by our previous study showing that palladin was able to compensate for Arp2/3 in the formation of Listeria actin comet tails. Here, we used chemical crosslinking to covalently link palladin and F‐actin residues based on spatial proximity. Samples were then enzymatically digested, separated by liquid chromatography, and analyzed by tandem mass spectrometry. Peptides containing the crosslinks and specific residues involved were then identified for input to the HADDOCK docking server to model the most likely binding conformation. Small‐angle x‐ray scattering was used to provide further insight into palladin flexibility and the binding interface, and NMR spectra identified potential interactions between palladin's Ig domains. Our final structural model of the F‐actin:palladin complex revealed how palladin interacts with and stabilizes F‐actin at the interface between two actin monomers. Three actin residues that were identified in this study also appear commonly in the actin‐binding interface with other proteins such as myotilin, myosin, and tropomodulin. An accurate structural representation of the complex between palladin and actin extends our understanding of palladin's role in promoting cancer metastasis through the regulation of actin dynamics.

Sargent, Rachel [Department of Chemistry and Bioch↗
DOE OSTI2025

Enabling Strong Neutrino Self-Interaction with an Unparticle Mediator

Recent explorations of the cosmic microwave background and the large-scale structure of the universe have indicated a preference for sizable neutrino self-interactions, much stronger than what the standard model offers. When interpreted in the context of simple particle-physics models with a light, neutrinophilic scalar mediator, some of the hints are already in tension with the combination of terrestrial, astrophysical, and cosmological constraints. We take a novel approach by considering neutrino self-interactions through a mediator with a smooth, continuous spectral density function. We consider Georgi’s unparticle with a mass gap as a concrete example and point out two useful effects for mitigating two leading constraints. (i) The Unparticle is “broadband’—it occupies a wide range of masses which allows it to pass the early universe constraint on effective number of extra neutrinos ( Δ N eff ) even if the mass gap lies below the MeV scale. (ii) Scattering involving unparticles is less resonant, which lifts the constraint set by IceCube based on a recent measurement of ultra-high-energy cosmogenic neutrinos. Our analysis shows that an unparticle mediator can open up ample parameter space for strong neutrino self-interactions of interest to cosmology and serves a well-motivated target for upcoming experiments. Published by the American Physical Society 2025

Foroughi-Abari, Saeid (ORCID:0000000294061896)↗
DOE OSTI2024

Multi-layered heterochromatin interaction as a switch for DIM2-mediated DNA methylation

Functional crosstalk between DNA methylation, histone H3 lysine-9 trimethylation (H3K9me3) and heterochromatin protein 1 (HP1) is essential for proper heterochromatin assembly and genome stability. However, how repressive chromatin cues guide DNA methyltransferases for region-specific DNA methylation remains largely unknown. Here, we report structure-function characterizations of DNA methyltransferase Defective-In-Methylation-2 (DIM2) in Neurospora . The DNA methylation activity of DIM2 requires the presence of both H3K9me3 and HP1. Our structural study reveals a bipartite DIM2-HP1 interaction, leading to a disorder-to-order transition of the DIM2 target-recognition domain that is essential for substrate binding. Furthermore, the structure of DIM2-HP1-H3K9me3-DNA complex reveals a substrate-binding mechanism distinct from that for its mammalian orthologue DNMT1. In addition, the dual recognition of H3K9me3 peptide by the DIM2 RFTS and BAH1 domains allosterically impacts the DIM2-substrate binding, thereby controlling DIM2-mediated DNA methylation. Together, this study uncovers how multiple heterochromatin factors coordinately orchestrate an activity-switching mechanism for region-specific DNA methylation.

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

Nanodomain Formation and Temperature-Dependent Diffusion in Deep Eutectic Solvents Revealed by Single-Molecule Tracking

Deep eutectic solvents (DESs) are typically regarded as homogeneous liquids; however, recent work shows that many exhibit nanoscale structural heterogeneity. Most studies attribute these nanoscale features to short-range chemical interactions. It is still unclear whether a long-range physical mechanism also plays a role. Here, in this study, we examined the nanoscale structure in two hydrophobic DESs, 1:3 tetrabutylammonium bromide: l-menthol (DES-butyl) and 1:3 tetraoctylammonium bromide: l-menthol (DES-octyl). The notation 1:3 represents the molar ratio of the hydrogen bond acceptors to hydrogen bond donors used in the synthesis of the DESs. Single-molecule tracking (SMT) coupled with maximum entropy method (MEM) analysis was used to measure the number of diffusion populations of a dilute concentration of an added fluorescent probe. The presence of more than one population of diffusion coefficients indicates the existence of multiple local environments for the fluorescent probe (i.e., nanoscale structures in the DES). DES-butyl showed a relatively narrow diffusion coefficient distribution centered at 0.55 μm 2 /s, whereas DES-octyl displayed two distinct diffusing populations at 20 °C, with diffusion coefficients of 0.12 μm 2 /s and 0.53 μm 2 /s for the slow and fast populations, respectively. As DES-octyl was heated, the slow-diffusing population steadily diminished and disappeared above ∼30 °C, indicating that the nanodomains present at lower temperatures collapse as the liquid becomes more thermodynamically mixed. This temperature-dependent homogenization is consistent with a physical mechanism of nanostructure formation, for example, liquid–liquid phase separation (LLPS), wherein the structure is not driven solely by specific chemical interactions. The SMT-MEM results suggest that a long-range physical mechanism is the most plausible origin of the measured nanoscale structure in DES-octyl.

Opare-Addo, Jemima [Ames Laboratory (AMES), Ames, ↗
DOE OSTI2024

WFOT: A Wave Function Overlap Tool between Single- and Multi-Reference Electronic Structure Methods for Spectroscopy Simulation

We report the development of a novel diagnostic tool, named wave function overlap tool (WFOT), designed to evaluate the overlap between wave functions computed at single-reference [i.e., time-dependent density functional theory or configuration interaction singles (CIS)] and multireference (i.e., CASSCF/CASPT2) electronic structure levels of theory. It relies on truncating the single- and multireference WFs to CIS-like expansions spanning the same configurational space and maximizing the molecular orbital overlap by means of a unitary transformation. To demonstrate the functionality of the tool, we calculate the transient spectrum of acetylacetone by evaluating excited state absorption signals with multireference quality on top of single-reference on-the-fly dynamics simulations. Semiautomatic spectra generation is facilitated by interfacing the tool with the COBRAMM package, which also allows one to use WFOT with several quantum chemistry codes such as Gaussian, NWChem, and OpenMolcas. Finally, other exciting possibilities for the utilization of the code beyond the simulation of transient absorption spectroscopy are eventually discussed.

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

Improving neutrino oscillation measurements through event classification

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance–information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasielastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE 𝜈 𝜇 disappearance analysis, yields improved accuracy and sensitivity at the 10%–20% level. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements.

Ellis, Sebastian A. R. [King's College, London (Un↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Graph characterization of higher-order structure in atmospheric chemical reaction mechanisms

Atmospheric chemical reactions play an important role in air quality and climate change. While the structure and dynamics of individual chemical reactions are fairly well understood, the emergent properties of the entire atmospheric chemical system, which can involve many different species that participate in many different reactions, are not well described. In this work, we leverage graph-theoretic techniques to characterize patterns of interaction (“motifs”) in three different representations of gas-phase atmospheric chemistry, termed “chemical mechanisms.” These widely used mechanisms, the master chemical mechanism, the GEOS-Chem mechanism, and the Super-Fast mechanism, vary dramatically in scale and application, but they all generally aim to simulate the abundance and variability of chemical species in the atmosphere. This motif analysis quantifies the fundamental patterns of interaction within the mechanisms, which are directly related to their construction. For example, the gas-phase chemistry in the very small Super-Fast mechanism is entirely composed of bimolecular reactions, and its motif distribution matches that of an individual bimolecular reaction well. The larger and more complex mechanisms show emergent motif distributions that differ strongly from any specific reaction type, consistent with their complexity. The proposed motif analysis demonstrates that while these mechanisms all have a similar design goal, their higher-order structure of interactions differs strongly and thus provides a novel set of tools for exploring differences across chemical mechanisms.

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

Transferable dispersion-aware machine learning interatomic potentials for multilayer transition metal dichalcogenide heterostructures

Stacking atomically thin transition metal dichalcogenides (TMDs) into heterostructures enables exploration of exotic quantum phases, particularly through twist-angle-controlled moiré superlattices. These structures exhibit novel electronic and optical behaviors driven by atomic-scale structural reconstruction. However, studying such systems with DFT is computationally demanding due to their large unit cells and van der Waals (vdW) interactions between layers. To address this, we develop a transferable neural network potential (NNP) that includes long-range vdW corrections up to 12Å with minimal overhead. Trained on vdW-corrected DFT data for Mo- and W-based TMDs with S, Se, and Te, the NNP accurately models monolayers, bilayers, heterostructures, and their interaction with h-BN substrates. It reproduces equilibrium structures, energy landscapes, phonon dispersions, and matches experimental atomic reconstructions in twisted WS2 and MoS2/WSe2 systems. We demonstrate that our NNP achieves DFT-level accuracy and high computational efficiency, enabling large-scale simulations of TMD-based moiré superlattices both with and without substrates.

materials for energy and catalysis↗
DOE OSTI2024

Insights into water extraction and aggregation mechanisms of malonamide-alkane mixtures

Structure at the nanoscale in the organic phase of liquid–liquid extraction systems is often tied to separation performance. However, the weak interactions that drive extractant assembly lead to poorly defined structures that are challenging to identify. Here, in this work, we investigate the mechanism of water extraction for a malonamide extractant commonly applied to f-element separations. We measure extractant concentration fluctuations in the organic phase with small angle X-ray scattering (SAXS) before and after contact with water at fine increments of extractant concentration, finding no qualitative changes upon water uptake that might suggest significant nanoscopic reorganization of the solution. The critical composition for maximum fluctuation intensity is consistent with small water–extractant adducts. The extractant concentration dependence of water extraction is consistent with a power law close to unity in the low concentration regime, suggesting the formation of 1 : 1 water–extractant adducts as the primary extraction mechanism at low concentration. At higher extractant concentrations, the power law slope increases slightly, which we find is consistent with activity effects modeled using Flory–Huggins theory without introduction of additional extractant–water species. Molecular dynamics simulations are consistent with these findings. The decrease in interfacial tension with increasing extractant concentration shows a narrow plateau region, but it is not correlated with any change in fluctuation or water extraction trends, further suggesting no supramolecular organization such as reverse micellization. This study suggests that water extraction in this system is particularly simple: it relies on a single mechanism at all extractant concentrations, and only slightly enhances the concentration fluctuations characteristic of the dry binary extractant/diluent mixture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗