Search NASA⌕ Search

SEARCH · Search NASA

Results for “explainability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Thermodynamic Water Activity Explains the Unusual Electrochemical Stability of Aqueous Deep Eutectic Solvents

The presence of water in nonaqueous deep eutectic solvent (DES) electrolytes has been debated in recent years, with efforts ranging from its complete removal to willful addition. It was shown that controlled amounts of water can be beneficial, as it not only enhances the physicochemical properties of these electrolytes but also has no significant detrimental effects on their electrochemical stability. Despite these advantages, there is still limited understanding of how water interacts with DES systems at the molecular level. This study examines the water activity in ethylene glycol and glycerol, as well as their binary mixtures with choline chloride to form the DESs ethaline and glyceline, respectively. In this work, we show that the high electrochemical stability of glyceline is related to its lower water activity compared to ethaline and can be attributed to the robust H-bonding network formed by the three hydroxyl groups of glycerol. Its 3D H-bond network effectively integrates water molecules within its solvent structure, reducing degradation and maintaining stability at higher water contents. The deviations from the ideal Raoult's law behavior are reflected in the water activity and activity coefficients, which highlight the intricate H-bond interactions within DES-water mixtures. Water acts like a lubricant within the more viscous DES mixtures without being detrimental to their electrochemical performance. The presented results emphasize the necessity of customizing DES-water compositions to enhance their performance as electrolytes, especially in flow battery applications where electrochemical stability, ionic conductivity, and fluidity are of utmost importance.

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

Explainable discrepancy checker and diagnosis for digital Twin-based supervisory control system

By virtually representing a physical object and process, a digital twin (DT) enables optimal autonomous operations by combining classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems. A DT’s values depend on how well models estimate quantities of interest and on how uncertainty is handled. Moreover, DTs often combine physics-based and data-driven models with mixed fidelities, where classical uncertainty quantification (UQ) struggles with many sources of uncertainty and real-time constraints. Here, this work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system. The tool is developed using metadata from an automated DT development process to learn correlations between sources of uncertainties and outcomes. During operation, it compares predictions with measurements, attributes discrepancies to dominant sources, and recommends parameter and configuration updates. We verify the workflow on a synthetic temperature-control problem and deploy it on a virtual Thermal Energy Delivery System, reducing mismatch and improving control robustness.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Soil fertility management for sustainable Miscanthus × giganteus production: Increased tiller weight from nitrogen management explains yield gains in aged miscanthus

Aging-related yield decline in Miscanthus × giganteus (miscanthus) remains a major constraint to sustainable biomass production. This study evaluated how nitrogen (N) management and soil fertility influence yield-component traits and productivity in aging miscanthus. Trials were conducted at two sites established in 2008 at the University of Illinois Energy Farm, Urbana, IL. (i) The Sun Grant trial received 0, 60, and 120 kg N ha −1 annually until 2015. Starting 2021, half of each plot received 60 or 120 kg N ha −1 , resulting in six legacy-contemporary treatments: 0N–0N, 0N–120N, 60N–0N, 60N–60N, 120N–0N, 120N–120N. (ii) The Energy Farm trial remained unfertilized until 2014, when one half of each plot received 56 kg N ha −1 , forming two treatments: 0N–0N, 0N–56N. Sun Grant trial results showed N fertilization increased tiller density (tillers m −2 ) and tiller weight (g tiller −1 ) in juvenile to early-mature miscanthus (2011–2015). After N withdrawal, both traits declined (20 % and 40 %), though legacy effects persisted in tiller weight in the aging stands (2020–2023). Contemporary N had little effect on tiller density but increased tiller weight by 34 %–77 %, resulting in 23 %–106 % higher machine-harvested biomass yield in 0–120N, 60-60N, and 120-120N plots. At the Energy Farm trial, 0N–56N plots yielded 59 %–108 % more biomass than 0N–0N. Soil total N increased (Sun Grant: 47 % by 2020; Energy Farm: 58 % by 2023), while Mehlich-3 P (42 %–44 %) and K (21 %–46 %) declined. These findings identify tiller weight as a key determinant of biomass yield in aging miscanthus and highlight the need for P and K management for long-term productivity.

09 BIOMASS FUELS↗
DOE OSTI2025

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr 0.78 Y 0.22 O 3-δ

Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr 0.78 Y 0.22 O 3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Here, our results reproduce the experimentally observed non-monotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration—two mechanisms that influence the lattice expansion in opposite directions. The importance of this work lies in its detailed demonstration of how advanced computational techniques can accurately capture complex environmental effects, providing a valuable framework for modeling similar phenomena in a variety of material systems and applications.

Proton conducting fuel cell↗
DOE OSTI2025

Hard–Soft Acid–Base Theory Explains Photoexcited Carrier Dynamics in Porphyrin/CNT Nanohybrids: Time-Domain Atomistic Analysis

We employ the fundamental chemical concepts of hard− soft acid−base to formulate general principles governing excited-state dynamics in zinc porphyrin (ZnP)/carbon nanotube (CNT) hybrids for energy photoconversion. Atomistic quantum dynamics simulations demonstrate that electron-withdrawing and donating substituents at the ZnP β-pyrrolic position strongly influence the dynamics. ZnP photoexcitation produces subpicosecond electron transfer (ET) from ZnP to CNT, in agreement with the experiment. Substitutions of CN by H and tBu accelerate the ET. The trend is directly related to the hard−soft acid−base concept because the soft−soft interaction between the t Bu- ZnP acid and the mild CNT base enhances the donor−acceptor coupling. Longer coherence and more active vibrational modes facilitate the ET in t Bu-ZnP/CNT. Electron−hole recombination in CN-ZnP/CNT occurs on a hundred picosecond time scale, nicely corroborated by the experiment. The exciton lifetime is extended beyond a nanosecond by the substitutions. The soft−soft interaction in t Bu-ZnP/ CNT increases the splitting between the highest occupied orbitals of the two subsystems, reduces their mixing, and decreases nonadiabatic coupling between the ground and excited states. Rapid decoherence and involvement of low-frequency vibrations favor longer lifetimes. Our investigation reveals that larger p K a of the β-pyrrolic acid gives rapid ET and slow recombination and provides detailed mechanistic information, essential for future optoelectronic applications.

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

(Un)Explained EMIC Waves: Understanding Quiet Time EMIC Wave Drivers

Electromagnetic ion cyclotron (EMIC) waves are known to be generated through cyclotron resonance with the local ion particle population and grow when there is a large enough temperature anisotropy. In general, these temperature anisotropies necessary for wave growth are found to be associated with either solar wind pressure pulses or particle injections during geomagnetic storms or substorms. However, some EMIC events do not show any clear association with these known drivers and appear unexplained. In our analysis of high-amplitude (>1 nT) non-storm time EMIC waves, we find that 24 out of the 223 (~11%) EMIC events with peak amplitude greater than 1 nT were not found to be associated with any clear EMIC wave driver. This raises a compelling question: What magnetospheric or solar wind driver provides the free energy to grow these quiet-time EMIC waves? Here, we examine two EMIC events on 13 April 2017 and 13 February 2014, which were excited during extremely quiet solar wind and geomagnetic conditions. Furthermore, an in-depth analysis of field and particle measurements from multiple data sets, including ground and in situ data for these two events, indicates that extremely weak and otherwise insignificant pressure values and/or very weak substorm injections occurring multiple hours before the event play a significant role in quiet time wave generation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Pivotal Role of Cloud‐Planetary Boundary Layer Coupling to Explain Contrasting Aerosol‐Cloud Relationships

The radiative effect of aerosol on cloud albedo via altering cloud droplet effective radius (r e ) is a major uncertainty in the Earth's climate system. Remote sensing studies have reported either negative or positive relationships between re and aerosol number concentration (N a ) or other aerosol proxies. However, there are much fewer in situ observational evidences and physical explanation remains elusive for the contrasting N a -r e relationships. Here we quantify the N a -r e relationship by using in situ aircraft measurements, together with a re decomposition method. Our analysis reveals that the cloud-planetary boundary layer (PBL) coupling plays a pivotal role on the N a -r e relationship. Quantitative r e decomposition indicates that the contrasting N a -r e relationships in two cloud-PBL coupling regimes result from different balances of four distinct aspects. The widely recognized number effect may be outweighed by the joint effects of the remaining three that have been rarely investigated and largely ignored in N a -r e parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Baseflow Identification via Explainable AI With Kolmogorov‐Arnold Networks

Abstract Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov‐Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We demonstrate KAN's potential on the problem of baseflow identification, a notoriously challenging task plagued by significant uncertainty. KAN‐derived functional dependencies of the baseflow components on the aridity index outperform their original counterparts; they demonstrate that water availability, rather than potential evapotranspiration, drives baseflow by constraining actual evapotranspiration under arid conditions. On a test set, they increase the Nash‐Sutcliffe efficiency (NSE) by 65%, decrease the root mean squared error by 29%, and increase the Kling‐Gupta efficiency by 34%. This superior performance is achieved while reducing the number of fitting parameters from three to two. Next, we use data from 378 catchments across the continental United States to refine the water‐balance equation at the mean‐annual scale. The KAN‐derived equations based on the refined water balance outperform both the current aridity index model, with up to a 105% increase in NSE, and the KAN‐derived equations based on the original water balance. While the performance of our model and tree‐based machine learning methods is similar, KANs offer the advantage of simplicity and transparency and require no specific software or computational tools. This case study focuses on the aridity index formulation, but the approach is flexible and transferable to other hydrological processes. Plain Language Summary Equations used in hydrologic model are often suboptimal, resulting in reduced prediction accuracy and efficiency. We implemented Kolmogorov‐Arnold networks (KAN), a machine learning algorithm for deriving symbolic formulations, to estimate groundwater recharge and showed that it outperforms an existing state‐of‐the‐art semi‐empirical formulation. In hydrology, Nash‐Sutcliffe efficiency (NSE), root mean squared error (RMSE), and Kling‐Gupta efficiency (KGE) are commonly used to evaluate model performance. Higher NSE and KGE values indicate better performance, while lower RMSE values are preferable. Our results show that NSE increased by 71%, RMSE decreased by 32%, and KGE improved by 25%. In addition, KAN identifies an optimal functional form and can be used to derive new analytical formulas using the prior knowledge. The KAN‐inspired equation outperformed the original formulation and reduced the fitting parameters. Furthermore, we refined the water‐balance equation at the mean‐annual scale and showed that, based on the new water‐balance equation, KAN can derive new formulations that are superior to the original aridity index formulations (up to 105% increase in NSE) and KAN‐derived equations based on the original water balance. These findings highlight the significant potential of KAN to advance the scientific understanding of a wide range of hydrologic processes. Key Points Kolmogorov‐Arnold networks (KANs) enhance interpretability of machine‐learned hydrological models KAN‐derived symbolic formulations outperform state‐of‐the‐art semi‐empirical aridity indices KAN‐identified functional form yields an analytical index with fewer fitting parameters and improved performance

baseflow↗
DOE OSTI2024

Sequence-specific dynamic DNA bending explains mitochondrial TFAM’s dual role in DNA packaging and transcription initiation

Abstract Mitochondrial transcription factor A (TFAM) employs DNA bending to package mitochondrial DNA (mtDNA) into nucleoids and recruit mitochondrial RNA polymerase (POLRMT) at specific promoter sites, light strand promoter (LSP) and heavy strand promoter (HSP). Herein, we characterize the conformational dynamics of TFAM on promoter and non-promoter sequences using single-molecule fluorescence resonance energy transfer (smFRET) and single-molecule protein-induced fluorescence enhancement (smPIFE) methods. The DNA-TFAM complexes dynamically transition between partially and fully bent DNA conformational states. The bending/unbending transition rates and bending stability are DNA sequence-dependent—LSP forms the most stable fully bent complex and the non-specific sequence the least, which correlates with the lifetimes and affinities of TFAM with these DNA sequences. By quantifying the dynamic nature of the DNA-TFAM complexes, our study provides insights into how TFAM acts as a multifunctional protein through the DNA bending states to achieve sequence specificity and fidelity in mitochondrial transcription while performing mtDNA packaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Aldehyde cool-flame chemistry explains a missing source of organic acids

Combustion emission is a significant source of organic acids, impacting atmospheric chemistry and climate. Their formation mechanisms, however, remain poorly understood, leading to underestimation in kinetic models. We investigate the cool-flame oxidation of key combustion intermediates—C 1 –C 4 aldehydes and benzaldehyde. Using in-situ synchrotron vacuum ultraviolet photoionization mass spectrometry, we observe the direct conversion of aldehydes to organic acids, a process enhanced by HO 2 radicals. Quantum chemistry calculations reveal that the reaction of RC(O)O 2 with HO 2 on the singlet potential energy surface contributes to organic acids. Incorporating this pathway into a kinetic model significantly improves organic acid prediction. Despite the high-temperature nature of engine combustion, significant spatial and temporal inhomogeneities (e.g., near-wall regions and crevice volumes) lead to localized cool-flame conditions, facilitating organic acid formation and emission. Elucidating the acid formation under cool-flame conditions provides a critical mechanism for accurately modelling anthropogenic organic acid emissions and developing mitigation strategies.

SVUV-PIMS↗
DOE OSTI2026

Can ferric-oxyl excited states explain elongated iron-oxygen bonds in heme peroxidase catalytic intermediates?

The use of X-ray structures to determine and interpret the ferryl iron-oxygen bond order in molecular oxygen-activating heme enzymes has, in the past, been controversial. This has mainly stemmed from the susceptibility of ferryl species to X-ray-induced electronic state changes. In this work we establishe using time-resolved serial femtosecond X-ray crystallography (tr-SFX) on a dye-decolourising peroxidase that the ferryl intermediate species (Compounds I and II) captured following in situ mixing of microcrystals with H 2 O 2 have single, rather than the double bond character expected. X-ray emission validated tr-SFX data with quantum refinement, time-dependent-DFT calculations and QM/MM geometry optimizations together support the concept that the single iron-oxygen bond character is not an indication of ferryl reduction or a protonated form (Fe IV -OH) but is instead attributed to the existence of accessible excited states possessing ferric-oxyl (Fe III –O •– ) character. Such states offer insight into the nature of ferryl heme.

Williams, Lewis J. [University of Essex, Colcheste↗
DOE OSTI2026

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pairwise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through ‘liquid-like’ product layers into a thermodynamic cellular reaction model. In the Ba–Ti–O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion–thermodynamics interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length scales and timescales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.

Atomistic models↗