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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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Large language models are very effective at solving general tasks, but can also be useful in materials design and extracting and using information from the scientific literature and unstructured corpora. For instance, in the domain of alloy design and manufacturing, they can expedite the materials design process and enable the inclusion of holistic criteria.
Abstract We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT–AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of $$7 \times 10^{6}$$ 7 × 10 6 times, in the best case, and $$7 \times 10^{3}$$ 7 × 10 3 times on average compared to a finite element solver. Furthermore, this BT–AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.
Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but American clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large language models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients.
A recent phenomenal study discovered that the extension domain of secreted amyloid-β precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABA B R1a) and modulate its synaptic transmission. The work provided an important structural foundation for the modulation of GABA B R1a; however, the detailed molecular interaction mechanism, crucial for future drug design, remains elusive. Here, we further investigated the dynamical interactions between sAPP peptides and the natively unstructured sushi 1 domain using all-atom molecular dynamics simulations, for both the 17-residue sAPP peptide (APP 17-mer) and its minimally active 9 residue segment (APP 9-mer). We then explored mutations of the APP 9-mer with rigorous free energy perturbation (FEP) calculations. Our in silico mutagenesis studies revealed key residues (D4, W6, and W7) responsible for the binding with the sushi 1 domain. More importantly, one double mutation based on different vertebrate APP sequences from evolution exhibited a stronger binding (ΔΔG = -1.91 ± 0.66 kcal mol -1 ), indicating a potentially enhanced GABA B R1a modulator. These large-scale simulations may provide new insights into the binding mechanism between sAPP and the sushi 1 domain, which could open new avenues in the development of future GABA B R1a-specific therapeutics.
Inorganic phototropic growth using only spatially conformal illumination generated Se–Cd films that exhibited precise light-defined mesoscale morphologies including highly ordered, anisotropic, and periodic ridge and trench nanotextures over entire macroscopic substrates. Growth was accomplished via a light-induced electrochemical method using an optically and chemically isotropic solution, an unpatterned substrate, and unstructured, incoherent, low-intensity illumination in the absence of chemical directing agents or physical templates and masks. The morphologies were defined by the illumination inputs: the nanotexture long axes aligned parallel to the optical E-field vector, and the feature sizes and periods scaled with the wavelength. Optically based modeling of the growth closely reproduced the experimental results, confirming the film morphologies were fully determined by the light–matter interactions during growth. Solution processing of the Se–Cd films resulted in stoichiometric, crystalline CdSe films that also exhibited ordered nanotextures, demonstrating that inorganic phototropic growth can effect tunable, template-free generation of ordered CdSe nanostructures over macroscopic length scales.
Here, the photodissociation dynamics of the dimethyl-substituted acetone oxide Criegee intermediate [(CH 3 ) 2 COO] is characterized following electronic excitation to the bright 1 ππ* state, which leads to O ( 1 D) + acetone [(CH 3 ) 2 CO, S 0 ] products. The UV action spectrum of (CH 3 ) 2 COO recorded with O ( 1 D) detection under jet-cooled conditions is broad, unstructured, and essentially unchanged from the corresponding electronic absorption spectrum obtained using a UV-induced depletion method. This indicates that UV excitation of (CH 3 ) 2 COO leads predominantly to the O ( 1 D) product channel. A higher energy O ( 3 P) + (CH 3 ) 2 CO (T 1 ) product channel is not observed, although it is energetically accessible. In addition, complementary MS-CASPT2 trajectory surface-hopping (TSH) simulations indicate minimal population leading to the O ( 3 P) channel and non-unity overall probability for dissociation (within 100 fs). Velocity map imaging of the O ( 1 D) products is utilized to reveal the total kinetic energy release (TKER) distribution upon photodissociation of (CH 3 ) 2 COO at various UV excitation energies. Simulation of the TKER distributions is performed using a hybrid model that combines an impulsive model with a statistical component, the latter reflecting the longer-lived (>100 fs) trajectories identified in the TSH calculations. The impulsive model accounts for vibrational activation of (CH 3 ) 2 CO arising from geometrical changes between the Criegee intermediate and the carbonyl product, indicating the importance of CO stretch, CCO bend, and CC stretch along with activation of hindered rotation and rock of the methyl groups in the (CH 3 ) 2 CO product. Detailed comparison is also made with the TKER distribution arising from photodissociation dynamics of CH 2 OO upon UV excitation.
Soft actuators are at the forefront of the innovation tide in medicine, manufacturing, and aerospace because they are able to mimic the behavior of biological tissue and adapt to complex, unstructured environments. Of all the materials used, silicone-based elastomers have drawn enormous attention since they offer a superb combination of mechanical flexibility, biocompatibility, thermal stability, and long-term durability. In the past few years, there has also been a rapid pace of material evolution, additive manufacturing, and biointegration that has enhanced the performance and applications of silicone-based soft actuators. However, there is no focused and timely review compiling these advances. This review seeks to address that need by critically discussing recent advancements in advanced silicone materials, exploring new fabrication methodologies, and discussing emerging applications that range from wearable devices to implantable robotics. We also present suggestions for directions and the problems which must be addressed in order to further develop the performance and potential of silicone-based soft actuators, justifying the relevance and urgency of this effort.
Simulating magnetically confined fusion plasmas presents a uniquely challenging problem due to the nonlinear anisotropic heat conduction. We introduce the MAPS (MFEM Anisotropic Plasma Solver) tool, which uses a high-order finite element method to compute transport solutions on unstructured meshes. We show results for a set of three 2-D verification tests, two of which demonstrate the expected convergence properties for various mesh resolutions and polynomial degrees. We then discuss the convergence rate for the third test.
Two existing particle-in-cell gyrokinetic codes, GEM for the core region and XGC for the edge region, have been successfully coupled with a spatial coupling scheme at the interface in a toroidal geometry. Additionally, a mapping technique is developed for transferring data between GEM's structured and XGC's unstructured meshes. Two examples of coupled simulations are presented to demonstrate the coupling scheme. The optimization of GEM for graphics processing unit is also presented.
Covering the core and the edge region of a tokamak, respectively, the two gyrokinetic turbulence codes Gyrokinetic Electromagnetic Numerical Experiment (GENE) and X-point Gyrokinetic Code (XGC) have been successfully coupled by exchanging three-dimensional charge density data needed to solve the gyrokinetic Poisson equation over the entire spatial domain. Certain challenges for the coupling procedure arise from the fact that the two codes employ completely different numerical methods. This includes, in particular, the necessity to introduce mapping procedures for the transfer of data between the unstructured triangular mesh of XGC and the logically rectangular grid (in a combination of real and Fourier space) used by GENE. Constraints on the coupling scheme are also imposed by the use of different time integrators. First, coupled simulations are presented. We have considered collisionless ion temperature gradient turbulence, in both circular and fully shaped plasmas. Coupled simulations successfully reproduce both GENE and XGC reference results, confirming the validity of the code coupling approach toward a whole device model. Here, many lessons learned in the present context, in particular, the need for a coupling procedure as flexible as possible, should be valuable to our and other efforts to couple different kinds of codes in pursuit of a more comprehensive description of complex real-world systems and will drive our further developments of a whole device model for fusion plasmas.
We report a target design which produced a substantial gain in relativistic electron-positron pair production using high-intensity lasers and targets with large-scale micro-structures on their surface. Comparing to an unstructured target, a selected Si microwire array target yielded a near 100% increase in the laser-to-positron conversion efficiency and produced a 10MeV increase in the average emitted positron energy under nominally the same experimental conditions. We had established a multi-scale particle-in-cell simulation scheme to simulate both the laser absorption and the subsequent pair productions in a thick metal target. Here, the experimental results are supported by the simulations demonstrating the performance increase is due to a higher conversion efficiency of laser energy into electrons with kinetic energies greater than 10 MeV due to enhanced direct laser acceleration of electrons enabled by the microwire array.
A low-Mach, unstructured, large-eddy-simulation-based, unsteady flamelet approach with a generalized heat loss combustion methodology (including soot generation and consumption mechanisms) is deployed to support a large-scale, quiescent, 5-m JP-8 pool fire validation study. The quiescent pool fire validation study deploys solution sensitivity procedures, i.e., the effect of mesh and time step refinement on capturing key fire dynamics such as fingering and puffing, as mesh resolutions approach O(1) cm. A novel design-order, discrete-ordinate-method discretization methodology is established by use of an analytical thermal/participating media radiation solution on both low-order hexahedral and tetrahedral mesh topologies in addition to quadratic hexahedral elements. Here, the coupling between heat losses and the flamelet thermochemical state is achieved by augmenting the unsteady flamelet equation set with a heat loss source term. Soot and radiation source terms are determined using flamelet approaches for the full range of heat losses experienced in fire applications including radiative extinction. The proposed modeling and simulation paradigm are validated using pool surface radiative heat flux, maximum centerline temperature location, and puffing frequency data, all of which are predicted within 10% accuracy. Simulations demonstrate that under-resolved meshes predict an overly conservative radiative heat flux magnitude with improved comparisons as compared to a previously deployed hybrid Reynolds-averaged Navier–Stokes/eddy dissipation concept-based methodology.
The electronic spectrum of methyl vinyl ketone oxide (MVK-oxide), a four-carbon Criegee intermediate derived from isoprene ozonolysis, is examined on its second π* ← π transition, involving primarily the vinyl group, at UV wavelengths (λ) below 300 nm. A broad and unstructured spectrum is obtained by a UV-induced ground state depletion method with photoionization detection on the parent mass (m/z 86). Electronic excitation of MVK-oxide results in dissociation to O ( 1 D) products that are characterized using velocity map imaging. Electronic excitation of MVK-oxide on the first π* ← π transition associated primarily with the carbonyl oxide group at λ > 300 nm results in a prompt dissociation, and yields broad total kinetic energy release (TKER) and anisotropic angular distributions for the O ( 1 D) + methyl vinyl ketone products. By contrast, electronic excitation at λ ≤ 300 nm results in bimodal TKER and angular distributions, indicating two distinct dissociation pathways to O ( 1 D) products. Furthermore, one pathway is analogous to that at λ > 300 nm, while the second pathway results in very low TKER and isotropic angular distributions indicative of internal conversion to the ground electronic state and statistical unimolecular dissociation.
An interpolation method to evaluate magnetic fields, given its unstructured and scattered magnetic data, is presented. The method is based on the reconstruction of the global magnetic field using a superposition of orthogonal functions. The coefficients of the expansion are obtained by minimizing a cost function defined as the L2 norm of the difference between the ground truth and the reconstructed magnetic field evaluated on the training data. The divergence-free condition is incorporated as a constraint in the cost function, allowing the method to achieve arbitrarily small errors in the magnetic field divergence. An exponential decay of the approximation error is observed and compared with the less favorable algebraic decay of local splines. Compared to local methods involving computationally expensive search algorithms, the proposed method exhibits a significant reduction of the computational complexity of the field evaluation, while maintaining a small error in the divergence even in the presence of magnetic islands and stochasticity. Finally, applications to the computation of Poincaré sections using data obtained from numerical solutions of the magnetohydrodynamic equations in toroidal geometry are presented and compared with local methods currently in use.
The role to which a realistic inflow turbulent boundary layer (TBL) influences transient and mean large-scale pool fire quantities of interest (QoIs) is numerically investigated. High-fidelity, low-Mach large-eddy simulations that activate low-dissipation, unstructured numerics are conducted using an unsteady flamelet combustion modeling approach with mutiphysics coupling to soot and participating media radiation transport. Three inlet profile configurations are exercised for a large-scale, high-aspect rectangular pool that is oriented perpendicular to the flow direction: a time-varying, TBL inflow profile obtained from a periodic precursor simulation, the time-mean of the transient TBL, and a steady power-law inflow profile that replicates the mean TBL crosswind velocity of 10.0 m/s at a vertical height of 10 m. Results include both qualitative transient flame evolution and quantitative flame shape with ground-level temperature and convective/radiative heat flux profiles. While transient fire events, which are driven by burst-sweep TBL coupling, such as blow-off and reattachment are vastly different in the TBL case (contributing to increased root mean square QoI fluctuation prediction and disparate flame lengths), mean surface QoI magnitudes are similar. Quadrant analysis demonstrates that the TBL configuration modifies burst-sweep phenomena at windward pool locations, while leeward recovery is found. In conclusion, positive fluctuations of convective heat flux correlate with fast moving fluid away from the pool surface due to intermittent combustion events.
The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.
High-harmonic upconversion driven by a mid-infrared femtosecond laser can generate coherent soft x-ray beams in a tabletop-scale setup. Here, we report on a compact ytterbium-pumped optical parametric chirped pulse amplifier (OPCPA) laser system seeded by an all-fiber front-end and employing periodically poled lithium niobate (PPLN) nonlinear media operated near the pulse fluence limits of current commercially available PPLN crystals. The OPCPA delivers 3 µm wavelength pulses with 775 µJ energy at 1 kHz repetition rate, with transform-limited 120 fs pulse duration, diffraction-limited beam quality, and ultrahigh 0.33% rms energy stability over >18 h. Using this laser, we generate soft x-ray high harmonics (HHG) in argon gas by focusing into a low-loss, high-pressure gas-filled anti-resonant hollow core fiber (ARHCF), generating coherent light at photon energies up to the argon L-edge (250 eV) and carbon K-edge (284 eV), with high beam quality and ∼1% rms energy stability. This work demonstrates soft x-ray HHG in a high-efficiency guided-wave phase matched geometry, overcoming the high losses inherent to mid-IR propagation in unstructured waveguides, or the short interaction lengths of gas cells or jets. The ARHCF can operate in the long term without damage and with the repetition rate, stability, and robustness required for demanding applications in spectromicroscopy and imaging. Finally, we discuss routes for further optimizing the soft x-ray HHG flux by driving He at higher laser intensities using either the signal (1.5 μm) or idler wavelengths (3 μm).
We consider methods for obtaining a multitude of structured laser beams (multiplexing) from an illuminating beam (both structured and unstructured) with the help of diffractive optical elements (DOEs). An approach of ‘intelligent multiplexing’ is proposed to describe and develop the methods. A DOE is calculated that forms a set of five diffraction orders located in a line. An example of focusing a set of doughnut-shaped azimuthally polarised laser beams using a diffraction beam splitter is presented. Efficient multiplexing of first-order vortex beams in a two-dimensional region is implemented by a two-dimensional diffraction grating. An approach is proposed and realised in which the transmission functions of a two-dimensional diffraction beam splitter and the elements forming structured laser beams with specified parameters are combined into the transmission function of one element. Such DOEs can be used in optical communication systems for encoding and decoding data. The possibility of using binary curved fork-shaped gratings for the formation of doughnut-shaped three-dimensional vortex beams, which are detected outside the focal plane, is demonstrated. This approach provides additional advantages for safe data transmission; it can be used in laser processing of materials and in laser manipulation applications. (paper)