A hybrid method for power system state estimation using Cellular Computational Network
Not Available
Rahman, Md. Ashfaqur↗
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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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Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks
Not provided.
Automation & Control Systems↗
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
Not provided.
Automation & Control Systems↗
Δ -PINNs: Physics-informed neural networks on complex geometries
Not provided.
Automation & Control Systems↗
Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2
The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.
25 ENERGY STORAGE↗
Patient-Specific Quality Assurance for the Delivery of {sup 60}Co Intensity Modulated Radiation Therapy Subject to a 0.35-T Lateral Magnetic Field
Purpose: This work describes a patient-specific dosimetry quality assurance (QA) program for intensity modulated radiation therapy (IMRT) using ViewRay, the first commercial magnetic resonance imaging-guided RT device. Methods and Materials: The program consisted of: (1) a 1-dimensional multipoint ionization chamber measurement using a customized 15-cm{sup 3} cube-shaped phantom; (2) 2-dimensional (2D) radiographic film measurement using a 30- × 30- × 20-cm{sup 3} phantom with multiple inserted ionization chambers; (3) quasi-3D diode array (ArcCHECK) measurement with a centrally inserted ionization chamber; (4) 2D fluence verification using machine delivery log files; and (5) 3D Monte Carlo (MC) dose reconstruction with machine delivery files and phantom CT. Results: Ionization chamber measurements agreed well with treatment planning system (TPS)-computed doses in all phantom geometries where the mean ± SD difference was 0.0% ± 1.3% (n=102; range, −3.0%-2.9%). Film measurements also showed excellent agreement with the TPS-computed 2D dose distributions where the mean passing rate using 3% relative/3 mm gamma criteria was 94.6% ± 3.4% (n=30; range, 87.4%-100%). For ArcCHECK measurements, the mean ± SD passing rate using 3% relative/3 mm gamma criteria was 98.9% ± 1.1% (n=34; range, 95.8%-100%). 2D fluence maps with a resolution of 1 × 1 mm{sup 2} showed 100% passing rates for all plan deliveries (n=34). The MC reconstructed doses to the phantom agreed well with planned 3D doses where the mean passing rate using 3% absolute/3 mm gamma criteria was 99.0% ± 1.0% (n=18; range, 97.0%-100%), demonstrating the feasibility of evaluating the QA results in the patient geometry. Conclusions: We developed a dosimetry program for ViewRay's patient-specific IMRT QA. The methodology will be useful for other ViewRay users. The QA results presented here can assist the RT community to establish appropriate tolerance and action limits for ViewRay's IMRT QA.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Towards the next generation of Geospatial Artificial Intelligence
Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.
58 GEOSCIENCES↗
Numerical approach of the quantum circuit theory
In this paper we develop a numerical method based on the quantum circuit theory to approach the coherent electronic transport in a network of quantum dots connected with arbitrary topology. The algorithm was employed in a circuit formed by quantum dots connected each other in a shape of a linear chain (associations in series), and of a ring (associations in series, and in parallel). For both systems we compute two current observables: conductance and shot noise power. We find an excellent agreement between our numerical results and the ones found in the literature. Moreover, we analyze the algorithm efficiency for a chain of quantum dots, where the mean processing time exhibits a linear dependence with the number of quantum dots in the array.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Latent space dynamics identification for interface tracking with application to shock-induced pore collapse
Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. Here, we propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised autoencoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 10 6 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.
Gaussian process↗
Thermal analyses of high-power advanced thermoacoustic radioisotope power system for future space exploration missions
This paper presents the results of 3-D thermal analyses of the heat source assembly of a 440 We advanced radioisotope power system (RPS) for future space exploration missions. This high thermal efficiency RPS employs a heat source assembly of eight Step 2 General Purpose Heat Source (GPHS) modules and four Thermoacoustic Power Converter (TAPC) units. Each unit is thermally coupled to two GPHS modules. Here, the analyses investigated the temperatures of the Iridium (Ir) alloy cladding of the 238 PuO 2 fuel pellets and the surface of the Fine Weave Pierced Fabric (FWPF) aeroshell in the GPHS modules to ensure reliable operation and safety in case of an unlikely reentry. Conductive coupling of the GPHS modules to the heater heads of the TAPC units produces relatively uniform axial and azimuthal distributions of heat flux and temperature. However, for this case the temperature of the Ir alloy cladding is lower than the desired values of 1173 K to maintain sufficient ductility. Adding a 5 mm wide He filled gap and decreasing the thickness of the graphite sleeve to 10 mm increased Ir cladding temperature to 1197–1174 K, which are in the desired range. The maximum surface temperatures of the FWPF graphite aeroshell of 1022–1131 K is well below the NASA specified limit of 1373 K. In addition, the total mass of the heat source assembly decreased by 19.6 kg, ~43% saving compared to original design.
42 ENGINEERING↗
Toward prediction of turbulent atmospheric flows over propagating oceanic waves via machine-learning augmented large-eddy simulation
Wind-wave interactions have important effects on the energy harvesting of offshore wind farms. High-fidelity large-eddy simulation (LES) is a powerful approach for investigating wind-wave interactions in turbulent oceanic environments. Due to the large scale of the flow domain and the high grid resolution required to resolve multi-scale flow motions, however, brute-force LES of wind-wave interactions is computationally very expensive. We propose augmenting brute-force LES via machine-learning data-driven modeling (ML-LES) to dramatically reduce the computational time required to obtain converged turbulence statistics when the brute-force approach is employed. Namely, we employ a convolutional neural network (CNN) autoencoder trained and validated with LES data sets to develop a highly efficient ML-LES approach for computing turbulence statistics from just a few snapshots of instantaneous LES flow fields. Further, our results demonstrate the accuracy and efficiency of ML-LES in predicting the mean velocity, velocity fluctuations, and turbulence kinetic energy in highly stretched computational grid systems required to carry out simulations in real-life oceanic environments.
42 ENGINEERING↗
Ab Initio Derived Force Fields for Predicting CO 2 Adsorption and Accessibility of Metal Sites in the Metal–Organic Frameworks M-MOF-74 (M = Mn, Co, Ni, Cu)
Metal–organic frameworks (MOFs) are versatile nanoporous materials that have gained significant interest as low heat capacity, high selectivity sorbents for CO 2 capture applications. Large-scale atomistic simulations for identifying high-performance MOFs are possible, but are limited to systems for which existing molecular mechanics force fields describe the interactions between the guest and framework atoms with sufficient accuracy. However, standard force fields are not applicable to cases involving coordinatively unsaturated metal centers that can strongly bind specific sorbate molecules. It has been previously shown that improved force fields can be derived from quantum mechanical calculations. We derived force fields for an isostructural series of MOFs, M-MOF-74, where M = Mn, Co, Ni, and Cu, from first principles. Monte Carlo calculations in the Gibbs ensemble were used to calculate the CO 2 adsorption isotherms in order to assess the quality of the derived force field parameters and to determine a generally applicable procedure for obtaining a reliable force field for a targeted MOF and adsorbate system. The computed CO 2 adsorption isotherms for the different M-MOF-74 members agree with experimental measurements at low loading and show that Ni-MOF-74 possesses the highest affinity toward CO 2 and Cu-MOF-74 the weakest. In addition, we explored the source of open metal site and pore inaccessibility in these materials and quantified its impact on adsorption, especially the discrepancies often observed between experiments and simulations at high loadings.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
The use of machine learning (ML) to refine low-level theoretical calculations to achieve higher accuracy is a promising and actively evolving approach known as Δ-ML. The density matrix renormalization group (DMRG) is a powerful variational approach widely used for studying strongly correlated quantum systems. High computational efficiency can be achieved without compromising accuracy. Here, we demonstrate the potential of a simple ML model to significantly enhance the performance of the quantum chemical DMRG method.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Buried Hydrogen Bond Interactions Contribute to the High Potency of Complement Factor D Inhibitors
Aberrant activation of the complement system is associated with diseases, including paroxysmal nocturnal hemoglobinuria and age-related macular degeneration. Complement factor D is the rate-limiting enzyme for activating the alternative pathway in the complement system. Recent development led to a class of potent amide containing pyrrolidine derived factor D inhibitors. Here, we used biochemical enzymatic and biolayer interferometry assays to demonstrate that the amide group improves the inhibitor potency by more than 80-fold. Our crystal structures revealed buried hydrogen bond interactions are important. Molecular orbital analysis from quantum chemistry calculations dissects the chemical groups participating in these interactions. Free energy calculation supports the differential contributions of the amide group to the binding affinities of these inhibitors. Cell-based hemolysis assay confirmed these compounds inhibit factor D mediated complement activation via the alternative pathway. Our study highlights the important interactions contributing to the high potency of factor D inhibitors reported recently.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Long-time error-mitigating simulation of open quantum systems on near term quantum computers
Not Available
Rost, Brian↗
Implausible projections overestimate near-term Bitcoin CO 2 emissions
Bitcoin mining is becoming an increasingly energy-intensive process whose future implications for energy use and CO 2 emissions remain poorly understood. This is in part because—like many IT systems—its computational efficiencies and service demands have been evolving rapidly. Therefore, scenario analyses that explore these implications can fill pressing knowledge gaps, but they must be approached with care. History has shown that poorly constructed scenarios of future IT energy use—often due to overly-simplistic extrapolations of early rapid growth trends—can do more harm than good by spreading misinformation and driving ill-informed decisions. Indeed, the utility of an energy demand scenario is directly proportional to its credibility, which is typically demonstrated through careful attention to technology characteristics and evolution, analytical rigor and transparency, and designing scenarios that align with plausible future outcomes.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels
Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanism is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training of a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction, and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.
36 MATERIALS SCIENCE↗