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DOE OSTI2026

Neural entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws

We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this dependency by embedding entropy-stable design principles into the learning process itself, enabling the discovery of physically consistent dynamics in a fully data-driven setting. By jointly learning both the flux function and a corresponding entropy, NESCFN promotes conservation and entropy dissipation, which is critical for long-term stability and fidelity in the system of hyperbolic conservation laws. Furthermore, numerical results demonstrate that the method achieves stability and conservation over extended time horizons and accurately captures shock propagation speeds, even without oracle access to future-time solution profiles in the training data.

Conservative flux form↗

DOE OSTI2025

Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels

Recent breakthroughs in natural language processing show that attention mech- anism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol con- tribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Model- ing (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol-aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding “in-between” samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM’s potential by applying it to the task of demodulating sig- nals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.

Bedir, Oguz↗

DOE OSTI2024

Temporal and spatial characterization of a thermogenic, fault-controlled gas hydrate system, Woolsey Mound, Gulf of Mexico

Woolsey Mound, located at Mississippi Canyon Lease Block 118 (MC118), is the site of the Gulf of Mexico hydrate research consortium’s seafloor observatory, where gas hydrates outcrop at the seafloor. The presence of gas hydrates in the mound is confirmed directly by coring and indirectly by 3D seismic reflection data. Craters, pockmarks, chemosynthetic communities, and authigenic carbonates populate the seafloor at Woolsey Mound. Each crater is characterized by a network of shallow crestal faults that connect the hydrate mound to the underlying allochthonous salt body. We characterize the temporal and spatial evolution of gas hydrates at Woolsey Mound under natural perturbations using four collocated 3D seismic reflection data sets that span over 14 years. Data acquisition differences embedded in the data sets arising from variation in geometry, sample rate, and phase are minimized using the “cross-equalization” method. Our results indicate that hydrate formation and dissociation vary temporally and spatially in close connection to the shallow crestal faults. Evidence of gas hydrate dissociation is observed over a period of three years (2000–2003), where major dissociation occurred along the southern portion of the crestal fault in the southeast crater. The dissociation is less prominent in the southwest crater. Evidence of methane venting is observed between 2000 and 2010, which is mostly concentrated in the southeast crater. The residual amplitude anomalies observed between 2000 and 2014 in the mound are mostly positive, implying that the methane venting had increased significantly. The positive anomalies are correlated with the methane seepage recorded in 2011. Our results indicate the evolution of a fault-controlled gas hydrate system in the northern Gulf of Mexico, which would aid in assessing its impact on the seafloor.

Geochemistry & Geophysics↗

DOE OSTI2026

Influence of Pt-Metal Alloy Catalysts with Various Ionomers on Oxygen Reduction Reaction in Fuel Cell Application

Pt-M/C (M = Co, Ni, Mn, etc.) alloy catalysts exhibit superior oxygen reduction reaction (ORR) activity compared to pure Pt/C, leading to a high energy efficiency in hydrogen fuel cells. However, many Pt-M/C alloy catalysts were synthesized and evaluated at the lab scale in model test-bed systems like rotating disc electrodes, which don't always correlate to performance within a fuel cell system; there is a clear need to evaluate catalysts in electrodes that can be prepared at industrially relevant scales to evaluate how factors like ink formulation can greatly affect device-level of fuel cell performance. Herein, three commercial Pt-M/C alloy catalysts (two Pt-Co/C and one Pt-Ni/C) were comprehensively characterized by various techniques. The results show that the average particle sizes of the three catalysts are close to 5 nm; the atomic ratio of Pt/M is around 4; and the M was successfully embedded into Pt lattice, resulting in the positive shift of Pt 4f in XPS spectra and XRD patterns. These catalytic materials were incorporated into 9 different cathode catalyst layers (CCLs) with three kinds of ionomers (Nafion D2020, high oxygen permeability ionomer (HOPI), and Aquivion D79-25BS), and their performance in proton exchange membrane fuel cells (PEMFCs) were investigated. The results demonstrate that the Pt-Co/C catalysts possess a higher mass activity (MA) than Pt-Ni/C; the cathodes with Nafion ionomer provide the highest MA while electrodes with Aquivion ionomer showed the lowest activity, attributed to poor H+ conductivity resulting from suboptimal ionomer incorporation. Finally, these alloys were shown to exceed DOE targets for MA and H2/Air performance reported in the recent publications at beginning of life and after 90k cycle catalyst AST protocol. This study provides valuable performance benchmarks for these materials guiding future Pt-M/C catalyst design and material integration for heavy duty PEMFC applications.

08 HYDROGEN↗

DOE OSTI2026

Innovating Distributed Embedded Energy Prize (InDEEP): A Lessons Learned Report

The U.S. Department of Energy's Water Power Technologies Office (WPTO) launched the Innovating Distributed Embedded Energy Prize (InDEEP) in March 2023 to accelerate innovation in Distributed Embedded Energy Conversion Technologies (DEEC-Tec) for ocean wave energy. Administered by the National Laboratory of the Rockies (NLR) with technical support from Sandia National Laboratories (SNL), InDEEP focused on the development of small, distributed, and embeddable energy converters (DEECs) and their integration into scalable DEEC-Tec metamaterials for marine renewable energy applications. Spanning three phases over two years, InDEEP awarded approximately $2.3 million to teams from academia, industry, and startups. Phase I emphasized conceptual design. Phase II moved into the prototyping of individual DEECs. Phase III required integration into functional DEEC-Tec metamaterial prototypes. Across 60 submissions, teams explored a wide range of energy conversion mechanisms - including piezoelectric, variable-capacitance, ionic, and inductive methods. Note, the prize did not include the design nor demonstration of ocean wave energy conversion systems. Rather, the prize only required participants to design and demonstrate individual DEECs and corresponding DEEC-Tec metamaterials. This prize utilized a mix of novel and proven techniques to attract participants from outside marine energy, including an engagement leaderboard, robust recruitment, technical expert mentorship, and a suite of technical trainings. Key insights from the competition emphasized that DEEC-Tec metamaterials must be intentionally designed to produce beneficial emergent behaviors - advantages that go beyond simply combining multiple DEEC units. Top-performing teams showed that thoughtful design of system architecture, coordinated deformation, and systems adaptabilities could unlock meaningful performance gains both at the DEEC system level and DEEC-Tec metamaterial system level. A critical realization was that many DEEC-Tec metamaterials could benefit from being designed to accept lower-frequency energy inputs and shift those into higher-frequencies per each DEEC making up the respective DEEC-Tec metamaterial. Other important takeaways included the need for rigorous and quantitative performance testing, effective integration of power conditioning electronics, and the pivotal role of material science in enabling innovative, adaptive DEEC-Tec-based energy conversion designs. InDEEP also helped establish a growing DEEC-Tec community of practitioners, attracting participants from beyond traditional marine energy sectors. Through a strong support infrastructure, InDEEP fostered early-stage innovation and laid a foundation for future DEEC-Tec-based ocean wave energy conversion solutions - positioning DEEC-Tec as a promising pathway toward scalable, resilient ocean wave energy conversion. Through focused R&D of individual DEECs and their integration into DEEC-Tec metamaterials, alongside a growing, multidisciplinary community catalyzed by InDEEP, there is a strong opportunity to drive a disruptive shift in ocean wave energy conversion design and development. This convergence of novel architectures, emergent behaviors, and collaborative innovation positions DEEC-Tec as a transformative approach, moving the field from rigid, centralized energy conversion-based designs to resilient, modular systems highly adaptable for real-world ocean wave energy conversion applications.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Experimental evaluation of the vapor box divertor concept with an open vapor box module in Magnum-PSI

A promising approach to handle the intense plasma heat flux in the divertor region of a tokamak is the Vapor Box Divertor (VBD). Here plasma-lithium interaction creates a dense lithium vapor cloud which interacts with the incoming plasma, effectively shielding the tungsten surface beneath, therefore preventing overheating and sputtering of the tungsten and increasing the component’s lifetime. Two key steps must be addressed to validate this concept: investigating plasma-Li interactions and the transport mechanism of the latter in the presence of plasma. To explore this, a Vapor Box Module (VBM) has been designed for use with the linear plasma generator Magnum-PSI. The VBM consists of a series of three cylindrical boxes, with Li being evaporated at a controlled temperature in the central box (CB). Divertor-like plasma enters the VBM from an upstream aperture, interacts with the Li vapor cloud and exits through a downstream aperture ultimately impacting a target equipped with a calorimetry system. Optical diagnostics Thomson scattering, Filtered fast camera Imaging and optical emission spectroscopy provided information on plasma parameters in terms of electron density n e , temperature T e and plasma composition before and after this interaction. A significant reduction in plasma power was observed upon the establishment of lithium vaporization determined via cooling water calorimetry and embedded thermocouples at the downstream target. This resulted in a drop of the target temperature from ∼800 °C to ∼350 °C (57%) at the highest applied power (11.1 MW m −2 ) and CB temperature (∼700 °C). Lithium condensation at the side boxes of the VBM in combination with strong plasma momentum transfer effectively prevented upstream migration of lithium, while enhancing Li transport toward the target. The achievement of the two main goals of reducing plasma power and confining the Li in the VBM, are consistent with earlier published preliminary SOLPS-ITER simulations. This study shows that the presence of lithium in a VBD-like configuration can result in a strong reduction of power to the target surface, while at the same time the lithium vapor is effectively confined by the VBM, aided by the incoming plasma, preventing its escape from the VBM geometry. This represents a valuable step toward validating the feasibility of the VBD configuration in future fusion reactors.

Magnum-PSI↗

DOE OSTI2025

Management and Operation of the Oak Ridge National Laboratory

ThermaMatrix, Inc provides novel vision inspection solutions for a wide range of manufacturers and industries, providing and implementing the leading technologies for nondestructive inspection (NDI) and material characterization. Many other inspection solutions are either not adequate or are not approachable due to implementation barriers needing expert level operators, excessive inspection time, and high cost. ThermaMatrix’sadvanced vision inspection technology addresses all of these limitations. The Lab Embedded Entrepreneurial Program (LEEP) opportunity by the Department of Energy (DOE) allows small-business start-ups to leverage national laboratory capabilities and skilled scientists to rapidly develop their technology that aligns with DOE goals. ThermaMatrix, Inc.was positioned in the Innovation Crossroads program at Oak Ridge National Laboratory to further develop thenovel Watson Vision Inspection System to support manufacturing quality control efforts. Theresearch goals were (1) explore fundamental parameters that would improve preexisting capabilities, (2) full-scale industrial setup for demonstration, and (3) capability testing and verification. Manufacturing is demanding more NDI implementation to support their quality control needs, which this technology development would support. Figure 1: Ryan Spencer of ThermaMatrix with Watson Vision Inspection System.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Latent heat thermal energy storage performance maps enabling fast & accurate building energy simulations

Thermal energy storage (TES) using phase change materials (PCMs) has gained attention as an effective approach to manage energy demand fluctuations and shift peak building loads. PCM embedded heat exchangers (PCM-HXs) offer high energy storage density and low temperature variation during phase change, being suitable for load-shifting applications. However, this component is typically evaluated using computationally expensive methods, which present significant challenges when the ultimate goal is to assess the performance of PCM-HX integrated thermal energy storage systems in the full building context. In this paper, we present a methodology to generate highly accurate and computationally efficient PCM-HX performance maps which can be easily integrated into building energy simulation tools to analyze the feasibility of space conditioning systems with latent heat PCM-based TES. The performance maps are generated using a computationally efficient PCM-HX simulation tool based on a Generalized Resistance-Capacitance Model (GRCM) which can simulate arbitrary PCM-HXs with high accuracy and significantly less computational effort compared to full CFD simulations. The methodology was verified for a case study considering a 5-ton (~17.5 kW) air-to-water heat pump-thermal energy storage system (HP-TES), which was co-simulated in Modelica for a DOE prototype small-office building in Vienna, Austria, using Spawn of EnergyPlus™. The TES performance maps provided accurate predictions of PCM-HX behavior when used as Modelica component, with deviations within 2-4% while also achieving at least 103 computational time reduction. Leveraging this faster prediction capability, four PCMs with different melting temperatures for cooling (12°C, 16°C) and heating (31°C, 36°C) were assessed to investigate their impact on system performance. This work highlights the importance of robust PCM-HX models for efficient and high-fidelity building-level simulations, presenting new opportunities for advanced control strategy development and parametric analysis of TES configurations in a computationally efficient manner

Modelica Building Simulations↗

DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

DOE OSTI2025

CRADA Final Report: CRADA Number NFE-24-10036 with ThermaMatrix, Inc.

ThermaMatrix, Inc provides novel vision inspection solutions for a wide range of manufacturers and industries, providing and implementing the leading technologies for nondestructive inspection (NDI) and material characterization. Many other inspection solutions are either not adequate or are not approachable due to implementation barriers needing expert level operators, excessive inspection time, and high cost. ThermaMatrix’s advanced vision inspection technology addresses all of these limitations. The Lab Embedded Entrepreneurial Program (LEEP) opportunity by the Department of Energy (DOE) allows small-business start-ups to leverage national laboratory capabilities and skilled scientists to rapidly develop their technology that aligns with DOE goals. ThermaMatrix, Inc. was positioned in the Innovation Crossroads program at Oak Ridge National Laboratory to further develop the novel Watson Vision Inspection System to support manufacturing quality control efforts. The research goals were (1) explore fundamental parameters that would improve preexisting capabilities, (2) full-scale industrial setup for demonstration, and (3) capability testing and verification. Manufacturing is demanding more NDI implementation to support their quality control needs, which this technology development would support.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

DOE OSTI2025

Temperature-dependent calibration procedures for the silicon photomultiplier readout of the cosmic ray veto detector for the Mu2e experiment

The cosmic ray veto detector for the Mu2e experiment consists of scintillation bars embedded with wavelength-shifting fibers and read out by silicon photomultipliers (SiPMs). Here, in this manuscript, the calibration procedures of the SiPMs are described including corrections for the temperature dependence of their light yield. These corrections are needed as the SiPMs are not kept at a constant temperature due to the complexity and cost of implementing a cooling system on such a large detector. Rather, it was decided to monitor the temperature to allow the appropriate corrections to be made. The SiPM temperature dependence has been measured in a dedicated experiment and the calibration procedures were validated with data from production detectors awaiting installation at Fermilab.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗

DOE OSTI2025

Breaking the mold: Overcoming the time constraints of molecular dynamics on general-purpose hardware

The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have, however, failed to provide strong scaling for MD, as scale-out central processing unit (CPU) and graphics processing unit (GPU) platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras wafer scale engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144 M steps/s for 200 000 atoms whose interactions are described by an embedded atom method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out. In this paper, we provide an overview of advances in MD over the last 60 years and present our recent result in the context of historical MD performance trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.

He, Shuaitong↗

DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗

DOE OSTI2025

Real-Time Artificial Intelligence for Particle Reconstruction and Higgs Physics

With the discovery of the Higgs boson at the CERN LHC, the world's highest-energy particle accelerator complex, scientists have acquired an important tool to study the fundamental building blocks of the universe. Precision measurements of Higgs bosons produced with large momentum allow for unique insights into the structure of the interactions of the Higgs boson with other particles that may shed light on physics beyond the standard model. While experimentally challenging, exploring such interactions with novel artificial intelligence (AI) methods can advance our understanding of the Higgs sector, including the Higgs boson's self-interaction. Moreover, the LHC is undergoing a major upgrade to further increase its particle collision rate and thereby operate for an additional decade. The experimental detectors at the upgraded facility must process at least a factor of ten more data at rates of hundreds of terabytes per second all under challenging conditions. New AI techniques are required to reconstruct and select, or trigger on, the most physics-sensitive events in real-time to handle the resulting avalanche of data. The proposed research will achieve the goals of the LHC program at the CMS experiment by developing a sub-microsecond event reconstruction system using real-time AI algorithms that employ field-programmable gate array technologies. By harnessing sophisticated AI techniques, this research focuses on measuring the production of Higgs bosons at large momentum while enhancing particle reconstruction methods in the trigger and beyond. Overall, the proposed research has broader implications for the use of AI in resource-constrained, low-latency embedded applications across all fields of science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗