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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Investigating shock-induced chemical reactions in Ni+Al multilayers: A continuum-based mesoscale approach with Arrhenius kinetics and artificial thermal conduction

A new continuum-based mesoscale modeling approach for shock-induced chemical reactions (SICRs) in Ni+Al multilayers is demonstrated in Sandia’s shock physics hydrocode, CTH. The approach utilizes Arrhenius-type kinetics and artificial thermal conduction. Our work builds upon previous efforts to parameterize equations of state for Ni x Al y [J. Appl. Phys. 137, 075102 (2025)], as well as simulations of inert shocks in realistic 2D microstructures [J. Appl. Phys. 137, 225301 (2025)]. To calibrate the reaction kinetics, pairs of the reaction coordinate, R′, vs time are extracted from the molecular dynamics (MD) literature. Here, the MD-informed kinetics are used to simulate the dynamic evolution of pressure and temperature in 2D mesoscale simulations. Overall, the MD-informed kinetics obtained for planar interfaces are too slow, as initial reaction is not observed on a nanosecond time scale. Even with quasi-periodic shock focusing leading to the formation of so-called “hot-spots,” the hot spots are unable to grow and coalesce using the fitted Arrhenius rate constants. However, by increasing the rate constants by two orders of magnitude, SICRs are observed at a shock pressure near 30 GPa, which is supported by experiments. Consequently, these mesoscale simulations suggest that unresolved shear-based mechanical mixing might possibly account for the discrepancies in kinetic rates, with shock-generated intense perturbations, interfacial vortical flows, and elevated temperatures serving as favorable reaction conditions. Future work will calibrate a shear-dependent reaction rate from the MD simulations with realistic interfaces that are wavy, diffuse, and disordered.

Kittell, David E. [Sandia National Laboratories (S↗
DOE OSTI2021

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease

Significance Microfluidics is an important in vitro platform to gain insights into mechanics of blood flow and mechanisms of pathophysiology of human diseases. Extraction of 3D fields in microfluidics with dense cell suspensions remains a formidable challenge. We present artificial-intelligence velocimetry (AIV) as a general platform to determine 3D flow fields and a microaneurysm-on-a-chip to simulate blood flow in microaneurysms in patients with diabetic retinopathy. AIV is built on physics-informed neural networks that integrate seamlessly 2D images from microfluidic experiments or in vivo observations with physical laws to estimate full 3D velocity and stress fields. AIV could be integrated into imaging technologies to automatically infer key hemodynamic metrics from in vivo and in vitro biomedical images.

42 ENGINEERING↗
DOE OSTI2023

Roses in the nonperturbative current response of artificial crystals

In two-dimensional artificial crystals with large real-space periodicity, the nonlinear current response to a large applied electric field can feature a strong angular dependence, which encodes information about the band dispersion and Berry curvature of isolated electronic Bloch minibands. Within the relaxation-time approximation, we obtain analytic expressions up to infinite order in the driving field for the current in a band-projected theory with time-reversal and trigonal symmetry. For a fixed field strength, the dependence of the current on the direction of the applied field is given by rose curves whose petal structure is symmetry constrained and is obtained from an expansion in real-space translation vectors. We illustrate our theory with calculations on periodically buckled graphene and twisted double bilayer graphene, wherein the discussed physics can be accessed at experimentally relevant field strengths.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure-driven development of a biomimetic rare earth artificial metalloprotein

The 2011 discovery of the first rare earth–dependent enzyme in methylotrophic Methylobacterium extorquens AM1 prompted intensive research toward understanding the unique chemistry at play in these systems. This enzyme, an alcohol dehydrogenase (ADH), features a La 3+ ion closely associated with redox-active coenzyme pyrroloquinoline quinone (PQQ) and is structurally homologous to the Ca 2+ -dependent ADH from the same organism. AM1 also produces a periplasmic PQQ-binding protein, PqqT, which we have now structurally characterized to 1.46-Å resolution by X-ray diffraction. This crystal structure reveals a Lys residue hydrogen-bonded to PQQ at the site analogously occupied by a Lewis acidic cation in ADH. Accordingly, we prepared K 142 A- and K 142 D-PqqT variants to assess the relevance of this site toward metal binding. Isothermal titration calorimetry experiments and titrations monitored by UV–Vis absorption and emission spectroscopies support that K 142 D-PqqT binds tightly (Kd = 0.6 ± 0.2 μM) to La 3+ in the presence of bound PQQ and produces spectral signatures consistent with those of ADH enzymes. These spectral signatures are not observed for WT- or K 142 A-variants or upon addition of Ca 2+ to PQQ ⸦ K 142 D-PqqT. Addition of benzyl alcohol to La 3+ -bound PQQ ⸦ K 142 D-PqqT (but not Ca 2+ -bound PQQ ⸦ K 142 D-PqqT, or La 3+ -bound PQQ ⸦ WT-PqqT) produces spectroscopic changes associated with PQQ reduction, and chemical trapping experiments reveal the production of benzaldehyde, supporting ADH activity. By creating a metal binding site that mimics native ADH enzymes, we present a rare earth-dependent artificial metalloenzyme primed for future mechanistic, biocatalytic, and biosensing applications.

Science & Technology - Other Topics↗
DOE OSTI2025

Fractional magnetic charges and channeling of Faraday lines by disclinations in artificial spin ice

We have studied the magnetic moments of artificial spin ice arrays of nanomagnets in both undistorted square arrays and in arrays with a topological defect induced by a single disclination. We confirm that the disclination induces global, macroscopic changes in the low-energy collective states of the nanomagnet moment configuration. Specifically, the disclination leads to Faraday lines of effective magnetic flux that run from the center all the way to the edge of the arrays. Moreover, the geometric deformation, induced by the topological defect, curves the geometry such that these Faraday lines are channeled preferentially by the arrays’ curved geometry. Our results demonstrate how the intentional combination of topology and geometry can be designed to control magnetic charges and the flow of local magnetization and thus manipulate associated collective excitations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-quality data, as well as advances in both hardware and software to efficiently process these data, are transforming a range of fields from computer vision and natural language processing to autonomous driving and healthcare. For example, the availability of high-resolution geographic data and high-performance computing techniques together with deep learning fuel progress in fast and accurate object detection. Recent examples of GeoAI work include the detections of terrain features and densely-distributed building footprints, information extraction from scanned historical maps, semantic classification (e.g. LiDAR point clouds), novel methods for spatial interpolation, and advances in traffic forecasting. Similarly, machine learning and natural language processing are facilitating the extraction of geographic information from unstructured (textual) data, such as news articles and Wikipedia as well as the matching of natural features in multiple gazetteers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2013

DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS

This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A new method for point-spread function correction using the ellipticity of re-smeared artificial images in weak gravitational lensing shear analysis

Highly accurate weak lensing analysis is urgently required for planned cosmic shear observations. For this purpose we have eliminated various systematic noises in the measurement. The point-spread function (PSF) effect is one of them. A perturbative approach for correcting the PSF effect on the observed image ellipticities has been previously employed. Here we propose a new non-perturbative approach for PSF correction that avoids the systematic error associated with the perturbative approach. The new method uses an artificial image for measuring shear which has the same ellipticity as the lensed image. This is done by re-smearing the observed galaxy images and observed star images (PSF) with an additional smearing function to obtain the original lensed galaxy images. We tested the new method with simple simulated objects that have Gaussian or Sérsic profiles smeared by a Gaussian PSF with sufficiently large size to neglect pixelization. Under the condition of no pixel noise, it is confirmed that the new method has no systematic error even if the PSF is large and has a high ellipticity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Logical gates embedding in artificial spin ice

The realization and study of arrays of interacting magnetic nanoislands, such as artificial spin ices, have reached mature levels of control that allow design and demonstration of exotic, collective behaviors not seen in natural materials. Advances in the direct manipulation of their local, binary moments also suggest a use as nanopatterned, interacting memory media, for computation within a magnetic memory. Recent experimental work has demonstrated the possibility of building logic gates from clusters of interacting magnetic domains, and yet the possibility of large scale integration of such gates can prove problematic even at the theoretical level. Here we introduce theoretically complete sets of logical gates, in principle realizable in an experiment, and we study the feasibility of their integration into tree-like circuits. By evaluating the fidelity control parameter between their collective behavior and their expected logic functionality we determine conditions for integration. Also, we test our numerical results against the presence of disorder in the couplings, showing that the design gate structure is robust to small coupling perturbations, and thus possibly to small imperfections in the fabrication of the islands.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Ice rule breakdown and frustrated antiferrotoroidicity in an artificial colloidal Cairo ice

Abstract We combine experiments and numerical simulations to investigate the low energy states and the emergence of topological defects in an artificial colloidal ice in the Cairo geometry. This type of geometry is characterized by a mixed coordination ( z ), with coexistence of both z = 3 and z = 4 vertices. We realize this particle ice by confining field tunable paramagnetic colloidal particles within a lattice of topographic double wells at a one to one filling using optical tweezers. By raising the interaction strength via an applied magnetic field, we find that the ice rule breaks down, and positive monopoles with charge q = + 2 accumulate in the z = 4 vertices and are screened by negative ones ( q = − 1 ) in the z = 3. The resulting, strongly coupled state remains disordered. Further, via analysis of the mean chirality associated to each pentagonal plaquette, we find that the disordered ensemble for this geometry is massively degenerate and it corresponds to a frustrated antiferrotoroid.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

An Artificial Retina Processor for Track Reconstruction at the LHC Crossing Rate

The goal of the INFN-RETINA R&D project is to develop and implement a computational methodology that allows to reconstruct events with a large number (> 100) of charged-particle tracks in pixel and silicon strip detectors at 40 MHz, thus matching the requirements for processing LHC events at the full bunch-crossing frequency. Our approach relies on a parallel pattern-recognition algorithm, dubbed artificial retina, inspired by the early stages of image processing by the brain. In order to demonstrate that a track-processing system based on this algorithm is feasible, we built a sizable prototype of a tracking processor tuned to 3 000 patterns, based on already existing readout boards equipped with Altera Stratix III FPGAs. The detailed geometry and charged-particle activity of a large tracking detector currently in operation are used to assess its performances. We report on the test results with such a prototype.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Design of detectors at the electron ion collider with artificial intelligence

Abstract Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The electron ion collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about ±35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.

Instruments & Instrumentation↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2021

Outlook for artificial intelligence and machine learning at the NSLS-II

Abstract We describe the current and future plans for using artificial intelligence and machine learning (AI/ML) methods at the National Synchrotron Light Source II (NSLS-II), a scientific user facility at the Brookhaven National Laboratory. We discuss the opportunity for using the AI/ML tools and techniques developed in the data and computational science areas to greatly improve the scientific output of large scale experimental user facilities. We describe our current and future plans in areas including from detecting and recovering from faults, optimizing the source and instrument configurations, streamlining the pipeline from measurement to insight, through data acquisition, processing, analysis. The overall strategy and direction of the NSLS-II facility in relation to AI/ML is presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions

Abstract New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein–ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein–ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein–ligand interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges

Abstract Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma

Abstract Background Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients. Objectives To determine if artificial intelligence and machine learning might help to stratify patients with cSCC by risk using more than solely clinical and histopathological factors. Methods We retrieved a retrospective cohort of 104 patients whose cSCCs had been excised with clear margins. Clinical and histopathological risk factors were evaluated. Haematoxylin and eosin-stained slides were scanned and analysed by an algorithm based on the stacked predictive sparse decomposition technique. Cellular morphometric biomarkers (CMBs) were identified via machine learning and used to derive a cellular morphometric risk score (CMRS) that classified cSCCs into clusters of differential prognoses. Concordance analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated and compared with results obtained with the Brigham and Women’s Hospital (BWH) staging system. The performance of the combination of the BWH staging system and the CMBs was also analysed. Results There were no differences among the CMRS groups in terms of clinical and histopathological risk factors and T-stage assignment, but there were significant differences in prognosis. Combining the CMRS with BWH staging systems increased distinctiveness and improved prognostic performance. C-indices were 0.91 local recurrence and 0.91 for nodal metastasis when combining the two approaches. The NPV was 94.41% and 96.00%, the PPV was 36.36% and 41.67%, and accuracy reached 86.75% and 89.16%, respectively, with the combined approach. Conclusions CMRS is helpful for cSCC risk stratification beyond classic clinical and histopathological risk features. Combining the information from the CMRS and the BWH staging system offers outstanding prognostic performance for patients with high-risk cSCC.

Pérez-Baena, Manuel J.↗
DOE OSTI2021

Detecting optical transients using artificial neural networks and reference images from different surveys

ABSTRACT We present a technique to detect optical transients based on an artificial neural networks method. We describe the architecture of two networks capable of comparing images of the same part of the sky taken by different telescopes. One image corresponds to the epoch in which a potential transient could exist; the other is a reference image of an earlier epoch. We use data obtained by the Dr. Cristina V. Torres Memorial Astronomical Observatory and archival reference images from the Sloan Digital Sky Survey. We trained a convolutional neural network and a dense layer network on simulated source samples and then tested the trained networks on samples created from real image data. Autonomous detection methods replace the standard process of detecting transients, which is normally achieved by source extraction of a difference image followed by human inspection of the detected candidates. Replacing the human inspection component with an entirely autonomous method would allow for a rapid and automatic follow-up of interesting targets of opportunity. The toy-model pipeline that we present here is not yet able to replace human inspection, but it might provide useful hints to identify potential candidates. The method will be further expanded and tested on telescopes participating in the Transient Optical Robotic Observatory of the South Collaboration.

79 ASTRONOMY AND ASTROPHYSICS↗