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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

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI

A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. Here, in this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.

Harwood, Thomas V. [USDOE Joint Genome Institute (↗
DOE OSTI2024

Microbial Proxies for Anoxic Microsites Vary with Management and Partially Explain Soil Carbon Concentration

Anoxic microsites are potentially important but unresolved contributors to soil organic carbon (C) storage. How anoxic microsites vary with soil management and the degree to which anoxic microsites contribute to soil C stabilization remain unknown. Sampling from four long-term agricultural experiments in the central United States, we examined how anoxic microsites varied with management (e.g., cultivation, tillage, and manure amendments) and whether anoxic microsites determine soil C concentration in surface (0–15 cm) soils. We used a novel approach to track anaerobe habitat space and, hence, anoxic microsites using DNA copies of anaerobic functional genes over a confined volume of soil. No-till practices inconsistently increased anoxic microsite extent compared to conventionally tilled soils, and within one site organic matter amendments increased anaerobe abundance in no-till soils. Across all long-term tillage trials, uncultivated soils had ~2–4 times more copies of anaerobic functional genes than their cropland counterparts. Finally, anaerobe abundance was positively correlated to soil C concentration. Even when accounting for other soil C protection mechanisms, anaerobe abundance, our proxy for anoxic microsites, explained 41% of the variance and 5% of the unique variance in soil C concentration in cropland soils, making anoxic microsites the strongest management-responsive predictor of soil C concentration. Our results suggest that careful management of anoxic microsites may be a promising strategy to increase soil C storage within agricultural soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Photolytic activation of Ni (II) X 2 L explains how Ni-mediated cross coupling begins

Nickel photocatalysis has recently become vital to organic synthesis, but how the Ni (II) X 2 L pre-catalyst (X = Cl, Br; L = bidentate ligand) becomes activated to Ni (I) XL has remained puzzling and is typically addressed on a case-by-case basis. Here, we reveal a general mechanism where light induces photolysis of the Ni (II) -X bond, either via direct excitation or triplet energy transfer. Photolysis produces Ni (I) XL and a halogen radical, X*. Subsequent hydrogen atom abstraction, often from the solvent, produces a C(sp 3 ) radical, R*, that recombines with Ni (I) to form organonickel(II) complexes, Ni (II) XRL. Rather than acting as a loss pathway, Ni (II) XRL behaves as a light-activated reservoir of Ni (I) via photolysis of the Ni (II) -C bond. These results explain the role of the solvent in protecting the catalyst from off-cycle dimerization, demonstrate that two photons are often required to drive the reaction, and show how tuning the ligand can control the concentration of active Ni (I) species.

08 HYDROGEN↗
DOE OSTI2024

Global burned area increasingly explained by climate change

Fire behaviour is changing in many regions worldwide. However, nonlinear interactions between fire weather, fuel, land use, management and ignitions have impeded formal attribution of global burned area changes. Here, in this work, we demonstrate that climate change increasingly explains regional burned area patterns, using an ensemble of global fire models. The simulations show that climate change increased global burned area by 15.8% (95% confidence interval (CI) [13.1–18.7]) for 2003–2019 and increased the probability of experiencing months with above-average global burned area by 22% (95% CI [18–26]). In contrast, other human forcings contributed to lowering burned area by 19.1% (95% CI [21.9–15.8]) over the same period. Moreover, the contribution of climate change to burned area increased by 0.22% (95% CI [0.22–0.24]) per year globally, with the largest increase in central Australia. Our results highlight the importance of immediate, drastic and sustained GHG emission reductions along with landscape and fire management strategies to stabilize fire impacts on lives, livelihoods and ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Changes in sea ice concentration explain half of the winter warming of the Arctic surface

Arctic winter warming is stronger than in summer, but its driving mechanisms remain debated, particularly the roles of local processes, like sea-ice loss, versus remote factors, like atmospheric heat transport. Here we introduce a novel decomposition framework that characterizes Arctic warming as a function of historical atmospheric circulation, sea ice concentration, and carbon dioxide changes using observational and reanalysis data. We show that sea ice changes explain about 55% of the winter Arctic near-surface temperature trend during 1959–2015, after removing the effects directly connected to atmospheric circulation. Dynamically induced warming accounts for about 20% at surface and up to 80% in mid-troposphere. The remaining ~25% is attributed to the increase in carbon dioxide, though it also indirectly affects sea-ice loss and circulation-related warming. These findings highlight the dominant role of sea ice loss and change in atmospheric dynamics in affecting the historical Arctic winter warming.

Arctic Sea Ice change↗
DOE OSTI2026

Attention-based explainability for structure–property relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

Slautin, Boris [Independent Researcher]↗
DOE OSTI2025

Competition response of cloud supersaturation explains diminished Twomey effect for smoky aerosol in the tropical Atlantic

The Twomey effect brightens clouds by increasing aerosol concentrations, which activates more droplets and decreases cloud supersaturation in response to more competition for water vapor. To quantify this competition response, we used marine low cloud observations in clean and smoky conditions at Ascension Island in the tropical South Atlantic during the Layered Aerosol Smoke Interactions with Cloud (LASIC) campaign. These observations show similar increases in droplet number for increased accumulation-mode particles from surface-based and satellite cloud retrievals, demonstrating the importance of below-cloud aerosol measurements for retrieving aerosol–cloud interactions (ACI) in clean and smoky aerosol conditions. Four methods for estimating cloud supersaturation from aerosol–cloud measurements were compared, with cloud scene-based and parcel-based methods showing sufficient variability for a strong dependence on both aerosol accumulation number concentration and cloud-base updraft velocities. Decomposing aerosol-related changes in cloud albedo and optical depth shows the calculated competition response accounts for dampening the activation response by 12 to 35%, explaining the diminished Twomey effect at high aerosol concentrations observed for smoky conditions at LASIC and previously around the world. This result was consistent for independent supersaturation retrievals by cloud scene-based droplet number and cloud condensation nuclei and parcel-based multimode size-resolving Lagrangian methods. Translating aerosol effects to local radiative forcing with clean conditions as a proxy for preindustrial and smoky conditions for present-day showed that the competition response reduces cooling from the Twomey radiative forcing by 12 to 35%, providing an essential process-specific constraint for improving the representation of aerosol competition in climate model simulation of indirect aerosol forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Observed declines in leaf nitrogen explained by photosynthetic acclimation to CO 2

Widespread evidence of decreasing leaf nutrients has raised concerns about ecosystem productivity under global change. Interpreting trends in leaf nutrients has important implications for the fate of ecosystem services, particularly the role of forests in mitigating climate change and sustaining quality food sources. Here, we challenge the common interpretation that decreasing leaf nitrogen concentration (LNC) is evidence of increasing nutrient limitations on ecosystem primary productivity. Instead, we show that declines in LNC (4% decrease per 50 ppm CO 2 increase), observed across 409 European forest plots over 22 y, can be explained by reduced photosynthetic nitrogen demand. This regional trend is consistent with leaf acclimation to increasing atmospheric CO 2 according to optimality theory. This finding suggests that enhanced photosynthetic nitrogen use efficiency due to CO 2 fertilization may lead to less nitrogen uptake and/or reallocation of nitrogen for plant growth and other functions. Our results have large implications for understanding and simulating interactions between ecosystem nitrogen and carbon cycles and suggest nitrogen requirements for terrestrial carbon uptake under elevated CO 2 may be lower than previously thought.

CO2 fertilization↗
DOE OSTI2025

Explaining the extra crystal-field mode in 𝐴 ⁢Ce ⁢𝑋 2 (𝐴 = K, Rb, Na; 𝑋 = O, S, Se, Te)

A growing list of Ce-based magnets have shown an extra and heretofore unexplained crystal electric field (CEF) mode at high energies. We describe a process whereby an optical phonon can produce a split CEF mode well above the phonon energy. We use density functional theory and point-charge model calculations to estimate the phonon distortions and coupling to model this effect in KCeO 2 , showing that it accounts for the extra CEF mode observed. Furthermore, this mechanism is generic and may explain the extra modes observed on a variety of Ce 3+ compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explaining Snowball-in-Hell Phenomena in Heavy-Ion Collisions Using a Novel Thermodynamic Variable

A loosely bound hadronic molecule produced by a relativistic heavy-ion collision has been described as a “snowball in hell” since it emerges from a hadron resonance gas whose temperature is orders of magnitude larger than the binding energy of the molecule. This remarkable phenomenon can be explained in terms of a novel thermodynamic variable called the “contact” that is conjugate to the binding momentum of the molecule. The production rate of the molecule can be expressed in terms of the contact density at the kinetic freeze-out of the hadron resonance gas. It approaches a nonzero limit as the binding energy goes to 0.

Relativistic heavy-ion collisions↗
DOE OSTI2025

Explainable and Differentiable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators

Operating particle accelerators involves optimizing multiple goals simultaneously, which can be challenging due to trade-offs among objectives. While evolutionary algorithms like the genetic algorithm (GA) have been used for various Multi-Objective Optimization (MOO) tasks, they are not inherently suited for complex control problems. This talk highlights two variations of Reinforcement Learning (RL) for concurrently optimizing heat load and trip rates at the Continuous Electron Beam Accelerator Facility (CEBAF). The problem involves strict constraints on individual states, actions, and overall energy requirements of the beam. First, this talk highlights how differentiability can be harnessed through a Deep Differentiable Reinforcement Learning (DDRL) approach to address MOO issues within particle accelerators. We examine the DDRL method alongside Model Free Reinforcement Learning (MFRL), GA, and Bayesian Optimization (BO). The performance of these methods is assessed by generating a Pareto-front for two objectives. Our findings indicate that DDRL excels in handling high-dimensional problems more effectively than MFRL, BO, and GA. Next, we will show integration of explainable physics-based constraints into RL algorithms to enhance trans- parency and trust in decision-making processes by enabling users to verify that agents adhere to established physical principles. This surrogate function can be modeled using neural networks or sparse dictionary mod- els. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment provided but the surrogate model. In addi- tion, we find that the introduction of a mathematical functional dictionary based surrogate model enables our reinforcement learning algorithms to reliably converge for difficult high-dimensional accelerator controls environments.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

Uncertainty-Based Design: Finite Element and Explainable Machine Learning Modeling of Carbon–Carbon Composites for Ultra-High Temperature Solar Receivers

Design under uncertainty has significantly grown in research developments during the past decade. Additionally, machine learning (ML) and explainable ML (XML) have offered various opportunities to provide reliable predictable models. The current article investigates the use of finite element modeling (FEM), ML and XML predictions, and uncertain-based design of carbon-carbon (C-C) composites for use in ultra-high temperatures. A C-C composite concentrating solar power (CSP) as a microvascular receiver is considered as a case study. These C-C composites are fiber composites with directly integrated carbonized microchannels to form a lightweight, high-absorptivity material that includes an embedded microvascular network of channels. The topology of these microchannels is engineered to optimize heat transfer to a supercritical carbon dioxide (sCO2) heat transfer fluid. The mechanical characterization of C-C composites is highly challenging. Thus, designing every component made of C-C composites for ultra-high temperature applications needs an uncertainty-based analysis. As a part of a comprehensive project on the development of a novel carbonized microvascular C-C composite, this paper explores C-C composite sensitivity analysis, FEM, ML prediction, and XML analysis. The resulting composite can then be carbonized and coated with an oxidation-resistant coating to form a thermally efficient and mechanically robust C-C composite. An ANSYS 3-D-FE model was used to analyze the CSP’s stress/strain. To consider the variability in the mechanical and thermal properties of C-C composites, various mechanical properties are considered as the ANSYS FEM’s input. A synthetic dataset from 730 ANSYS runs was produced to feed into the ML and XML algorithms for uncertainty analysis and prediction. The ML and XML algorithms could accurately predict the CSP stresses/strains.

Daghigh, Vahid (ORCID:0000000298941620)↗
DOE OSTI2025

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry↗
DOE OSTI2025

Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids

Understanding aquatic ecosystem metabolism involves the study of two key processes: carbon fixation via primary production and organic C mineralization as total ecosystem respiration (ERtot). In streams and rivers, ERtot includes respiration in the water column (ERwc) and in the sediments (ERsed). While literature surveys suggest that ERsed is often a dominant contributor to ERtot, recent studies indicate that the relative influence of sediment-associated processes versus water column processes can fluctuate along the river continuum. Still, a comprehensive understanding of the factors contributing to these shifts within basins and across stream orders is needed. Here we contribute to this need by measuring ERwc and collecting water samples across 47 sites in the Yakima River basin, Washington, USA. We found that ERwc rates varied throughout the basin during baseflow conditions, ranging from –7.38 to 0.36 g O2 m?3 d?1, and encompassed the range of ERwc literature values. Additionally, by comparing to ERtot estimates for rivers across the contiguous United States, we suggest that the contribution of ERwc rates to reach-scale ERtot rates across the Yakima River was likely highly variable, but we did not test this directly. We observed that temperature, nutrient concentrations (dissolved organic carbon, total dissolved nitrogen), and total suspended solids explained 41% of ERwc variability across the basin. Our findings highlight the potential relevance of water column processes in aquatic ecosystem metabolism, with the Yakima River basin serving as an environmentally diverse river network representative of the larger Columbia River basin that spans much of the Pacific Northwest region of the United States. Our results are generally congruent with previous work, suggesting that the observed variability and suite of associated environmental factors influencing ERwc are potentially transferable across basins.

Laan, Maggi M.↗
DOE OSTI2025

Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation

Proximal remote sensing has the potential to provide critical information on vegetation biophysical factors that can predict land-atmosphere exchange of water and energy. Latent energy (LE) flux is traditionally estimated using process-based models which rely on vegetation parameters that change during the growing season. Data-driven models have the potential to address these issues by offering flexible predictor selection and more efficient utilization of the information in predictor sets. These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. The results presented here demonstrate that a model using four environmental predictors in combination with two proximal sensing variables can capture 88 % of the variability in LE. ML models using only three predictors (one meteorological and two proximal remote sensing) captured 81 % of LE variability, offering the best trade-off between performance and complexity. An ML model utilizing only two predictors, one proximal remote sensing variable and downwelling radiation, captured 77 % of LE variability. These results demonstrate the power of proximal remote sensing and meteorological observations to estimate land-atmosphere water vapor exchange, providing a solution where more direct methods such as eddy covariance are not available and for evaluations of agronomic management and genotypic variations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

25 ENERGY STORAGE↗
DOE OSTI2025

Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.

Barakati, Kamyar [University of Tennessee, Knoxvil↗