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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

First Measurement of 87 Rb( α , xn ) Cross Sections at Weak r -process Energies in Supernova ν -driven Ejecta to Investigate Elemental Abundances in Low-metallicity Stars

Observed abundances of Z ∼ 40 elements in metal-poor stars vary from star to star, indicating that the rapid and slow neutron capture processes may not contribute alone to the synthesis of elements beyond iron. The weak r-process was proposed to produce Z ∼ 40 elements in a subset of old stars. Thought to occur in the ν-driven ejecta of a core-collapse supernova, ( α, xn ) reactions would drive the nuclear flow toward heavier masses at T = 2−5 GK. However, current comparisons between modeled and observed yields do not bring satisfactory insights into the stellar environment, mainly due to the uncertainties of the nuclear physics inputs where the dispersion in a given reaction rate often exceeds 1 order of magnitude. Involved rates are calculated with the statistical model where the choice of an α -optical-model potential ( α OMP) leads to such a poor precision. The first experiment on 87 Rb( α, xn ) reactions at weak r -process energies is reported here. Total inclusive cross sections were assessed at E c.m. = 8.1−13 MeV (3.7−7.6 GK) with the active target MUlti-Sampling Ionization Chamber. With an N = 50 seed nucleus, the measured values agree with statistical model estimates using the α OMP Atomki-V2. A reevaluated reaction rate was incorporated into new nucleosynthesis calculations, focusing on ν-driven ejecta conditions known to be sensitive to this specific rate. These conditions were found to fail to reproduce the lighter heavy element abundances in metal-poor stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Scientific Discovery with Physics-Informed System Identification (Abbreviated Report)

My fellowship research focused on making physics-based simulations faster and more useful through machine learning. Many problems in science and engineering are governed by partial differential equations, but high-fidelity simulations are often too expensive to run repeatedly. I worked on improving Latent Space Dynamics Identification (LaSDI), a reduced-order modeling framework that compresses large simulation data sets into a smaller representation and then learns how that representation evolves over time. The motivation was to develop reduced models that remain accurate for more challenging systems, especially when predictions must remain reliable over long time intervals or when the underlying dynamics are more complicated than standard methods can easily handle. I also contributed to related work on Quandary, a high-performance software effort for simulation and control of open quantum systems, before focusing primarily on Latent Space Dynamics Identification methods. The main outcomes of the fellowship were two new algorithms (both of which were published), Rollout-LaSDI and Higher-Order LaSDI, together with supporting work on multi-stage Latent Space Dynamics Identification. Rollout-LaSDI improved long-term prediction by training the model to stay accurate over extended time horizons, and Higher-Order LaSDI broadened the method so it could model systems with higher-order time dynamics. My contributions to multistage Latent Space Dynamics Identification also helped show that its later training stages could be simplified without losing effectiveness, and that this behavior held across different model architectures and training strategies. Taken together, these advances improved the accuracy, flexibility, and practical value of reduced-order modeling tools for computational science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tensor decompositions for count data that leverage stochastic and deterministic optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the maximum likelihood estimator (MLE) of the Poisson CPD model. Here, this work presents two new algorithms that extend state-of-the-art local methods for Poisson CPD. Hybrid GCP-CPAPR combines Generalized Canonical Decomposition (GCP) with stochastic optimization and CP Alternating Poisson Regression (CPAPR), a deterministic algorithm, to increase the probability of converging to the MLE over either method used alone. Restarted CPAPR with SVDrop uses a heuristic based on the singular values of the CPD model unfoldings to identify convergence toward optimizers that are not the MLE and restarts within the feasible domain of the optimization problem, thus reducing overall computational cost when using a multi-start strategy. We provide empirical evidence that indicates our approaches outperform existing methods with respect to converging to the Poisson CPD MLE.

CPAPR↗
DOE OSTI2025

Modeling inter‐reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model

Abstract Background Accurate delineation of the clinical target volume (CTV) is essential in the radiotherapy treatment of soft tissue sarcomas. However, this process is subject to inter‐reader variability due to the need for clinical assessment of risk and extent of potential microscopic spread. This can lead to inconsistencies in treatment planning, potentially impacting treatment outcomes. Most existing automatic CTV delineation methods do not account for this variability and can only generate a single CTV for each case. Purpose This study aims to develop a deep learning‐based technique to generate multiple CTV contours for each case, simulating the inter‐reader variability in the clinical practice. Methods We employed a publicly available dataset consisting of fluorodeoxyglucose positron emission tomography (FDG‐PET), x‐ray computed tomography (CT), and pre‐contrast T1‐weighted magnetic resonance imaging (MRI) scans from 51 patients with soft tissue sarcoma, along with an independent validation set containing five additional patients. An experienced reader drew a contour of the gross tumor volume (GTV) for each patient based on multi‐modality images. Subsequently, two additional readers, together with the first one, were responsible for contouring three CTVs in total based on the GTV. We developed a diffusion model‐based deep learning method that is capable of generating arbitrary number of different and plausible CTVs to mimic the inter‐reader variability in CTV delineation. The proposed model incorporates a separate encoder to extract features from the GTV masks, leveraging the critical role of GTV information in accurate CTV delineation. Results The proposed diffusion model demonstrated superior performance with the highest Dice Index (0.902 compared to values below 0.881 for state‐of‐the‐art models) and the best generalized energy distance (GED) (0.209 compared to values exceeding 0.221 for state‐of‐the‐art models). It also achieved the second‐highest recall and precision metrics among the compared ambiguous image segmentation models. Results from both datasets exhibited consistent trends, reinforcing the reliability of our findings. Additionally, ablation studies exploring different model structures and input configurations highlighted the significance of incorporating prior GTV information for accurate CTV delineation. Conclusions The proposed diffusion model successfully generates multiple plausible CTV contours for soft tissue sarcomas, effectively capturing inter‐reader variability in CTV delineation.

Dong, Yafei [Yale Biomedical Imaging Institute Yal↗
DOE OSTI2025

Progress in modeling hydrogen assisted ammonia oxidation with new experiments and a further reconciliation of the NH 3 + OH rate constant

Hydrogen-assisted oxidation of ammonia in a premixed, laminar flow tubular reactor under reducing conditions was investigated experimentally and through chemical kinetic modeling. Due to its impact on the competition for OH among ammonia and hydrogen, the rate constant for NH 3 + OH (R1) was determined through state-of-the-art theoretical kinetics calculations, employing composite energies that include the effects of higher order electronic excitations on the electronic energies along the variational reaction path and treating the limitations in the kinetics posed by the passage through a hydrogen-bonded complex. The resulting rate constant was in close agreement with the recent experimental value from Zaczek et al. (2025), settling a long-term dispute about the high-temperature value of k 1 and confirming within 20% the value previously used in modeling. The chemical kinetic model, with no other changes, captured well measured concentrations of NH 3 , H 2 , NO, and N 2 O from flow reactor oxidation of NH 3 /H 2 at slightly reducing conditions over a range of temperature (900-1350 K) and NH 3 /H 2 ratios (0.5-2.0). Comparison of the present results with reported data from a non-premixed setup indicates that for laminar flow tubular reactors, the reactor configuration may have implications for the observed H 2 consumption due to the possibility of preferential oxidation during mixing.

Ab initio theory↗
DOE OSTI2024

The quality/cosmology tension for a post-inflation QCD axion

Abstract It is difficult to construct a post-inflation QCD axion model that solves the axion quality problem (and hence the Strong CP problem) without introducing a cosmological disaster. In a post-inflation axion model, the axion field value is randomized during the Peccei-Quinn phase transition, and axion domain walls form at the QCD phase transition. We emphasize that the gauge equivalence of all minima of the axion potential (i.e., domain wall number equals one) is insufficient to solve the cosmological domain wall problem. The axion string on which a domain wall ends must exist as an individual object (as opposed to a multi-string state), and it must be produced in the early universe. These conditions are often not satisfied in concrete models. Post-inflation axion models also face a potential problem from fractionally charged relics; solving this problem often leads to low-energy Landau poles for Standard Model gauge couplings, reintroducing the quality problem. We study several examples, finding that models that solve the quality problem face cosmological problems, and vice versa. This is not a no-go theorem; nonetheless, we argue that it is much more difficult than generally appreciated to find a viable post-inflation QCD axion model. Successful examples may have a nonstandard cosmological history (e.g., multiple types of cosmic axion strings of different tensions), undermining the widespread expectation that the post-inflation QCD axion scenario predicts a unique mass for axion dark matter.

Physics↗
DOE OSTI2025

A network-enabled pipeline for gene discovery and validation in non-model plant species

Identifying key regulators of important genes in non-model crop species is challenging due to limited multi-omics resources. To address this, we introduce the network-enabled gene discovery pipeline NEEDLE, a user-friendly tool that systematically generates coexpression gene network modules, measures gene connectivity, and establishes network hierarchy to pinpoint key transcriptional regulators from dynamic transcriptome datasets. After validating its accuracy with two independent datasets, we applied NEEDLE to identify transcription factors (TFs) regulating the expression of cellulose synthase-like F6 ( CSLF6 ), a crucial cell wall biosynthetic gene, in Brachypodium and sorghum. Our analyses uncover regulators of CSLF6 and also shed light on the evolutionary conservation or divergence of gene regulatory elements among grass species. These results highlight NEEDLE’s capability to provide biologically relevant TF predictions and demonstrate its value for non-model plant species with dynamic transcriptome datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Neutrino and pair creation in reconnection-powered coronae of accreting black holes

A ubiquitous feature of accreting black hole systems is their hard X-ray emission which is thought to be produced through Comptonization of soft photons by electrons and positrons in the vicinity of the black hole, in a region with optical depth of order unity. The origin and composition of this Comptonizing region, known as the corona, is a matter open for debate. In this paper we investigate the role of relativistic protons accelerated in black-hole magnetospheric current sheets for the pair enrichment and neutrino emission of AGN coronae. Our model has two free parameters, namely the proton plasma magnetization σ p , which controls the peak energy of the neutrino spectrum, and the Eddington ratio λ X,Edd (defined as the ratio between X-ray luminosity L X and Eddington luminosity L Edd ), which controls the amount of energy transferred to secondary particles. For sources with λ X,Edd ≳ λ Edd,crit (where λ Edd,crit ∼ 10 -1 for σ p = 10 5 or ∼ 10 -2 for σ p = 10 7 ), proton-photon interactions and γγ annihilation produce enough secondary pairs to achieve Thomson optical depths τ T ∼ 0.1-10. In the opposite case of λ X,Edd ≲ λ Edd,crit , the coronal pairs cannot originate only from hadronic interactions. Additionally, we find that the neutrino luminosity scales as L 2 X /L Edd for λ X,Edd ≲ λ Edd,crit , while it is proportional to L X for higher λ X,Edd values. We apply our model to four Seyfert galaxies, including NGC 1068, and discuss our results in light of recent IceCube observations.

active galactic nuclei↗
DOE OSTI2025

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Thermal Decomposition Kinetics of 4,6‐Diamino‐5,7‐dinitro‐benzo‐furazan

This experimental study investigated the thermal decomposition kinetics of 4,6-diamino-5,7-dinitro-benzo-furazan (referred to as F1 hereafter)—an important decomposition product of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB—a prototypical insensitive high explosive). Simultaneous differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and mass spectrometry (MS) measurements were employed to determine the decomposition kinetics of F1 and to track the evolution of product gases. The DSC profiles were measured at 10 different heating rates between 0.025°C/min and 10°C/min. The measured exotherms were influenced by F1 melting at heating rates above 0.25°C/min, and corresponding changes in decomposition enthalpy and TGA mass-loss-rate profiles indicated a transition from solid-to-gas decomposition to an increasing contribution from liquid-to-gas decomposition. Analysis of low-heating-rate DSC data between 0.025°C/min and 0.17°C/min with the extended Prout–Tompkins model yielded an activation energy of 305 kJ/mol for solid-to-gas F1 decomposition, higher than previous values inferred from TATB decomposition models involving F1. This study provides the first direct experimental determination of the energy barrier for F1 decomposition. MS measurements showed that the major gaseous products matched species previously reported for TATB decomposition (e.g., CO 2 , HCN, C 2 N 2 , etc.), with water identified as the dominant product. Furthermore, these results provide important experimental constraints for improving chemical kinetics models of TATB decomposition and for predicting the reactivity, stability, and safety of TATB-based high explosives under long-term aging conditions and abnormal thermal environments.

4,6-Diamino-5,7-dinitro-benzo-furazan↗
DOE OSTI2026

Microreactor Core Transportation Cask Model Description for Criticality Safety Validation Basis Assessment (Rev. 1)

Criticality safety analyses are completed on transportation casks used for microreactor whole core shipment to provide examples of models and analyses to industry, regulators, and nuclear community at large to be used in verification and validation analyses of similar applications. The microreactors considered are based on a Gas-Cooled Microreactor (GCMR) and a Heat-Pipe Microreactor (HPMR), both utilize HALEU fuel in the form of TRISO particles and various other design options considered in industry microreactor designs. Variant design options of GCMR and HPMR were also investigated to provide a wider application range for each technology. Criticality safety analyses for the GCMR and HPMR packages were performed using the CSAS6 sequence of SCALE 6.3.2 with the ENDF/B-VII.1-based continuous energy neutron libraries. Different scenarios were investigated, including normal operation and water flooded conditions to represent nominal and hypothetical accident scenarios. Sensitivity and similarity analyses are also performed using the TSUNAMI sequence of SCALE 6.3.2, and the similarity analysis uses all the experiments from the ICSBEP Handbook with Intermediate and mixed Enriched Uranium (IEU) and Low Enriched Uranium (LEU) systems, together with additional experiments that are sponsored by the DNCSH program. Many experiments were found marginally similar to the GCMR and HPMR models, with similarity index (ck) values greater than 0.8 but less than 0.9. Among all the experiments analyzed, no case has a ck value greater than 0.9, indicating that additional critical experiments might be needed to further validate the criticality safety models for microreactors transport packages.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The phototrophic purple non‐sulfur bacteria Rhodomicrobium spp. are novel chassis for bioplastic production

Abstract Petroleum‐based plastics levy significant environmental and economic costs that can be alleviated with sustainably sourced, biodegradable, and bio‐based polymers such as polyhydroxyalkanoates (PHAs). However, industrial‐scale production of PHAs faces barriers stemming from insufficient product yields and high costs. To address these challenges, we must look beyond the current suite of microbes for PHA production and investigate non‐model organisms with versatile metabolisms. In that vein, we assessed PHA production by the photosynthetic purple non‐sulfur bacteria (PNSB) Rhodomicrobium vannielii and Rhodomicrobium udaipurense. We show that both species accumulate PHA across photo‐heterotrophic, photo‐hydrogenotrophic, photo‐ferrotrophic, and photo‐electrotrophic growth conditions, with either ammonium chloride (NH 4 Cl) or dinitrogen gas (N 2 ) as nitrogen sources. Our data indicate that nitrogen source plays a significant role in dictating PHA synthesis, with N 2 fixation promoting PHA production during photoheterotrophy and photoelectrotrophy but inhibiting production during photohydrogenotrophy and photoferrotrophy. We observed the highest PHA titres (up to 44.08 mg/L, or 43.61% cell dry weight) when cells were grown photoheterotrophically on sodium butyrate with N 2 , while production was at its lowest during photoelectrotrophy (as low as 0.04 mg/L, or 0.16% cell dry weight). We also find that photohydrogenotrophically grown cells supplemented with NH 4 Cl exhibit the highest electron yields – up to 58.89% – while photoheterotrophy demonstrated the lowest (0.27%–1.39%). Finally, we highlight superior electron conversion and PHA production compared to a related PNSB, Rhodopseudomonas palustris TIE‐1. This study illustrates the value of studying non‐model organisms like Rhodomicrobium for sustainable PHA production and indicates future directions for exploring PNSB metabolisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

HTO and selenate diffusion through compacted Na-, Na–Ca-, and Ca-montmorillonite

Radionuclide transport in smectite clay barrier systems used for nuclear waste disposal is controlled by diffusion, with adsorption significantly retarding transport rates. While a relatively minor component of spent nuclear fuel, 79 Se is a major driver of the safety case for spent fuel disposal due to its long half-life (3.3×10 5 yr) and its low adsorption to clay (K D < 10 L/kg), thus a thorough understanding of Se diffusion through clay is critical for understanding the long-term safety of spent fuel disposal systems. Through-diffusion experiments with tritiated water (HTO, conservative tracer) and Se(VI) were conducted with a well-characterized, purified montmorillonite source clay (SWy-2) under a constant ionic strength (0.1 M) and three different electrolyte compositions: Na + , Ca 2+ , and a Na + -Ca 2+ mixture at pH 6.5 in order to probe the effects of electrolyte composition and interlayer cation composition on clay microstructure, Se(VI) aqueous speciation, and ultimately diffusion. Further, the results were modeled using a reactive transport modeling approach to determine values of porosity (ε), D e (effective diffusion coefficient), and K D (distribution coefficient for adsorption). HTO diffusive flux was higher in Ca-montmorillonite (D e =1.68×10 -10 m 2 s -1 ) compared to Na-montmorillonite (De=7.83×10 -11 m 2 s -1 ). This increase in flux is likely due to a greater degree of clay layer stacking in the presence of Ca 2+ compared to Na + , which leads to larger inter-particle pores. Overall, the Se(VI) flux was much lower than the HTO flux due to anion exclusion, with Se(VI) flux following the order Ca (D e = 1.03×10 -11 m 2 s -1 ) > Na–Ca (D e = 2.12×10 -12 m 2 s -1 ) > Na (D e = 1.28×10 -12 m 2 s -1 ). These differences in Se(VI) flux are due to a combination of factors, including (1) larger accessible porosity in Ca-montmorillonite due to clay layer stacking and smaller electrostatic effects compared to Na-montmorillonite, (2) larger accessible porosity for neutral-charge CaSeO4 species which makes up 32% of aqueous Se(VI) in the pure Ca system, and (3) possibly higher Se(VI) adsorption for Ca-montmorillonite. Through a combination of experimental and modeling work, this study highlights the compounding effects that electrolyte and counterion compositions can have on radionuclide transport through clay. Diffusion models that neglect these effects are not transferable from laboratory experimental conditions to in situ repository conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Missing components in ΛCDM from DESI Y1 baryonic acoustic oscillation measurements: Insights from redshift remapping

We explore transformations of the Friedman-Lemaître-Robertson-Walker (FLRW) metric and cosmological parameters that align with observational data while aiming to gain insights into potential extensions of standard cosmological models. We modified the FLRW metric by introducing a scaling factor, e 2Θ(a) –the cosmological scaling function (CSF), which alters the standard relationship between cosmological redshift and the cosmic scale factor without affecting angular measurements or cosmic microwave background (CMB) anisotropies. Using data from DESI Year 1, Pantheon+ supernovae, and the Planck CMB temperature power spectrum, we constrained both the CSF and cosmological parameters through a Markov chain Monte Carlo approach. Our results indicate that the CSF model fits observational data with a lower Hubble constant (although it is compatible with the value given by Planck 2018 within 1σ) and is predominantly dark matter dominated. Additionally, the CSF model produces temperature and lensing power spectra similar to those predicted by the standard model, though with lower values in the CSF model at large scales. We also checked that when fitting a CSF model without dark energy to the data, we obtain a more negative conformal function. This suggests that the CSF model may offer hints about missing elements and opens up a new avenue for exploring physical interpretations of cosmic acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Semantic Stealth: Crafting Covert Adversarial Patches for Sentiment Classifiers Using Large Language Models

Deep learning models have been shown to be vulnerable to adversarial attacks, in which perturbations to their inputs cause the model to produce incorrect predictions. As opposed to adversarial attacks in computer vision, where small changes introduced to pixel values can drastically alter a model's output while remaining imperceptible to humans, text-based attacks are difficult to conceal due to the discrete nature of tokens. Consequently, unconstrained gradient-based attacks often produce adversarial examples that lack semantic meaning, rendering them detectable through visual inspection or perplexity filters. In contrast to methods that rely on gradient-based optimization in the embedding space, we propose an approach that leverages a Large Language Model's ability to generate grammatically correct and semantically meaningful text to craft adversarial patches that seamlessly blend in with the original input text. These patches can be used to alter the behavior of a target model, such as a text classifier. Since our approach does not rely on gradient backpropagation, it only requires access to the target model's confidence scores, making it a grey-box attack. We demonstrate the feasibility of our approach using open-source LLMs, including Intel's Neural Chat, Llama2, and Mistral-Instruct, to generate adversarial patches capable of altering the predictions of a distilBERT model fine-tuned on the IMDB reviews dataset for sentiment classification.

Roa Carvajal, Maria↗
DOE OSTI2024

Physical, socio-psychological, and behavioural determinants of household energy consumption in the UK

Determining which attitudes and behaviours predict household energy consumption can help accelerate the low-carbon energy transition. Conventional approaches in this domain are limited, often relying on survey methods that produce data on individuals’ motivations and self-reported activities without pairing these with actual energy consumption records, which are particularly hard to collect for large, nationally representative samples. This challenge precludes the development of empirical evidence on which attitudes and behaviours influence patterns of energy consumption, thus limiting the extent to which these can inform energy interventions or conservation programs. This study demonstrates a novel methodology for estimating energy consumption in the absence of actual energy records by using a large, publicly available data set of energy consumption in the UK. We develop a predictive model using the Smart Energy Research Laboratory (SERL) data portal (with records from nearly 13,000 UK households) and then use this model to predict energy consumption (both electric and gas) for a sample of 1,000 UK householders for which we separately collect over 200 variables relating to climate change attitudes and practices. Our approach uses a set of over 50 independent variables that are shared between the data sets, allowing us to train a model on the SERL data and use it to analyse the relationship between energy consumption and the opinions, motivations, and daily practices of survey respondents. Results show that electricity consumption is influenced by a broader range of factors compared to gas. Household energy use is best explained by physical dwelling characteristics, socio-demographic variables, and certain behavioural and attitudinal measures. Notably, pro-environmental attitudes, frugality, and conscientiousness correlate with lower energy use, while income and consumerism are linked to higher consumption. We discuss how these findings can inform efforts to decarbonise home energy use in the UK.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Generating synthetic signaling networks for in silico modeling studies

Predictive models of signaling pathways have proven to be difficult to develop. Reasons include the uncertainty in the number of species, the complexity in species’ interactions, and the sparseness and uncertainty in experimental data. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. For example, uncertainty and sparseness of the data often result in overfitted models that have little predictive value beyond recapitulating the experimental data itself. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. However, to determine the effectiveness of any new algorithm, a baseline model is needed to test its performance. To meet this need, we developed a method for generating artificial synthetic networks that are reasonably realistic and thus can be treated as ground truth models. These synthetic models can then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. Here, we describe a simple approach for generating synthetic signaling networks that can be used for this purpose.

42 ENGINEERING↗
DOE OSTI2025

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗