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At least 217 records · Page 12

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DOE OSTI2023

Robust deep learning framework for constitutive relations modeling

Modeling the full-range deformation behaviors of materials under complex loading and materials conditions is a significant challenge for constitutive relations (CRs) modeling. Here, we propose a general encoder-decoder deep learning framework that can model high-dimensional stress-strain data and complex loading histories with robustness and universal capability. The framework employs an encoder to project high-dimensional input information (e.g., loading history, loading conditions, and materials information) to a lower-dimensional hidden space and a decoder to map the hidden representation to the stress of interest. We evaluated various encoder architectures, including gated recurrent unit (GRU), GRU with attention, temporal convolutional network (TCN), and the Transformer encoder, on two complex stress-strain datasets that were designed to include a wide range of complex loading histories and loading conditions. All architectures achieved excellent test results with an root-mean-square error (RMSE) below 1 MPa. Additionally, we analyzed the capability of the different architectures to make predictions on out-of-domain applications, with an uncertainty estimation based on deep ensembles. The proposed approach provides a robust alternative to empirical/semi-empirical models for CRs modeling, offering the potential for more accurate and efficient materials design and optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Multi-omics profiling of the cold tolerant Monoraphidium minutum 26B-AM in response to abiotic stress

Microalgae that are of interest for biofuel production must be able to tolerate environmental changes that occur in outdoor cultivation systems. While algal cultures may experience daily temperature fluctuations and seasonal environmental changes, the underlying mechanisms that control and regulate physiological responses and adaptation to environmental pressures are largely unknown. Systems-level characterization enabled by functional genomics can help identify biochemical pathways that promote stability and productivity of algae in various environmental conditions. Monoraphidium minutum 26B-AM, a freshwater green microalga, was identified as a top performer in biomass production in winter season screens. We sequenced the genome of M. minutum 26B-AM and applied our multi-omics pipeline to profile this high potential strain under high salt and cold temperature perturbations. Through comparative analysis, including other green algae in the class Chlorophyceae, we identified gene families unique to the genus Monoraphidium, including a desaturase that has been linked to cold tolerance in plants. We observed that osmolytes, such as trehalose, proline and betaine, accumulate under salt stress, coinciding with upregulation of genes involved in biosynthesis of these metabolites. From the genome annotation, we reconstructed a metabolic model to provide a detailed map of the metabolic pathways and can be used to simulate growth and reaction fluxes. This multi-omics analysis provides a foundation to explore algal strain potential for biofuel applications, guides strain engineering, and expands our understanding of metabolic and regulatory mechanisms of algae in applied systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Analysis of strain in ion implanted 4H-SiC by fringes observed in synchrotron X-ray topography

A novel high energy implantation system has been successfully developed to fabricate 4H-SiC superjunction devices for medium and high voltage via implantation of dopant atoms with multi-energies ranging from 13 to 66 MeV. The significantly higher levels of energy used compared to conventional implantation processes, necessitates detailed characterization of the lattice damage caused by implantation. To achieve this by employing the novel high energy system, 4H-SiC wafer with 12 μm epilayers were blanket implanted by 13.8–65.7 MeV Al atoms. The lattice damages induced by the implantation were primarily characterized by Synchrotron X-ray Plane Wave Topography (SXPWT) and Reciprocal Space Mapping (RSM). Topographs reveal fringe contrast akin to multiple asymmetric diffraction peaks with an angular separation of only 2″ (arcseconds) observed on rocking curves, indicating inhomogeneous strain distribution across the implanted layer. The strain profile of the implanted layer was extracted from the fringe contrast by applying Rocking-curve Analysis by Dynamical Simulation (RADS). In conclusion, the maximum strain value is similar to that measured on the RSM.

A1. Characterization↗

DOE OSTI2022

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Abstract A novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics (GH) is developed for predicting the response of complex physical phenomena. The method utilizes GDMaps to obtain a low‐dimensional representation of the underlying behavior of physical/mathematical systems with respect to uncertain input parameters. Using this representation, GH, an out‐of‐sample extension technique, is employed to create a global map from the input parameter space to a Grassmannian diffusion manifold. GH is further employed to locally map points on the diffusion manifold onto the tangent space of a Grassmann manifold. The exponential map is then used to project the points in the tangent space onto the Grassmann manifold, where reconstruction of the full solution is performed. The performance of the proposed surrogate model is verified with three examples. The first problem is a toy example used to illustrate the technique. In the second example, errors associated with the various mappings are assessed by studying response predictions of the electric potential of a dielectric cylinder in a homogeneous electric field. The last example applies the method for uncertainty prediction in the strain field evolution in a model amorphous material using the shear transformation zone theory of plasticity.

42 ENGINEERING↗

DOE OSTI2026

Mapping the Residual Stress Distribution in Polycrystalline Quartz Tiger's Eye Using Raman Spectroscopy

Stress distribution maps in polycrystalline materials are needed to reveal stress pathways caused by short‐and long‐range interactions of crystallites. Stress induced changes to wavenumbers in Raman spectroscopy is a well‐known phenomenon that occurs when a crystalline material undergoes elastic strain. Using spatially resolved Raman spectroscopy, we have developed a technique to experimentally measure grain‐scale residual stresses across a sample of polycrystal quartz Tiger's Eye. The results of this technique are validated using multiple criteria, including evaluation of the residual stress map itself and the accuracy of the measurements taken. The residual map of Tiger's Eye shows striking similarity to observed characteristics of other polycrystalline residual stress distributions, revealing heterogenous areas of high and low magnitude stresses. The stress magnitudes are consistent with residual stress magnitudes previously measured from polycrystalline quartz. Characterization of Tiger's Eye quartz also revealed the geometric nature of the iron oxide inclusions, not previously observed in Tiger's Eye quartz. With this technique, we present a residual stress map of Tiger's Eye quartz that shows with accuracy a heterogeneous stress state with inter‐ and intra‐granular detail.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS 2 through Machine‐Learning Models

Abstract 2D transition metal dichalcogenides (TMDCs) with intense and tunable photoluminescence (PL) have opened up new opportunities for optoelectronic and photonic applications such as light‐emitting diodes, photodetectors, and single‐photon emitters. Among the standard characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non‐destructive technique capable of probing material's crystallinity and perturbations such as doping and strain. However, a comprehensive understanding of the correlation between photoluminescence and Raman spectra in monolayer MoS 2 remains elusive due to its highly nonlinear nature. Here, the connections between PL signatures and Raman modes are systematically explored, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This study's analysis further disentangles the strain and doping contributions from the Raman spectra through machine‐learning models. First, a dense convolutional network (DenseNet) to predict PL maps by spatial Raman maps is deployed. Moreover, a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features is applied. Last, a support vector machine (SVM) to project PL features on Raman frequencies is adopted. This work may serve as a methodology for applying machine learning to characterizations of 2D materials.

Lu, Ang‐Yu↗

DOE OSTI2020

XtalCAMP: a comprehensive program for the analysis and visualization of scanning Laue X-ray micro-/nanodiffraction data

XtalCAMP is a software package based on the MATLAB platform, which is suitable for, but not limited to, the analysis and visualization of scanning Laue X-ray micro-/nanodiffraction data. The main objective of the software is to provide complementary functionalities to the Laue indexing software packages used at several synchrotron beamlines. Here, the graphical user interfaces allow the easy analysis of characteristic microstructure features, including real-time intensity mapping for a quick examination of phase, grain and defect distribution, 2D color-coded mapping of microstructural properties from the output of other Laue indexing software, crystal orientation visualization, grain boundary characterization based on orientation/misorientation calculation, principal strain/stress analysis, and strain ellipsoid representation, as well as a series of additional toolkits. As an example, XtalCAMP is applied to the microstructural investigation of a solution-heat-treated Ni-based superalloy manufactured using a laser 3D-printing technique, and a deformed natural quartzite from Val Bregaglia in the Central Alps.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Cas3-Mediated Genome Reduction: Demonstration in Cupriavidus Necator H16 Improves Growth on Heterotrophic and Autotrophic Carbon Sources

Genome reduction is widely used to improve microbial bioprocessing hosts by reducing the burden of inessential physiology. Rationally identifying genomic regions that are dispensable or even detrimental to bioprocessing is challenged by our inability to map genome sequence to function across complex regulation and physiology. Thus, there is a need for tools that rapidly generate reduced genome strains with improved performance in process-relevant conditions. Here, we report a Cascade-Cas3-enabled method called TRIM3 that generates large deletions by targeting a randomly integrated transposon, enabling facile generation of a genome-reduced mutant library. Mutants with improved performance were isolated following growth-coupled selection and analyzed by long-read DNA sequencing to identify deletions in their genomes. We deploy this system iteratively in the industrial host Cupriavidus necator H16 on fructose and on formate. After two rounds of TRIM3, we isolate a strain containing a total reduction of 1.4 Mb (18.4% of the genome) that grows 25% faster in a bioreactor on fructose and a strain with a total reduction of 0.5 Mb (7.3% of the genome) that grows 14% faster on formate. This work demonstrates a method for random, iterative, growth-selectable genome reduction that represents a new avenue for large-scale genome modifications and the development of improved bioprocessing hosts.

09 BIOMASS FUELS↗

DOE OSTI2025

Investigating the Effect of Water on the Mechanical Properties of Cellulose from Multiscale Molecular Dynamics Simulations

Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.

BASIC BIOLOGICAL SCIENCES,RADIATION PROTECTION AND↗

DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗

DOE OSTI2024

Stressful crystal histories recorded around melt inclusions in volcanic quartz

Abstract Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals across studied magma systems preserve similar and relatively small magnitudes of elastic residual stress (mean 53–135 MPa, median 46–116 MPa) in comparison to the strength of quartz (~ 10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~ 100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption.

58 GEOSCIENCES↗

DOE OSTI2020

Eulerian thermo-mechanical simulations of heterogeneous solid propellants using an approximate projection method

We present a mathematical model that describes thermo-mechanical deformations and thermal gradients on the unsteady burning of a heterogeneous solid propellant. A scaling study shows that the deformations in solid at combustion timescales can be treated as quasi-static. The resulting thermo-mechanical formulation is formulated on a Cartesian grid and makes use of a weak form of Chorin-type projection method to deal with large difference in shear modulus of constituent materials. Further, a one-dimensional verification study is carried out by comparing numerical simulations with those of an analytical model. In addition, convergence studies for a two-dimensional propellant sandwich configuration are presented for the stress, velocity, and reference map components. Finally, simulations are carried out for a two-dimensional random propellant pack and the time-averaged burn rate and strains are reported.

42 ENGINEERING↗

DOE OSTI2021

Investigating Fracture Network Deformation Using Noble Gas Release

We investigate deformation mechanics of fracture networks in unsaturated fractured rocks from subsurface conventional detonation using dynamic noble gas measurements and changes in air permeability. We dynamically measured the noble gas isotopic composition and helium exhalation of downhole gas before and after a large subsurface conventional detonation. These noble gas measurements were combined with measurements of the subsurface permeability field from 64 discrete sampling intervals before and after the detonation and subsurface mapping of fractures in borehole walls before well completion. We saw no observable increase in radiogenic noble gas release from either an isotopic composition or a helium exhalation point of view. Large increases in permeability were observed in 13 of 64 discrete sampling intervals. Of the sampling intervals which saw large increases in flow, only two locations did not have preexisting fractures mapped at the site. Given the lack of noble gas release and a clear increase in permeability, we infer that most of the strain accommodation of the fractured media occurred along previously existing fractures, rather than the creation of new fractures, even for a high strain rate event. These results have significant implications for how we conceptualize the deformation of rocks with fracture networks above the percolation threshold, with application to a variety of geologic and geological engineering problems.

Gardner, W. Payton↗

DOE OSTI2024

Structure and antigenicity of the divergent human astrovirus VA1 capsid spike

Human astrovirus (HAstV) is a known cause of viral gastroenteritis in children worldwide, but HAstV can cause also severe and systemic infections in immunocompromised patients. There are three clades of HAstV: classical, MLB, and VA/HMO. While all three clades are found in gastrointestinal samples, HAstV-VA/HMO is the main clade associated with meningitis and encephalitis in immunocompromised patients. To understand how the HAstV-VA/HMO can infect the central nervous system, we investigated its sequence-divergent capsid spike, which functions in cell attachment and may influence viral tropism. Here we report the high-resolution crystal structures of the HAstV-VA1 capsid spike from strains isolated from patients with gastrointestinal and neuronal disease. The HAstV-VA1 spike forms a dimer and shares a core beta-barrel structure with other astrovirus capsid spikes but is otherwise strikingly different, suggesting that HAstV-VA1 may utilize a different cell receptor, and an infection competition assay supports this hypothesis. Furthermore, by mapping the capsid protease cleavage site onto the structure, the maturation and assembly of the HAstV-VA1 capsid is revealed. Finally, comparison of gastrointestinal and neuronal HAstV-VA1 sequences, structures, and antigenicity suggests that neuronal HAstV-VA1 strains may have acquired immune escape mutations. Overall, our studies on the HAstV-VA1 capsid spike lay a foundation to further investigate the biology of HAstV-VA/HMO and to develop vaccines and therapeutics targeting it.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails

New therapies are necessary to combat increasingly antibiotic-resistant bacterial pathogens. We have developed a technology platform of computational, molecular biology, and microbiology tools which together enable on-demand production of phages that target virtually any given bacterial isolate. Two complementary computational tools that identify and precisely map prophages and other integrative genetic elements in bacterial genomes are used to identify prophage-laden bacteria that are close relatives of the target strain. Phage genomes are engineered to disable lysogeny, through use of long amplicon PCR and Gibson assembly. Finally, the engineered phage genomes are introduced into host bacteria for phage production. As an initial demonstration, we used this approach to produce a phage cocktail against the opportunistic pathogen Pseudomonas aeruginosa PAO1. Two prophageladen P. aeruginosa strains closely related to PAO1 were identified, ATCC 39324 and ATCC 27853. Deep sequencing revealed that mitomycin C treatment of these strains induced seven phages that grow on P. aeruginosa PAO1. The most diverse five phages were engineered for nonlysogeny by deleting the integrase gene (int), which is readily identifiable and typically conveniently located at one end of the prophage. The Δint phages, individually and in cocktails, killed P. aeruginosa PAO1 in liquid culture as well as in a waxworm (Galleria mellonella) model of infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Informed unsupervised machine learning analysis of dislocation microstructure from high-resolution differential aperture X-ray structural microscopy data

This study leverages high-resolution differential-aperture X-ray structural microscopy (DAXM) to probe the local dislocation structure in deformed 304L-stainless steel at small strain, by measuring the lattice rotation and deviatoric elastic strain with a sub-micron resolution. For a single grain in a polycrystalline specimen, the measured lattice rotation field over the measured volume exhibited a multimodal distribution while the deviatoric elastic strain showed a single-mode distribution. An unsupervised Cauchy mixture machine learning model was developed to resolve the multimodal distribution of the lattice rotation. By mapping the lattice rotation data associated with each Cauchy peak in the model back onto the measured volume, we identify contiguous regions of the crystal rotated near the average values corresponding to the peaks of the overall rotation distribution. These regions represent the grain subdivision in the microstructure. Finally, the dislocation density tensor was also computed and its norm was laid over the rotation field to detect the subgrain boundaries. This step provided a validation of the Cauchy mixture model for the analysis of the lattice rotation distribution. The current study highlights the integration of advanced X-ray microscopy techniques with data-driven analysis methods to uncover detailed microstructure scales in deformed crystals.

Machine learning; Lattice rotation; High-energy X-↗

DOE OSTI2021

Multiwavelength anomalous X-ray diffraction for combined imaging of atomic displacement and strain

The X-ray Bragg coherent diffractive imaging (CDI) technique assumes that the structure factor holds constant over the measured crystal. This approximation breaks down for materials exhibiting variations in the unit-cell configuration, such as piezo- and ferroelectrics. In that case, the strain field cannot be reliably determined from the reconstruction because the lattice deformation and the structure factor contribute concomitantly. Proposed here is a solution to this problem achieved by combining Bragg CDI and the multiwavelength anomalous diffraction approach that measures a Friedel pair of reflections at two different photon energies near an absorption edge. Comparing the obtained reconstructions with a parametric model that includes calculating the scattering amplitude as a function of wavelength and the unit-cell configuration, the contributions of the lattice deformation and the structure factor are separated. Simulations of the ferroelectric material BaTiO 3 demonstrate the possibility of simultaneous probing of the strain and displacement of the Ti atoms. The proposed method opens up an opportunity to apply coherent X-ray diffraction for nanoscale-resolved 3D mapping of polarization domains in micro- and nanocrystals.

lattice deformation↗

DOE OSTI2022

The EGS Collab Project – Stimulations at Two Depths

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.

EGS Collab, Enhanced Geothermal Systems, EGS, fiel↗