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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 OSTI2024

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Computer Science↗
DOE OSTI2026

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li + , by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH – dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li + -coupled electron transfer along the PAN backbone, causing ∼10 4 times faster sequential ring-formation of the remaining nitriles. PAN’s extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

Chahal-Crockett, Rajni [Oak Ridge National Laborat↗
DOE OSTI2026

Aragonite saturation horizon variability along North Pacific seamounts and implications for deep-sea coral reefs

The 2014 discovery of living deep-sea coral reefs along the Northwest Hawaiian Islands (NWHI) and lower Emperor Seamount Chain (ESC), despite the North Pacific’s shallow aragonite saturation horizon (ASH) and high CaCO3 dissolution rates, underscores the need to understand the local seawater chemistry that allows these reefs to persist. We investigated seawater carbonate chemistry using discrete samples along the NWHI and ESC from two cruises ~1 year apart (08/26/21 – 09/26/21, 09/09/22 – 10/24/22). Across the two cruises, ASH depth difference ranged from 15 to 77 m. Since the Pacific ASH shoals by 1–2 m yr?¹, this long-term trend cannot explain the magnitude of ASH change observed between cruises. Temperature-salinity plots similarly indicate no major shifts in intermediate water masses that could account for these changes. Instead, ASH depth variability was primarily governed by localized biogeochemical processes, namely changes in intermediate water respiration and CaCO3 dissolution. Indicators for dissolution (TA*) and respiration (AOU) suggest changes in ASH depth were driven by changes in dissolution at the northern- and southern-most sites, whereas respiration exerted stronger control at central sites. Combining 2021 and 2022 data with data from 2014 – 2019 (excluding 2018) revealed high interannual ASH variability, by as much as >200 m at one site. Deep-sea coral reefs across the NWHI and ESC currently reside close to the ASH depth and likely experience seasonal and interannual shifts between under- and supersaturation. As ocean acidification progresses however, persistent exposure to undersaturated seawater could further threaten these critical deep-sea ecosystems.

coral reefs↗
DOE OSTI2023

High‐Entropy Lithium Argyrodite Solid Electrolytes Enabling Stable All‐Solid‐State Batteries

Abstract Superionic solid electrolytes (SEs) are essential for bulk‐type solid‐state battery (SSB) applications. Multicomponent SEs are recently attracting attention for their favorable charge‐transport properties, however a thorough understanding of how configurational entropy (ΔS conf ) affects ionic conductivity is lacking. Here, we successfully synthesized a series of halogen‐rich lithium argyrodites with the general formula Li 5.5 PS 4.5 Cl x Br 1.5‐x (0≤x≤1.5). Using neutron powder diffraction and 31 P magic‐angle spinning nuclear magnetic resonance spectroscopy, the S 2− /Cl − /Br − occupancy on the anion sublattice was quantitatively analyzed. We show that disorder positively affects Li‐ion dynamics, leading to a room‐temperature ionic conductivity of 22.7 mS cm −1 (9.6 mS cm −1 in cold‐pressed state) for Li 5.5 PS 4.5 Cl 0.8 Br 0.7 (ΔS conf =1.98R). To the best of our knowledge, this is the first experimental evidence that configurational entropy of the anion sublattice correlates with ion mobility. Our results indicate the possibility of improving ionic conductivity in ceramic ion conductors by tailoring the degree of compositional complexity. Moreover, the Li 5.5 PS 4.5 Cl 0.8 Br 0.7 SE allowed for stable cycling of single‐crystal LiNi 0.9 Co 0.06 Mn 0.04 O 2 (s‐NCM90) composite cathodes in SSB cells, emphasizing that dual‐substituted lithium argyrodites hold great promise in enabling high‐performance electrochemical energy storage.

Chemistry↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
DOE OSTI2025

Detection of visible-wavelength aurora on Mars

Mars hosts various auroral processes despite the planet’s tenuous atmosphere and lack of a global magnetic field. To date, all aurora observations have been at ultraviolet wavelengths from orbit. We describe the discovery of green visible-wavelength aurora, originating from the atomic oxygen line at 557.7 nanometers, detected with the SuperCam and Mastcam-Z instruments on the Mars 2020 Perseverance rover. Near–real-time simulations of a Mars-directed coronal mass ejection (CME) provided sufficient lead-time to schedule an observation with the rover. The emission was observed 3 days after the CME eruption, suggesting that the aurora was induced by particles accelerated by the moving shock front. To our knowledge, detection of aurora from a planetary surface other than Earth has never been reported, nor has visible aurora been observed at Mars. This detection demonstrates that auroral forecasting at Mars is possible, and that during events with higher particle precipitation, or under less dusty atmospheric conditions, aurorae will be visible to future astronauts.

Science & Technology - Other Topics↗
DOE OSTI2026

Adaptation of virtual synchronous generators to dynamic conditions in power grids

Virtual synchronous generators (VSGs) are widely adopted as grid-forming controls for inverter-based resources. However, when grid conditions vary significantly as characterized by changes in short-circuit ratio (SCR) and the reactance-to-resistance (X/R) ratio, fixed-gain designs and the commonly used P–Q decoupling assumption can become inaccurate. Such conditions can degrade transient power performance, leading to oscillations, prolonged settling, and overshoot, particularly in stiff-grid operating points. This paper quantifies how grid strength and impedance-dependent coupling affect the active–reactive power dynamics of a conventional VSG over a broad range of SCR and X/R values. An adaptive VSG tuning framework is then developed by combining (i) a coupling-explicit, impedance-parameterized state-space model to enable systematic controller synthesis, (ii) a full-state-feedback law designed via pole placement to meet prescribed damping and settling-time specifications, and (iii) a physics-informed neural network (PINN)–based online grid-impedance estimator that updates controller gains in real time as grid conditions vary. Offline simulations in MATLAB/Simulink and real-time validation on an OPAL-RT platform show that the proposed method preserves consistent damping and settling behavior with reduced overshoot across wide SCR and X/R ranges, compared with fixed-gain VSG baselines.

Adaptive control↗
NASA NTRS2026

Recirculating and Sub-Atmospheric Rapid Cycle Amine Swing Bed Testing at Various Metabolic Profiles, Temperatures, and Half-Cycle Times

Sixty years since the first spacewalk, NASA’s Extravehicular Mobility Unit (xEMU) exploration space suit has undergone numerous design improvements and iterations. As commercial manufacturers develop next-generation systems to support increasingly complex missions, including Moon-to-Mars architecture prototyping and the design of the Martian Portable Life Support System (PLSS), material selection and individual subunit testing under realistic metabolic and pressure conditions are paramount. To determine whether a reusable carbon dioxide (CO 2 ) scrubber design and adsorbent can be utilized in new suit configurations, an all-encompassing test rig is required to compare existing and newly developed technologies. The test rig must be capable of sub-atmospheric testing, calibrated humid CO 2 dosing, and facilitation of the evaluation of sorbents across a wide range of operational requirements. Recently, XploSafe has developed an Extravehicular Mobility Unit (xEMU) testing rig specifically designed to explore CO 2 and humidity control materials for spacesuits. This closed-loop recirculating swing bed (Rapid Cycle Amine Test Rig) was utilized to compare adsorbent utility across a wide range of xEMU operating conditions. Following successful testing with Xplo-SA9T over multiple simulated eight-hour Extravehicular Activity (EVA) tests, improvements to the test stand were considered. Regeneration vacuum supply within the test rig was updated to more closely resemble space vacuum, theoretically increasing half-cycle timing. An improved water vapor delivery system was incorporated to dose a wider calibrated range of humidity over both short and long-duration EVAs. A range of metabolic profiles between 800 and 3000 BTU/h were tested, and the corresponding average CO 2 steady-states were compared. The effect of dosing temperature at the adsorbent bed inlet between 10 and 40 ̊C and the corresponding CO 2 removal efficiency was also evaluated.

John R Tidwell↗
NASA NTRS2025

JWST Reveals Spectral Tracers of Recent Surface Modification on Europa

Europa has been modified by a variety of geologic processes, exposing internally-derived materials that are heavily irradiated by charged particles trapped in Jupiter’s magnetosphere. Prior spectral analysis of H2O ice on Europa relied on low signal-to-noise data at wavelengths >2.5 µm, limiting assessment of a 3.1 µm Fresnel peak that is diagnostic of exposed crystalline ice. We report new measurements of H2O ice spectral features using high signal-to-noise data collected by the NIRSpec spectrograph (1.48 – 5.35 µm) on the James Webb Space Telescope. These data reveal a narrow 3.1 µm crystalline H2O ice Fresnel peak, which is primarily located at southern latitudes in Tara and Powys Regiones. Our analysis indicates that crystalline ice exposed in these low-latitude regiones is likely sustained by ongoing thermal (re)crystallization outpacing charged particle amorphization of the top ∼10 µm of Europa’s regolith over short timescales (<15 days). We also measured H2O ice features centered near 1.5 µm, 1.65 µm, and 2.0 µm, and a broad 3.6 µm H2O continuum peak, which are all stronger at northern latitudes, in contrast to the 3.1 µm Fresnel peak identified at southern latitudes. These results support the hypothesis that H2O ice in Europa’s regolith is vertically stratified, with amorphous ice grains dominating its exposed surface, except in Tara and Powys Regiones. We also find that a previously detected 4.38 µm 13CO2 feature is present almost exclusively at southern latitudes in Tara and Powys Regiones, likely derived from an internal source of carbon-bearing material.

Surface processes (2116)↗
NASA NTRS2013

Colors of Jupiter's large anticyclones and the interaction of a Tropical Red Oval with the Great Red Spot in 2008

The nature and mechanisms producing the chromophore agents that provide color to the upper clouds and hazes of the atmospheres of the giant planets are largely unknown. In recent times, the changes in red coloration that have occurred in large- and medium-scale Jovian anticyclones have been particularly interesting. In late June and early July 2008, a particularly color-intense red tropical oval interacted with the Great Red Spot (GRS) leading to the destruction of the red tropical oval and cloud dispersion. We present a detailed study of the tropical vortices, usually white but sometimes red, and a characterization of their color spectral signatures and dynamics. From the spectral reflectivity in methane bands we study their vertical cloud structure compared to that of the GRS and BA. Using two spectral indices we found a near-correlation between anticyclones cloud top altitudes and red color. We present detailed observations of the interaction of the red oval with the GRS and model simulations of the phenomena that allow us to constrain the relative vertical extent of the vortices. We conclude that the vertical cloud structure, vertical extent and dynamics of Jovian anticyclones are not the causes of their coloration. We propose that the red chromophore forms when background material (a compound or particles) is entrained by the vortex, transforming into red once inside the vortex due to internal conditions, exposure to ultraviolet radiation or to the mixing of two chemical compounds that react inside the vortex, confined by a potential vorticity ring barrier.

red color↗
NASA NTRS2025

Sulfur outgassing and in-gassing in lunar orange glass beads and implications for 33S “Anomaly” in the Moon

Our recent investigations have discovered inward diffusion (in-gassing) of moderately volatile elements (MVEs; e.g., Na, K and Cu) from volcanic gas into volcanic beads/droplets. In this work, we examine the distribution of sulfur in lunar orange glass beads. Our analyses reveal that sulfur exhibits a non-uniform distribution across the beads, forming “U” or “W” shaped profiles typical of in-gassing. A model developed to assess sulfur contributions from different sources (original magmatic sulfur versus atmospheric in-gassed sulfur) in the orange beads indicates that atmospheric sulfur in-gassed during eruption contributes approximately 9–24 % to the total sulfur content of an orange bead, averaging around 16 %. This in-gassed sulfur is derived from the eruption plume, where atmospheric sulfur could undergo photochemical reactions induced by UV light, leading to mass independent fractionation and a distinct sulfur isotope signature. Interestingly, a recent study discovered a small mass independent isotope fractionation of sulfur in lunar orange glass beads in drive tube 74002/1 and a lack of such mass independent isotope fractionation in black glass beads in the same lunar sample. This finding contrasts with sulfur in lunar basalts, which typically exhibit mass dependent fractionation. With our work, the observed mass independent fractionation signal in sulfur isotopes of orange beads can be attributed to the in-gassing of photolytic sulfur in the optically thin part of the eruption plume where UV light can penetrate. Using the sulfur isotope data of lunar orange beads, we estimate that the Δ33S value of atmospheric sulfur is approximately −0.18 ‰. Our study provides new insights into the complex dynamics of volatile elements in lunar volcanic processes, highlighting the role of in-gassing in shaping sulfur isotope signatures in volcanic glass beads.

Xue Su↗
DOE OSTI2025

Proximal remote sensing: an essential tool for bridging the gap between high‐resolution ecosystem monitoring and global ecology

Summary A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or ‘proximal’ remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site‐level eddy‐covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high‐spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions. We provide current best practices for data availability and metadata for proximal remote sensing: spectral reflectance, solar‐induced fluorescence, thermal infrared radiation, microwave backscatter, and LiDAR. Our paper outlines the steps necessary for making these data streams more widespread, accessible, interoperable, and information‐rich, enabling us to address key ecological questions unanswerable from space‐based observations alone and, ultimately, to demonstrate the feasibility of these technologies to address critical questions in local and global ecology.

Plant Sciences↗
NASA NTRS2026

Multi-Agent Swarm State of the Art Report

The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.

agent↗
DOE OSTI2026

Dressed-State Hamiltonian Engineering in a Strongly Interacting Solid-State Spin Ensemble

In quantum science applications, ranging from many-body physics to quantum metrology, dipolar interactions in spin ensembles are often controlled via Floquet engineering. However, this technique typically reduces the interaction strength between spins and effectively weakens the coupling to a target sensing field, limiting the metrological sensitivity. In this Letter, we develop and demonstrate an alternative method that directly tunes the native dipolar interaction in an ensemble of nitrogen-vacancy (NV) centers in diamond, thereby overcoming these limitations inherent to Floquet engineering. Our approach utilizes dressed-state qubit encoding under a bias magnetic field applied perpendicular to the crystal lattice orientation. This method leads to a 3.2× enhancement of the dimensionless coherence parameter JT 2 compared to state-of-the-art Floquet engineering and a 2.6× (8.3 dB) enhanced sensitivity in ac magnetometry. Furthermore, our results provide a powerful Hamiltonian engineering tool for future studies with NV ensembles and other interacting higher-spin (S > $\frac{1}{2}$) systems.

Quantum control↗
DOE OSTI2026

SimH 2 : an integrated techno-economic modeling framework for hydrogen pipeline infrastructure and network optimization

Large-scale hydrogen (H 2 ) pipeline transport design and network optimization have seldom been reported due to the lack of a cost model accounting for the relationship between transport cost and hydrogen mass flow rate. Here, this work introduced a system-level cost model for hydrogen pipeline transport at supercritical state and integrated it with an existing CO 2 pipeline network tool, SimCCS, for hydrogen-specific pipeline design and optimization. The Intermountain West (I-West) region of the U.S., historically dependent on fossil fuel-based economies, is chosen to demonstrate the capabilities of our H 2 pipeline cost model and transport network optimization platform called SimH 2 . Two scenarios are examined: one where the pipeline is not allowed to pass through disadvantaged communities and the other where it is permitted. The results highlight that incorporating disadvantaged-community constraints lead to longer pipeline routes and increased transport costs, reflecting the trade-offs involved in equitable infrastructure development. It is demonstrated that the newly developed SimH 2 tool not only enables the efficient design of H 2 transportation pipelines but also optimizes the network by accounting for local terrain and the presence of disadvantaged areas.

08 HYDROGEN↗
DOE OSTI2025

Photosynthetic capacity is reduced by warming but unaffected by elevated CO2 in seedlings of five boreal tree species

Abstract Increasing atmospheric CO2 concentrations fuel global warming, with boreal regions warming at a faster rate than many other areas. Boreal forests are an important component of the global carbon cycle, yet we have little data on photosynthetic responses of boreal trees to elevated CO2 (EC) and warming. We grew seedlings of 5 widespread North American boreal tree species (from Betula, Larix, Picea, and Pinus) under current (410 ppm) or elevated (750 ppm) CO2 and either ambient (+0 °C) or increased (+4 °C or +8 °C) temperature, then measured photosynthetic traits over a range of leaf temperatures. Our results were generally consistent across species: photosynthetic capacity (maximum rates of Rubisco carboxylation, Vcmax, and electron transport, Jmax) was unaffected by EC but decreased under +8 °C warming. Accordingly, net photosynthesis measured at the growth CO2 concentration (Agrowth) was reduced under warming and increased under EC. The thermal optimum for Agrowth (ToptA) increased by ∼1.8 °C with EC but increased with warming in only two species. In contrast, the activation energies and thermal optima for Vcmax and Jmax, which are used to estimate photosynthesis in Earth System Models, were unaffected by growth environment. There were a few interactions between growth, CO2, and warming. These results suggest increased photosynthesis of widespread boreal tree species under EC may be offset by future reductions in photosynthetic capacity related to warming. We also show that the temperature sensitivities of parameters used to estimate global photosynthesis in large-scale models are generally unaffected by simulated climate change in these species.

Plant Sciences↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗