Single-View Tomographic Reconstruction Using Learned Primal Dual
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97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “Computed Tomography”
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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Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins
Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that classifies particles by searching for nuances of evolutionarily conversed motifs and the geometrical characteristics of protein structure. GRIP-Tomo 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and high-performance computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in classifying between proteins and noise using both real and synthetic datasets which represents a foundational step toward advancing cryo-ET workflows and empowering automated visual proteomics.
Li, Chengxuan↗
Advanced synchrotron characterization techniques for fusion materials science
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99 GENERAL AND MISCELLANEOUS↗
Coupling computational thermodynamics with density-function-theory based calculations to design L12 precipitates in Fe Ni based alloys
Achieving a high-volume fraction of thermodynamically stable L12-type precipitates that are resistant to coarsening is of great importance for the development of low-cost FeNi based austenitic steels. With the aid of computational thermodynamics, this work designed two model alloys: Fe-37.4Ni-6.1Al-2.9Ti (FNAT) and Fe-45.2Ni-5.9Al-8.5Si (FNAS). Both alloys were designed to contain a similar amount of L1 2 precipitate in Fe-Ni matrix without forming other precipitates. Density-Function-Theory (DFT) calculation was coupled with computational thermodynamics to predict the critical radius at which the precipitates change shape from spherical to cuboidal. The calculation results suggest that critical radius for the FNAT alloy is about two orders of magnitude larger than that for the FeNiAlSi alloy. Phase stability and morphology of the L1 2 precipitates in these two alloys were experimentally investigated through X-ray diffraction, atom probe tomography, and scanning and transmission electron microscopy. The L1 2 precipitates in the FeNiAlSi system were found to be cuboidal and rod shaped, with much larger size than the spherical ones in the FeNiAlTi system, agreeing with the calculation results. This work suggested that coupling computational thermodynamics with DFT calculations can be reliably used to design L1 2 precipitates in FeNi based alloys.
36 MATERIALS SCIENCE↗
Revealing the complex chemistry of grain boundaries in K-doped BaFe 2 As 2 with atom probe tomography
Iron-based superconductors have attractive properties for high-field applications, but there is a lack of understanding of the effect of grain boundary chemistry on the in-field performance. The near atomic-scale resolution, ppm sensitivity and 3D analysis offered by atom probe tomography make it a powerful tool to investigate the nanoscale structure and chemistry of these defects in fine-grained K-doped BaFe 2 As 2 samples. A computational method to systematically extract and compare the Gibbsian interfacial excess of chemical species across grain boundaries has been explored in this work. The robustness of the method has been tested by evaluating the effects of selected variables on simulated APT datasets. The accuracy and precision of the calculated Gibbsian interfacial excess were found to be stable over a range of analysis conditions: varying grain boundary widths and detection efficiencies, spatial precisions below 1.5 nm, and bin widths between 1.2 and 1.6 nm. For the K-doped BaFe 2 As 2 samples studied, segregation of As, Ba, K and impurities of O, Na, and Sb were found at grain boundaries. The Gibbsian excess values were found to vary widely between different boundaries, showing the complexity of the grain boundary chemistry in this material. Possible links between the observed critical current density (Jc) of these samples and their nano- and micro-structure have also been investigated and discussed.
36 MATERIALS SCIENCE↗
Quantum circuit fidelity estimation using machine learning
The computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be efficiently simulated classically, it is important to quantify the accuracy with which the computation has been performed. In this work, we introduce a machine learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. Our machine learning model is trained in a supervised manner, using smaller or simpler circuits for which the fidelity can be estimated using other techniques like direct fidelity estimation and quantum state tomography. Here we demonstrate that, for simulated random quantum circuits with a realistic noise model, the trained model can predict the fidelities of more complicated circuits for which such methods are infeasible. In particular, we show that the trained model may make predictions for circuits with higher degrees of entanglement than were available in the training set and that the model may make predictions for non-Clifford circuits even when the training set included only Clifford-reducible circuits. This empirical demonstration suggests classical machine learning may be useful for making predictions about beyond-classical quantum circuits for some non-trivial problems.
97 MATHEMATICS AND COMPUTING↗
Quantum circuit cutting with maximum-likelihood tomography
Abstract We introduce maximum-likelihood fragment tomography (MLFT) as an improved circuit cutting technique for running clustered quantum circuits on quantum devices with a limited number of qubits. In addition to minimizing the classical computing overhead of circuit cutting methods, MLFT finds the most likely probability distribution for the output of a quantum circuit, given the measurement data obtained from the circuit’s fragments. We demonstrate the benefits of MLFT for accurately estimating the output of a fragmented quantum circuit with numerical experiments on random unitary circuits. Finally, we show that circuit cutting can estimate the output of a clustered circuit with higher fidelity than full circuit execution, thereby motivating the use of circuit cutting as a standard tool for running clustered circuits on quantum hardware.
97 MATHEMATICS AND COMPUTING↗
Understanding Fragmentation of Organic Small Molecules in Atom Probe Tomography
In atom probe tomography of molecular organic materials, field ionization of either entire molecules or molecular fragments can occur, but the mechanism governing this behavior was not previously understood. This work explains when a doubly ionized small molecule organic material is expected to undergo fragmentation. Here, we find that multiple detection events arising from post-ionization fragmentation of a parent molecular dication into two daughter ions is well explained by the free energy and geometries of the molecules computed using density functional theory. Of the systems studied, exergonic free energies for formation of the daughter ions, smaller activation energies for dissociation, and increases in bond length are all found to be quantitative predictors for ion fragmentation. This work expands the applicability of atom probe tomography to organic materials by increasing the fundamental understanding of processes occurring during this analysis technique.
36 MATERIALS SCIENCE↗
Towards Autonomous Experiments by Connecting High Performance Microscopy with High Performance Computing
The digitization of controls, data, and analysis in microscopy is bringing the idea of autonomous microscopes closer to reality than ever before. Automated transmission electron microscopy (TEM) is already fairly routine for some experiments the only require simple repetitive tasks such as imaging biological macromolecules for single particle cryoEM [1], tilt series for electron tomography [2], and movies for crystallography [3]. The vast majority of TEM experiments are conducted completely by human operators who choose the regions of interest, optimize experimental parameters, and make decisions about data quality visually during an experiment. The field is still a long way from having completely autonomous TEMs that can adapt to sample difficulties and tune experimental parameters based on data quality and desired experimental outcomes. Part of the issue is the lack of capability for feeding information learned from on-line, live data analysis back into the on-going experiment [4]. Furthermore, this presentation will discuss current capabilities for large scale data reduction and analysis using high performance computing (i.e. supercomputing) and progress towards developing a true feed-back loop that places data analysis and theory in the experimental loop.
97 MATHEMATICS AND COMPUTING↗
Quantum Computing Algorithms and Applications for Coherent and Strongly Correlated Chemical Systems
This project advanced quantum algorithms, quantum information theory, strongly correlated electronic structure methods, molecular quantum materials, and exciton transport imaging in coherent condensed phase systems. Across the award period, the team developed new methods for open-quantum-system simulation, Hamiltonian learning, state tomography, reduced-density-matrix and contracted-quantum eigensolver approaches, and quantum diagnostics for device capability and openness.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Energy-Dispersive X-ray Spectroscopy and Atom-probe Tomography Data Quantifying Component-Ratios of Multicomponent Nano-Precipitates in Ion-Irradiated Ceria
Samples of ~1 µm films of CeO2 doped with 2 wt% Mo, 1.5 wt% Ru, 0.75 wt% Pd, 0.5 wt% Re and 0.25 wt% Rh grown with pulsed laser deposition were irradiated with I2+ ions (610°C and 730°C, 1016 and 5×1016 I2+/cm2). For selected samples post-irradiation heat treatment was conducted (900°C, 1100°C). The specimens were sectioned with focussed ion beam milling and characterized in a transmission electron microscope with energy dispesive x-ray spectroscopy, and with atom-probe tomography. Energy dispersive x-ray spectroscopy was used to obtain elemental maps showing the distribution of dopants in the specimen after exposure. Some of these maps are discussed in detail in our companion article “Formation of multicomponent alloy particles in doped ceria under I2+ ion irradiation and thermal annealing” in the Journal of Nuclear Materials [REF]. Advanced computational analysis could be used to more accurately quantify local compositions. Data is provided for additional regions of interest and one additional irradiation condition. The doped Ceria film that was heat treated at 1100 °C delaminated from the substrate in most places. Samples were extracted from the underside of a delaminated piece and analyzed with atom-probe tomography. The resulting data show Ceria and a Mo-rich particle and demonstrate that this approach is feasable in principle to study local compositions in a sample exposed to such extreme conditions.
multicomponent alloys, epsilon phase, EDS, atom pr↗
Combining different 3-D global and regional seismic wave propagation solvers towards box tomography in the deep Earth
SUMMARY In previous publications, we presented a general framework, which we called ‘box tomography’, that allows the coupling of any two different numerical seismic wave propagation solvers, respectively outside and inside a target region, or ‘box’. The goal of such hybrid wavefield computations is to reduce the cost of computations in the context of full-waveform inversion for structure within the target region, when sources and/or receivers are located at large distances from the box. Previously, we had demonstrated this approach with sources and receivers outside the target region in a 2-D acoustic spherical earth model, and demonstrated and applied this methodology in the 3-D spherical elastic Earth in a continental scale inversion in which all stations were inside the target region. Here we extend the implementation of the approach to the case of a 3-D global elastic earth model in the case where both sources and stations are outside the box. We couple a global 3-D solver, SPECFEM3D_GLOBE, for the computation of the wavefield and Green’s functions in a reference 3-D model, with a regional 3-D solver, RegSEM, for the computation of the wavefield within the box, by means of time-reversal mirrors. We briefly review key theoretical aspects, showing in particular how only the displacement is needed to be stored at the boundary of the box. We provide details of the practical implementation, including the geometrical design of the mirrors, how we deal with different sizes of meshes in the two solvers, and how we address memory-saving through the use of B-spline compression of the recorded wavefield on the mirror. The proposed approach is numerically efficient but also versatile, since adapting it to other solvers is straightforward and does not require any changes in the solver codes themselves, as long as the displacement can be recovered at any point in time and space. We present benchmarks of the hybrid computations against direct computations of the wavefield between a source and an array of stations in a realistic geometry centred in the Yellowstone region, with and without a hypothetical plume within the ‘box’, and with a 1-D or a 3-D background model, down to a period of 20 s. The ultimate goal of this development is for applications in the context of imaging of remote target regions in the deep mantle, such as, for example, Ultra Low Velocity Zones.
Geochemistry & Geophysics↗
Designing Antifouling and Antimicrobial Interfaces: Structural Characterization using CryoEM, Automated Microscopy, and AI Image Segmentation
The design of functionalized surfaces for interactions with biological systems is critical across sectors such as healthcare, energy, and agriculture. Tailoring materials for specific applications, such as antifouling and antimicrobial surfaces, demands a comprehensive understanding of topology and chemistry across multiple length and time scales on both biological and materials systems. This work presents the development and characterization of nanostructured surfaces with controlled topographies and chemistries that enhance bacterial membrane disruption, reduce biofilm formation, and improve antimicrobial and antifouling capabilities. Two specific use cases will be presented - the use of cellulose nanocrystals (CNCs) for bacterial growth inhibition and the development of antifouling surfaces to prevent protein and bacterial adsorption [1-4]. By leveraging large language models (LLMs) for image segmentation and training [5], we enable automated analysis of terabyte-scale cryogenic electron microscopy (cryoEM) datasets. This analysis provides statistical insights into the biotic/abiotic interface and facilitates automated electron microscopy experiments to mitigate time and dose. The integration of cryogenic electron tomography (cryoET) and cryogenic focused ion beam (cryoFIB) milling enables high-resolution, near-native-state imaging and 3D reconstructions of bio/material interfaces [6]. Orthogonal characterization techniques and computational modeling further enhances our understanding, offering a robust platform for the design and optimization of next-generation functional surfaces [7].
Williams, Alexis [ORNL] (ORCID:0000000252835822)↗
Microstructural Characterization of AGR-2 TRISO-coated Particle Buffer, IPyC, and Buffer-IPyC Interfaces
Investigating the microstructural, mechanical, and chemical behaviors of Tristructural Isotropic (TRISO) fuel particles is vital for its qualification and use in advanced reactors. Central to the study of TRISO particles is understanding the silicon carbide (SiC) layer's ability to confine fission products, with failure mechanisms linked to chemical degradation following mechanical degradation of the buffer and IPyC layers. Research has been done to quantify the micro-tensile properties of the buffer, inner pyrolytic carbon (IPyC), and buffer-IPyC interlayer regions and their interactions within both irradiated and un-irradiated TRISO particles. Techniques such as atom probe tomography (APT) and transmission electron microscopy (TEM) have also been deployed to examine microstructural defects and fission product distribution in detail. The goal is to understand layer delamination, establish connections between microstructure and mechanical attributes, and inform computational predictions of fuel performance. This work may help refine predictive models of TRISO fuel behavior and facilitating its certification for use in advanced reactors.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Detecting damage in composites using volume decomposition analysis of tomographic data
Detection of damage in a single tow ceramic matrix composite specimen has been achieved using orthogonal decomposition of volumetric tomographic datasets collected at four tensile loads. This decomposition approach has been applied at two different length scales: (i) individual fibres and (ii) bulk volumes containing fibres and matrix material. Volumes were first decomposed to feature vectors, orders of magnitude smaller than the original volume they describe, and then comparisons between datasets at different load levels were made in feature vector space. The results show quantitative measurements of damage location, damage morphology and the relative growth of this damage with increased load when compared with a dataset with less or no damage. No prior knowledge of the dataset or training of algorithms is required for damage to be detected, it is only necessary that at least two datasets are available for comparison, e.g. from in situ or repeated scanning measurements. Results are generated on significantly shorter timescales when compared with previous automated approaches to tomography data processing. This approach has the potential to be applied to damage detection in a range of materials through comparisons of volumetric datasets from a range of measurement or computational techniques.
Middleton, Ceri A.↗
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
Three-dimensional inspection of nanostructures such as integrated circuits is important for security and reliability assurance. Two scanning operations are required: ptychographic to recover the complex transmissivity of the specimen, and rotation of the specimen to acquire multiple projections covering the 3D spatial frequency domain. Two types of rotational scanning are possible: tomographic and laminographic. For flat, extended samples, for which the full 180° coverage is not possible, the latter is preferable because it provides better coverage of the 3D spatial frequency domain compared to limited-angle tomography. It is also because the amount of attenuation through the sample is approximately the same for all projections. However, both techniques are time consuming because of extensive acquisition and computation time. Here, we demonstrate the acceleration of ptycho-laminographic reconstruction of integrated circuits with 16 times fewer angular samples and 4.67 times faster computation by using a physics-regularized deep self-supervised learning architecture. We check the fidelity of our reconstruction against a densely sampled reconstruction that uses full scanning and no learning. As already reported elsewhere [ Opt. Express 28 , 12872 ( 2020 ) OPEXFF 1094-4087 10.1364/OE.379200 ], we observe improvement of reconstruction quality even over the densely sampled reconstruction, due to the ability of the self-supervised learning kernel to fill the missing cone.
47 OTHER INSTRUMENTATION↗
Linking void and interphase evolution to electrochemistry in solid-state batteries using operando X-ray tomography
Despite progress in solid-state battery engineering, our understanding of the chemo-mechanical phenomena that govern electrochemical behavior and stability at solid-solid interfaces remains limited compared to solid-liquid interfaces. Here, we use operando synchrotron X-ray computed microtomography to investigate the evolution of lithium/solid-state electrolyte interfaces during battery cycling, revealing how the complex interplay among void formation, interphase growth, and volumetric changes determines cell behavior. Void formation during lithium stripping is directly visualized in symmetric cells, and the loss of contact that drives current constriction at the interface between lithium and the solid-state electrolyte (Li10SnP2S12) is quantified and found to be the primary cause of cell failure. The interphase is found to be redox-active upon charge, and global volume changes occur due to partial molar volume mismatches at either electrode. Finally, these results provide new insight into how chemo-mechanical phenomena can impact cell performance, which is necessary to understand for the development of solid-state batteries.
42 ENGINEERING↗
CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets
Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, 3D reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, in this study, we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets, and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM and cryo-ET as a high-throughput tool for structural biology and discovery.
Levy, Axel [Stanford Univ., CA (United States); SL↗