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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 217 records · Page 12

Q -score as a reliability measure for protein, nucleic acid and small-molecule atomic coordinate models derived from 3DEM maps

Atomic coordinate models are important for the interpretation of 3D maps produced with cryoEM and cryoET (3D electron microscopy; 3DEM). In addition to visual inspection of such maps and models, quantitative metrics can inform about the reliability of the atomic coordinates, in particular how well the model is supported by the experimentally determined 3DEM map. A recently introduced metric, Q-score, was shown to correlate well with the reported resolution of the map for well fitted models. Here, we present new statistical analyses of Q-score based on its application to ∼10 000 maps and models archived in the EMDB (Electron Microscopy Data Bank) and PDB (Protein Data Bank). Further, we introduce two new metrics based on Q-score to represent each map and model relative to all entries in the EMDB and those with similar resolution. We explore through illustrative examples of proteins, nucleic acids and small molecules how Q-scores can indicate whether the atomic coordinates are well fitted to 3DEM maps and also whether some parts of a map may be poorly resolved due to factors such as molecular flexibility, radiation damage and/or conformational heterogeneity. These examples and statistical analyses provide a basis for how Q-scores can be interpreted effectively in order to evaluate 3DEM maps and atomic coordinate models prior to publication and archiving.

B factors↗

Numerical Evaluation of Effective Thermal Conductivity of PCM with Metal Foam Incorporating Buoyancy Effects for Thermal Energy Storage

The thermal energy storage (TES) system has the capability to efficiently preserve thermal energy directly derived from the energy source, minimizing any conversion losses. Especially latent heat storage offers distinct advantages, including a substantial increase in energy storage density and minimization of temperature fluctuations within the plants. However, the phase change material (PCM) employed in latent heat storage has low thermal conductivity. Consequently, various studies are being conducted to enhance heat transfer. One approach to enhance heat transfer involves utilizing metal foam to maximize the heat transfer area. However, modeling metal foam with its intricate structure is a challenging task in numerical analysis. For this reason, ongoing research focuses on simplifying the modeling of metal foam. Nevertheless, fully encompassing all the characteristics of actual metal foam proves to be a challenging task for the simplified analytical model. The objective of this paper is to interpret the simple lattice metal foam analysis model from the perspective of behavior induced by buoyancy. When comparing the analysis results of solid PCM and liquid PCM with the same thermal conductivity under changes in porosity and gravity direction, we conducted an analysis to discern the trends in effective thermal conductivity that are overestimated due to convection. In the analysis, a constant heat flux of 10 kW and a constant surface boundary condition of 350 K were applied, and a sensitivity study regarding the mesh was conducted. The results indicate that, from the perspective of gravity in the simple lattice model, the solid analysis yields an effective thermal conductivity 29-47% higher compared to the liquid analysis. Additionally, as porosity increases, there is an observed increase of 24-33% in effective thermal conductivity.

25 ENERGY STORAGE↗

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗

Data-Driven Kinetic Reaction Networks for Separation Chemistry

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. Here, in this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO 2 (NO 3 ) 2 (DEHiBA) 2 , agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Rare and Experimentally Challenging Supersymmetry Signatures

Supersymmetry has long played a central role in the search for physics beyond the Standard Model at colliders, providing a comprehensive and internally consistent framework for generating well-motivated experimental signatures. For more than 15 years of Large Hadron Collider (LHC) operation, the CMS and ATLAS Collaborations have achieved remarkable sensitivity to a wide range of supersymmetric signatures. Despite this unprecedented reach, no conclusive evidence for supersymmetry has emerged. If supersymmetry is nature's solution to outstanding questions in particle physics, it is necessarily challenging to find. In this article, we review supersymmetric signatures that are particularly rare or otherwise challenging, with a focus on searches at the LHC. We highlight experimental challenges related to detector constraints and analysis difficulties, in addition to model challenges in the interpretation and optimization of searches. Here, we also identify regions of signature space that remain comparatively unconstrained and therefore represent promising targets for future exploration.

Large Hadron Collider↗

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics, and fundamental symmetries. The longstanding goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of 108 Ru, 110 Ru, and 116 Pd were measured to a relative mass precision $\delta$⁢$m/m$ ≈ 10 -8 via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. Further, these mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Towards interpretable Cryo-EM: disentangling latent spaces of molecular conformations

Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.

59 BASIC BIOLOGICAL SCIENCES↗

Search for electroweak production of vector-like leptons in $\tau$-lepton and b-jet final states in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for pair-production of vector-like leptons is presented, considering their decays into a third-generation Standard Model (SM) quark and a vector leptoquark ( U 1 ) as predicted by an ultraviolet-complete extension of the SM, referred to as the ‘4321’ model. Given the assumed decay of U 1 into third-generation SM fermions, the final state can contain multiple τ-leptons and b-quarks. This search is based on a dataset of pp collisions at $\sqrt{s}=13$ TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of up to $140~\textrm{fb}^{-1}$. No significant excess above the SM background prediction is observed, and 95% confidence level limits on the cross-section times branching ratio are derived as a function of the vector-like lepton mass. A lower observed (expected) limit of 910 GeV (970 GeV) is set on the vector-like lepton mass. Additionally, the results are interpreted for a supersymmetric model with an R-parity violating coupling to the third-generation quarks and leptons. Lower observed (expected) limits are obtained on the higgsino mass at 880 GeV (940 GeV) and on the wino mass at 1170 GeV (1170 GeV).

Aad, G. [CPPM, Aix-Marseille Université, CNRS/IN2P↗

Megahertz Rate Optical Diagnostics of Explosively Generated Soot

Detonation of a solid explosive produces a turbulent and luminous post‐detonation fireball containing condensed carbon soot. Diagnostics of soot dynamics are needed for model validation and to interpret emission signals. Diffuse back‐illumination extinction imaging (DBI‐EI) and laser‐induced incandescence (LII) are two common optical diagnostics for flame soot. This work extends both to measure time‐resolved soot dynamics from a 12 mm HMX hemisphere. DBI‐EI measured line‐of‐sight optical extinction exceeds 99% in some regions. Results are tomographically inverted to obtain a signal proportional to the soot volume fraction. Pulse‐burst LII measures are presented at 1 MHz. For the first time, LII results are combined with DBI‐EI extinction measurements to quantify and correct for signal trapping. Following this, spatially and temporally resolved DBI‐EI and LII measures are shown to be in reasonable agreement. Finally, experimental results are compared against recently developed simulations. Quantitative differences in the soot mixing dynamics are resolved. These findings motivate future model improvements and demonstrate ongoing needs for diagnostic advancements for heavily sooting environments.

diffuse back-illumination extinction imaging↗

Modeling a High-Temperature Electrochemically Driven Water-Gas-Shift Process Using a Mixed-Conducting Membrane without External Electrical Power

This paper develops a model to predict and interpret the performance of an elevated-temperature, electrochemical, membrane-assisted, water-gas-shift process. The process uses separated feed streams of H 2 O and CO to produce separated streams of H 2 and CO 2 , without an external electrical power source. The dense ceramic membrane is mixed ionic-electronic-conducting (MIEC) gadolinium-doped ceria (GDC) and the porous composite electrodes are Ni-YSZ. At elevated temperature, GDC conducts both oxygen ions and small polarons. The present process uses chemical potential to drive the process. Electrochemical oxidation of CO proceeds within the composite anode and H 2 O reduction proceeds within the composite cathode. At high temperature (e.g., T > 700 °C), GDC has significant electronic leakage in the form of a reduced-cerium small polaron, which supports the charge-transfer reactions. In a typical electrolyzer or fuel cell, this leakage is significantly problematic. However, the present process depends on the leakage current to complete the electrochemical circuit. Model development and validation is based on measured material properties and reactor performance. Potential applications include using CO-rich blast-furnace off gases in steel processing, producing separated streams of H 2 and CO 2 .

Zhu, Huayang↗

ACES-GNN: can graph neural network learn to explain activity cliffs?

Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

PDF DECODER ANALYSIS CODE

SF-24-038"PDFdecoder", as a new application to explore parametrizations of parton distribution functions (PDFs) of the proton or other hadrons. The PDFs are fundamental quantities in particle physics which are necessary inputs to precise theoretical predictions for experiments at the Large Hadron Collider (LHC) and other facilities. As such, understanding how the PDFs are parametrized and associated uncertainties is a pressing need. The specific problem PDFdecoder confronts is the need of having a tractable and interpretably machine-learning (ML) framework to parametrize the PDFs and their uncertainties so as to understand how a given preferred parametrization is obtained. This problem has not been significantly addressed in the current literature. While other groups have used ML-based approaches to parametrize PDFs in the form of feed-forward neural networks, the question of tractability has not been explored in a PDF context. Our solution makes significant progress in this problem by using an array of encoder-decoder (essentially, autoencoder) architectures with varying constraints to the intermediate latent spaces based on interpretable physics. As a consequence, the trained models can be used as generative networks to produce interpretable predictions for the PDFs in a way that can be refined and studied further.

Hobbs, Timothy↗

Hydrogen adsorption and transport in clay-rich geomaterials: Implications for large-volume underground hydrogen storage

Depleted oil and gas reservoirs, characterized by impermeable clay-rich caprocks, are promising sites for large-scale underground hydrogen storage (UHS), which is a key strategy to support hydrogen-based energy systems. However, experimental data on hydrogen storage in clay-rich geomaterials remain scarce. In this work, we experimentally investigated hydrogen adsorption and migration in clay-rich geomaterials in the presence of nitrogen and water under controlled temperatures. Experimental observations showed that hydrogen was adsorbed in dry illite. A dual-porosity transport model was developed to interpret hydrogen transport between large-pore and small-pore domains in illite. The large-pore domain is the space between clay particles (i.e., inter-particle space), whereas the small-pore domain is the nanoscale pore space between clay mineral layers (i.e., inter-layer or intra-particle space). In contrast, nitrogen showed no evidence of adsorption in dry illite because it cannot move into the small-pore domain due to the relatively large kinetic diameter, referred to as the molecular sieving effect. Here, we found that 0.7–1.3 nm is the length scale regulating this molecular sieving effect, matching the interlayer spacing in illite, suggesting that nitrogen is a promising cushion gas in UHS, which aims to maintain adequate pressure in the reservoir for economic operations. In wetted illite, hydrogen was not adsorbed into the interlayer space due to the occupation of adsorption sites by interlayer water, which highlights the critical role of the clay hydration state in controlling hydrogen-clay interactions. Additionally, hydrogen adsorption experiments on crushed shale indicated that the shale surface possessed adsorption sites more favorable for hydrogen than for nitrogen. Through these experiments, we provide new insights into hydrogen storage mechanisms in clay-rich geomaterials and offer valuable laboratory data for evaluating the performance of large-scale UHS systems.

Adsorption↗

Generative models on phase space

Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be concentrated on a submanifold of the data embedding space. For high-energy physics data, consisting of collections of relativistic energy-momentum 4-vectors, this submanifold can enforce extremely strong physically-motivated priors, such as energy and momentum conservation. If these constraints are learned only approximately, rather than exactly, this can inhibit the interpretability and reliability of such generative models. To remedy this deficiency, we introduce generative models which are, by construction, confined at every step of their sampling trajectory to the manifold of massless N-particle Lorentz-invariant phase space in the center-of-momentum frame. In the case of diffusion models, the "pure noise" forward process endpoint corresponds to the uniform distribution on phase space, which provides a clear starting point from which to identify how correlations among the particles emerge during the reverse (de-noising) process. We demonstrate that our models are able to learn both few-particle and many-particle distributions with various singularity structures, paving the way for future interpretability studies using generative models trained on simulated jet data.

Bogorad, Zachary [Fermilab]↗

Interpreting experimental measurements of helium bubbles using stochastic cluster dynamics models of heterogeneous nucleation and growth in irradiated ferritic alloys

Among a number of other advantageous features, ferritic/martensitic steels are being considered as fusion reactor structural materials due to their low intrinsic swelling under irradiation. However, under high-energy neutron irradiation, He produced through (n, α) reactions stabilizes vacancy clusters, which then act as seeds for bubble formation and growth, negating the intrinsic swelling resistance of these alloys. Standard models of irradiation damage accumulation and microstructural evolution consider homogeneous nucleation as the basis for bubble formation and growth. However, this generally does not explain the large bubble densities and sizes observed experimentally under a number of different conditions. Here, we propose a more realistic physical model of bubble nucleation, formation, and growth designed to capture recent experimental measurements of He-bubble formation and evolution during co-implantation of He and Fe ions in model ferritic alloys. We find that experimental results are explained only when the following three features are considered simultaneously: (i) heterogeneous nucleation of He-vacancy bubbles at defect sinks (e.g., dislocations, grain boundaries, and second-phase precipitates), (ii) nucleation and growth of bubbles via the ‘trap mutation’ mechanism (i.e., spontaneous production of Frenkel pairs due to absorption of He atoms), and (iii) transition from a growth-limited, He-stabilized bubble regime to a ‘runaway’ void-type growth scenario. The model is implemented into a stochastic cluster dynamics framework that takes advantage of cluster size grouping methods to accelerate the simulations, allowing us to reach 10 dpa of simulated irradiated dose, and to capture cluster sizes in excess of 20 nm. Further, a careful extrapolation exercise conducted assuming classical nucleation theory leads to excellent agreement with the experimental measurements at 50 dpa of irradiation.

36 MATERIALS SCIENCE↗

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov–Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and robustness in modeling complex functions. Building on this, we introduce Conformalized-KANs, which integrate conformal prediction, a distribution-free UQ technique, with KAN ensembles to generate calibrated prediction intervals with guaranteed coverage.} Extensive numerical experiments are conducted to evaluate the effectiveness of these methods, focusing particularly on the robustness and accuracy of the prediction intervals under various hyperparameter settings. We show that the conformal KAN predictions can be applied to recent extensions of KANs, including Finite Basis KANs (FBKANs) and multifideilty KANs (MFKANs). The results demonstrate the potential of our approaches to significantly improve the reliability and applicability of KANs in scientific machine learning.

• Artificial intelligence (AI) / machine learning ↗

Reinvestigation of the level structures of the even-even nuclei 90 Zr and 92 Zr

Excited states of 90 Zr and 92 Zr were investigated using the fusion reactions 6 Li + 89 Y and 14 N + 82 Se , respectively. Based on the experimental data, 5 and 18 new 𝛾 rays have been added to the level schemes of 90 Zr and 92 Zr , respectively. The level structures of 90 Zr and 92 Zr have been interpreted with shell-model calculations using the GWBXG and SNET effective interactions. The neutron core-breaking and the importance of the 1⁢𝑔 7/2 and 1⁢ℎ 11/2 orbits for high-spin states of 92 Zr were discussed. Finally, the excitation energy of the neutron core-breaking of nuclei in the 𝐴=90 region was compared.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗