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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems

Data-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well suited to help us learn about systems with unpredictable evolution or systems with ambiguous governing dynamics given our current understanding. Many plasma problems of interest fall into these categories as there are a wide range of models that exist, however each model is only useful in a certain regime and often limited by computational complexity. To ensure data-driven methods align with theory, they must be consistent and predictable when acting on data whose governing dynamics are known. Weak Sparse Identification of Nonlinear Dynamics (WSINDy) is a recently developed data-driven method that has shown promise in learning governing dynamics from data with high noise levels [1]. This work examines how WSINDy acts on ideal MHD test problems as the initial conditions are varied and specifies limiting requirements for successful equation identification. Furthermore, it is hard to recover the governing dynamics from data that emphasize a single dominant behavior. In these low information cases, Shannon information entropy is able to pick up on the redundancies in the data that affect recoverability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Non-equilibrium entropy production and information dissipation in a non-Markovian quantum dot

This study measures trajectory-level entropy production and information dissipation in a driven, non-Markovian quantum dot using time-resolved optical dynamics and machine-learning-based analysis. Although not a 2D-material system, it is relevant because it demonstrates quantitative extraction of nonequilibrium dynamics from nanoscale optical fluctuations, which is conceptually connected to the proposed studies of transient charge and spin dynamics at interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

Abstract An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Entropy of the Quantum–Classical Interface: A Potential Metric for Security

Hybrid quantum–classical systems are emerging as key platforms in quantum computing, sensing, and communication technologies, but the quantum–classical interface (QCI)—the boundary enabling these systems—introduces unique and largely unexplored security vulnerabilities. This position paper proposes using entropy-based metrics to monitor and enhance security, specifically at the QCI. We present a theoretical security outline that leverages well-established information-theoretic entropy measures, such as Shannon entropy, von Neumann entropy, and quantum relative entropy, to detect anomalous behaviors and potential breaches at the QCI. By linking entropy fluctuations to scenarios of practical relevance—including quantum key distribution, quantum sensing, and hybrid control systems—we promote the potential value and applicability of entropy-based security monitoring. While explicitly acknowledging practical limitations and theoretical assumptions, we argue that entropy-based metrics provide a complementary approach to existing security methods, inviting further empirical studies and theoretical refinements that can strengthen future quantum technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Environmental Controls on Water Vapor Deuterium Excess in the Coastal Boundary Layer: An Information Theory Perspective

We use information theory to quantify the environmental controls on water vapor deuterium excess (D-excess) in coastal Southern California from June 2023 through February 2024. Using Shannon entropy, mutual information (MI), and joint mutual information, metrics that capture both linear and nonlinear relationships, we identify the most informative variables and variable combinations governing D-excess across contrasting marine and continental regimes. Relative humidity with respect to sea surface temperature (RHS) is consistently the strongest individual predictor, explaining up to 27% of D-excess variability during marine conditions but only 10% in continental air masses. The Relative humidity(RHS) + sea surface temperature (SST) combination demonstrates synergistic effects, where their joint influence (explaining up to 36% of D-excess variability) exceeds what either variable achieves individually, confirming their coupled influence on deuterium excess. Wind direction complements RHS most effectively during continental conditions. The best three-variable combination (RHS + SST + Planetary Boundary Layer height) explains 38% of D-excess variability in marine air, while no combination exceeds 20% explanatory power during continental periods. Information theory shows that heteroscedasticity in D-excess relationships indicates regime shifts in controlling processes and quantifies fundamental constraints on predictor variables: some environmental factors like surface pressure or water vapor flux contain insufficient information content to explain D-excess variability regardless of their physical relevance. These results highlight the different predictability limits between marine and continental regimes, challenging the adequacy of linear models and providing a rigorous framework for quantifying the information content of isotope-climate relationships with implications for both modern and paleoclimate applications.

information theory↗

DOE OSTI2025

Entropy-based feature selection for capturing impacts in Earth system models with abrupt forcing

This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in an Earth system model. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO 2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO 2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model based on a 9-day lag had significantly fewer nodes than a graph based on monthly means. Furthermore, this demonstrates our method’s ability to perform dimension reduction while still uncovering source-to-impact pathways.

Changepoint detection↗

DOE OSTI2025

Machine learning informed rational design of high entropy double perovskite oxide universal air/steam electrodes for solid oxide electrochemical cells

Due to their high efficiency and versatility, solid oxide electrochemical cells (SOCs) are poised to play a significant role in future energy conversion and storage applications. In recent years, SOCs have bifurcated into two distinct categories: traditional oxygen-ion conducting SOCs that typically operate from ∼650—850 °C and the more recent proton-conducting ceramic (PCC) SOCs that typically operate from ∼400—650 °C. Current performance and lifetime of both oxygen-ion conducting SOCs and PCCs is primarily limited by the air/steam electrode, which facilitates the oxygen reduction reaction (ORR) during fuel cell operation and must also facilitate the oxygen evolution reaction (OER) during electrolysis operation. Here, we present a newly designed high-entropy double perovskite oxide suitable as a universal ORR/OER electrode for both oxygen-ion conducting SOCs and PCCs. Machine learning methods are applied to identify chemical descriptors for highly catalytic high-entropy double perovskite oxides (AA’B 2 O 6 ) across a large compositional space. Based on the machine-learning guidance, we ultimately converge on Ba 0.9 Cs 0.1 (Ca 0.2 Gd 0.2 La 0.2 Pr 0.2 Sr 0.2 )Co 1.5 Fe 0.5 O 6 (CsBaHEO) as a universal air/steam electrode. Structure stabilization is accomplished by an equimolar five-cation high-entropy composition on the A’-site, while cesium substitution on the A-site enhances the electrical conductivity and leads to a higher oxygen vacancy concentration. This material exhibits versatility and high performance in reversible oxygen-ion SOCs, reversible PCCs, and also large-scale tubular PCCs. For example, the CsBaHEO-based PCC reaches 1018 mW∙cm −2 at 600°C, while a large-scale tubular PCC using CsBaHEO for electrolysis achieves a hydrogen production rate of 21.314 ML∙min −1 at 600 °C.

Cell↗

DOE OSTI2025

FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression

Cross-Silo federated learning systems have been identified as an efficient approach to scaling DNN training across geographically-distributed data silos to preserve the privacy of the training data. Communication efficiency and fairness are two major issues that need to be both satisfied when federated learning systems are deployed in practice. Simultaneously guaranteeing both of them, however, is exceptionally difficult because simply combining communication reduction and fairness optimization approaches often causes non-converged training or drastic accuracy degradation. Here, to bridge this gap, we propose FedEFsz. On the one hand, it integrates the state-of-the-art error-bounded lossy compressor SZ3 into cross-silo federated learning systems to significantly reduce communication traffic during the training. On the other hand, it achieves a high fairness (i.e., rather consistent model accuracy and performance across different clients) through a carefully designed heuristic algorithm that can tune the error-bound of SZ3 for different clients during the training. Extensive experimental results based on a GPU cluster with 65 GPU cards show that FedEFsz improves the fairness across different benchmarks by up to 60.88% and meanwhile reduces the communication traffic by up to 315×.

Cross-Silo Federated Learning Systems↗

DOE OSTI2026

Spatiotemporal structure of edge harmonic oscillation and its role in ELM-free QH-mode at KSTAR

In Quiescent H-mode (QH-mode), edge-localized modes (ELMs) are naturally replaced by a low-n edge harmonic oscillation (EHO), yet the self-regulating transport mechanism driven by the EHO remains insufficiently understood. Using high-spatiotemporal-resolution imaging diagnostics on KSTAR—electron cyclotron emission imaging and broadband electron cyclotron emission—we resolve the eigenmode structure of the EHO and elucidate its regulatory role in edge transport. The EHO is localized within the pedestal near the maximum pressure gradient, propagates in the ion-diamagnetic direction, and its radial envelope expands with increasing shear, suggesting that rotational shear is closely associated with the structural evolution of the EHO. Information-theoretic Transfer Entropy analysis identifies a distinct ‘dual-stabilization’ regulatory interaction pattern: the EHO is associated with enhanced outward energy transport to limit the pressure gradient, while the background shear flow is associated with preferential regulatory influence on the EHO saturation amplitude and energy flux. These results demonstrate that the QH-mode pedestal is sustained by a shear-associated regulation mechanism, where the coupling between rotational shear and mode structure appears closely linked to the sustained ELM-free state.

ECEI↗

DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗

DOE OSTI2025

Entropy-Assisted Quality Pattern Identification in Finance

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.

Physics↗

DOE OSTI2026

Complexity analysis of a CT injection experiment on BRB

In this work, we use Jensen–Shannon complexity and permutation entropy to analyze the magnetic field fluctuations of an astrophysically scaled plasma experiment. The experiment was intended to emulate an interplanetary coronal mass ejection event in the lab, recreating the major sections seen in satellite data. We also use a technique called “delay,” in which we use select elements, skipping one or more data points at a time, in our time series data to obtain Jensen–Shannon complexity as a function of frequency and investigate the frequency of maximized complexity. We then compare the delay frequencies to other frequencies in the plasma. We found that the frequencies for maximum complexity do not correspond to the frequencies investigated, implying that other physical mechanisms lead to an increase in complexity at these frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Structural and compositional complexities of hierarchical self-assembly: A hypergraph approach

Programmable self-assembly enables the construction of complex molecular, supramolecular, and crystalline architectures from well-designed building blocks. In this work, we introduce a hypergraph-based formalism, Blocks & Bonds (B&B), which generalizes classical chemical graph theory by incorporating directed and multicolored interactions, internal symmetries, and hierarchical organization. Within this framework, we develop the Structure Code (SC), a compact and versatile language for describing self-assembled architectures. We define a Kolmogorov-style structural complexity as the total information content of SC, obtained through its tokenization and Shannon information assignment. Complementing this encoding-based measure, we introduce a much simpler quantity, the compositional complexity, which depends only on the number and cumulative usage of block and bond types in the construction set. A central result of this work is a strong empirical correlation between the token-based structural complexity and the compositional complexity across all examined systems. Owing to this agreement, the compositional complexity emerges as the most practical and broadly applicable measure: it is easy to compute, requires no explicit encoding, and yet closely tracks the actual information content of structurally diverse architectures. Applications to molecular systems (ethylene glycol and glucose), DNA-origami lattices, and crystalline assemblies show that B&B hypergraphs provide a unified, scalable, and information-efficient representation of structural organization, naturally capturing symmetry, modularity, and stereochemistry. This framework establishes a quantitative foundation for complexity-aware classification and inverse design of programmable matter.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Living on the edge: a non-perturbative resolution to the negativity of bulk entropies

Lin, Maldacena, Rozenberg, and Shan (LMRS) presented a new information paradox in black hole physics by noticing that the entanglement and Rényi entropies in a two-sided black hole can become negative when the geometry contains a very large number of matter excitations behind the black hole horizon. While originally this puzzle was presented in the context of BPS two-sided black holes in two-dimensional supergravity, the negativity in fact persists for more general two-sided black holes in the presence of a large number of matter excitations. Since the entanglement and Rényi entropies in ordinary quantum systems cannot be negative, resolving this puzzle is a necessary step towards understanding the quantum mechanical description of black holes. In this paper, we explain how to address the entanglement negativity puzzle, both in the original setting discussed by LMRS and in more general non-supersymmetric settings, by summing over all non-perturbative contributions to the gravitational path integral. We then interpret this result from the point of view of a dual matrix integral, which we use to extend our analysis beyond the regime of validity of the genus re-summation performed in the gravitational path integral. In this regime, positivity is rescued by new saddles of the matrix integral, a one-eigenvalue instanton and a two-eigenvalue instanton. Finally, we formulate a similar puzzle and its resolution using random tensor network techniques.

2D Gravity↗

DOE OSTI2025

Living on the edge: a non-perturbative resolution to the negativity of bulk entropies

FOS: Physical sciences↗

DOE OSTI2026

Investigation of Causal Relationships of the Cross‐Scale Wave Coupling Through Information Theoretical Approach

On 2015 October 2, MMS spacecraft observed an electron micro-injection event near the southern hemispheric high-altitude cusp, coinciding with intense wave activity across several frequency bands. Here, we investigated the MMS magnetic field and plasma during this event to explore cross-scale coupling among the wave modes. Employing the Hilbert-Huang transform, we perform an empirical mode decomposition to extract frequencies and amplitudes of the intrinsic mode functions (IMFs). In this analysis, we establish both linear and nonlinear relationships and examine the information transfer between the IMFs. Notably, the transfer entropy suggests that high frequency ion cyclotron waves may be driven by the mirror mode structures. Our case study effectively demonstrates the utility of the information theory based tools for studying cross-scale wave coupling phenomena.

Rivera, Elmer C. [Andrews University, Berrien Spri↗