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At least 289 records · Page 16

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗

Burst characteristics of advanced accident-tolerant FeCrAl cladding under temperature transient testing

Here assessment of burst characteristics of accident-tolerant fuel (ATF) claddings is essential to evaluate the safety margins of nuclear reactors, and better understanding can enable accelerated licensing for reactor concepts that include the new materials. Therefore, this study investigated the burst behavior of an ATF candidate of C26M, which is an iron–chromium–aluminum alloy (FeCrAl) under transient testing like the simulated loss-of-coolant accident (LOCA) conditions, except the water-quenching phase, in light-water reactors (LWRs). The effect of LOCA specimen length was assessed in terms of post-burst tube parameters. No critical length effect was determined on the burst pressure, burst location, and burst size measurements. Burst temperature showed larger variation likely due to its measurement approach used in this study. The post-test diametral strain was identified as the critical parameter to reduce the specimen length as compared to tube burst length or width. Postmortem optical metrology and digital image correlation were employed to determine the local strain-state during the simulated LOCA, which showed the loading path was close to equibiaxial conditions in regions away from the burst location, rather than internally pressurized conditions (or “plane-strain” tension). Pst-test microstructural characterizations of the FeCrAl revealed that ductile damage was present at the edge of the outer radial surface while the rest of the material was ruptured via cleavage at LOCA burst temperatures. Furthermore, internal grain boundary cracks were observed at locations away from the tube burst region. This behavior was considered to occur during the cooling down period of the simulated LOCA test. Overall, this study aimed to provide essential data for fuel performance code development by considering micromechanics of the deformation and failure.

36 MATERIALS SCIENCE↗

Multiscale numerical investigation of ratchet growth damage effects in PBX 9502

This paper presents results of numerical experiments conducted on the high explosive PBX 9502 to investigate how recently observed grain-scale damage mechanisms of ratchet growth affect uniaxial compression measurements. Simulations are multiscale in the sense of directly resolving grains, pores, cracks, and grain-interfaces based upon scanning electron microscope (SEM) images of damaged and undamaged samples. The combined finite-discrete element method (FDEM) is utilized to resolve both grain-scale microfracture and elastoplastic deformation of solid grains. Pristine (undamaged) and damaged microstructures are compared in simulation of unconfined compression tests of the same material from the literature. Here, the simulation results show the observed microscale mechanisms of damage, specifically microfracture predominantly around and sometimes through grains and crack-associated pore growth, can well-explain the effective degradation of strength and stiffness observed in the laboratory measurements.

36 MATERIALS SCIENCE↗

Atomic-Scale Structural Mapping of Active Sites in Monolayer PGM-Free Catalysts by Low-Voltage 4D-STEM

Two-dimensional (2D) materials have attracted a large amount of attention in both basic and applied fields, and scanning transmission electron microscopy (STEM) is often uniquely well-suited for characterizing the atomic-scale structure of these materials [1-4]. As a result, STEM is poised to significantly impact progress on platinum group metal (PGM)-free catalysts, which are currently under intense development to enable low-cost, commercially viable hydrogen fuel cells [5]. While recent advancements have resulted in fuel cell performance comparable to Pt catalysts by some measures [6], cell durability remains a significant challenge, limiting practical applications [7]. Catalytically active sites in PGM-free materials are proposed to be FeN4 complexes embedded in a graphene lattice (Fig. 1b) within layered or other larger materials, but this is still under debate largely due to the range of potential actives sites predicted by computational methods and lack of methods for directly validating these models [5]. Fundamental insights into the atomic structure and resulting degradation pathways of proposed active sites are therefore needed to fully understand and control cell performance and durability [6].2D materials typically make ideal samples for STEM, but those within PGM-free catalysts present additional challenges since these materials are often defect-rich, with a high density of edges, dopant atoms, etc., which significantly increase susceptibility to beam damage at standard operating voltages. This makes analysis of potential FeN4 active sites particularly challenging, since a large proportion of Fe exists at edge sites where beam-induced atomic displacements can prohibit high-resolution structural characterization [6]. Conventional dark-field imaging compounds this problem by producing less signal for a given dose and being less sensitive to light elements than dose-efficient phase contrast imaging techniques such as those enabled by four-dimensional (4D)-STEM [8-10] (Fig. 1a). Consequently, active site structural analysis is often left to methods such as low-resolution imaging combined with quantum chemical calculations [6], which hinders accurate determination of reaction and degradation mechanisms.Here, we demonstrate direct atomic-scale structural mapping of FeN4 sites by performing low-voltage 4D-STEM on a model PGM-free catalyst system with many exposed monolayer regions. To accomplish this, we pair a 30 keV aberration-corrected probe with a fast pixelated detector that has optimal performance at low beam voltages [11]. This enables us to simultaneously image light and heavy elements with high signal-to-noise by center-of-mass analysis (Fig. 1c) while minimizing beam-induced atomic displacements at sensitive sites. The monolayer nature of these materials additionally allows for experimental validation by direct comparison with multislice simulations [12] of model structures (Fig. 1d-e). This work demonstrates how low-voltage 4D-STEM will provide new insights into the atomic-scale structure and degradation mechanisms of active sites in PGM-free catalysts, facilitating the development of low-cost hydrogen fuel cells and other energy conversion technologies in the future [13].

Zachman, Michael↗

Chemical and steric effects in simulating noncontact atomic force microscopy images of organic molecules on a Cu (111) substrate

Noncontact mode of atomic force microscopy (nc-AFM) employing a CO-functionalized tip is a very powerful tool for studying molecular structures. However, interpreting nc-AFM images for nonplanar molecules can sometimes be problematic. To illustrate and resolve the nature of such problematic systems, we employ real-space pseudopotentials constructed within density functional theory to simulate nc-AFM images. In this work, we focus on several representative nonplanar organic molecules (pentacene, naphthanthrone, olympicene, and 6-phenylhexa-1,3,5-triynybenzene (PHTB)) on a typical substrate: the Cu (111) surface. This substrate results in significant distortions in the molecular geometries of pentacene and naphthanthrone. Including these distortions in simulated nc-AFM imaging notably improves the agreement between the simulated and measured images. In naphthanthrone, the relatively large interaction between the O atom and the Cu substrate offers a straightforward explanation for the absence of the C = O bond in the measured image. Nonplanar features such as tilting or twisting are also apparent in olympicene and PHTB. A “triangular” bright feature associated with the –CH 2 group in olympicene appears in simulated and measured nc-AFM images. This feature is directly related to the tilting angle of the molecule with respect to the Cu substrate. The “defective” benzene ring feature and the faint ellipsoidal C ≡ C feature in PHTB can be ascribed to its twisted nature. The ability to simulate such subatomic images in nc-AFM reflects the accuracy and efficiency of calculating quantum forces in real space.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

In situ characterization of laser-generated melt pools using synchronized ultrasound and high-speed X-ray imaging

Metal additive manufacturing is a fabrication method that forms a part by fusing layers of powder to one another. An energy source, such as a laser, is commonly used to heat the metal powder sufficiently to cause a molten pool to form, which is known as the melt pool. The melt pool can exist in the conduction or the keyhole mode where the material begins to rapidly evaporate. The interaction between the laser and the material is physically complex and difficult to predict or measure. Here, high-speed X-ray imaging was combined with immersion ultrasound to obtain synchronized measurements of stationary laser-generated melt pools. Furthermore, two-dimensional and three-dimensional finite-element simulations were conducted to help explain the ultrasonic response in the experiments. In particular, the time-of-flight and amplitude in pulse-echo configuration were observed to have a linear relationship to the depth of the melt pool. These results are promising for the use of ultrasound to characterize the melt pool behavior and for finite-element simulations to aid in interpretation.

42 ENGINEERING↗

Image quality, space-qualified UV interference filters

The progress during the contract period is described. The project involved fabrication of image quality, space-qualified bandpass filters in the 200-350 nm spectral region. Ion-assisted deposition (IAD) was applied to produce stable, reasonably durable filter coatings on space compatible UV substrates. Thin film materials and UV transmitting substrates were tested for resistance to simulated space effects.

Mooney, Thomas A.↗

Results of On-Orbit Testing of an Extra-Vehicular Infrared Camera Inspection System

This paper will discuss an infrared camera inspection system that has been developed to allow astronauts to demonstrate the ability to inspect reinforced carbon-carbon (RCC) components on the space shuttle as part of extra-vehicular activities (EVA) while in orbit. Presented will be the performance of the EVA camera system coupled with solar heating for inspection of damaged RCC specimens and NDE standards. The data presented was acquired during space shuttle flights STS-121 and STS-115 as well during a staged EVA from the ISS. The EVA camera system was able to detect flatbottom holes as small as 2.54cm in diameter with 25% material loss. Results obtained are shown to be comparable to ground-based thermal inspections performed in the laboratory using the same camera and simulated solar heating. Data on both the time history of the specimen temperature and the ability of the inspection system to image defects due to impact will likewise be presented.

Howell, Patricia A.↗

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗

Quantifying deformation during Zry-4 burst testing: a comparison of BISON and a combined in-situ digital image correlation and infrared thermography method

The development and verification of nuclear fuel-cladding performance codes require extensive testing to establish empirical correlations, necessitating more accelerated testing methods that can replace large validation studies. Here, in the present work, a technique was developed and implemented to experimentally quantify the relationship between temperature, cladding internal pressure, and strain in-situ during a burst test, with the specific aim of generating data for code validation and model refinement. Digital image correlation was used to measure hoop, axial, and radial strain, and infrared thermography to quantify axial temperature gradients. Several key experimental modifications to traditional LOCA burst testing were necessary, but are shown to effectively have little impact on burst temperatures. The time/temperature dependent pressure and time/spatially dependent thermal gradient data were used with BISON to simulate cladding burst and strain rates. Although the measured DIC strains reach appreciable amounts at slightly lower temperatures than BISON simulated strains, there is good agreement between the measured and simulated strains in terms of magnitude and rates leading up to burst. However, in the few seconds prior to and during burst, the BISON simulated strains are significantly lower than the measured strains, indicating the potential for model improvement. The combined experiment and simulation technique may be applied to any new cladding concept to accelerate fuel qualification.

36 MATERIALS SCIENCE↗

Data association algorithm for large-scale multi-object tracking with complex interactions

We present an online multi-object tracking algorithm to track multiple objects across a large number of image frames. Our work is motivated by the need to study evolution of nanoscale objects by transmission electron microscopy. The proposed approach is based on the existing multi-way data association tracking algorithm that is capable of tracking interacting objects with complex behaviors (i.e., merge, split, overlap, and appearance or disappearance). The multi-way data association is an offline algorithm to associate objects across all image frames at one step with a global optimization, which does not scale very well for large number of image frames. The proposed online tracking algorithm processes image frames as they arrive by detecting all objects in the newly arrived image frame and making the associations of the objects to those detected from the previous frame by the multi-way data association. This frameby-frame association scheme can cause fragmented traces of the objects that are occasionally misdetected for some image frames. We overcome this issue by allowing previously unassociated objects to be associated when the objects reappear within a fixed number of future image frames, namely the frame-delayed association. We combine the multi-way data association with the frame-delayed association to be able to track interacting objects with accurate handling of object disappearance events. The proposed method is validated through applications to simulated multi-object tracking problem and a real multi-object tracking problem. Here, the outcome of the proposed method is compared with four state-of-the-art algorithms.

36 MATERIALS SCIENCE↗

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗

The effects of microstructure on deformation twinning in Mg WE43

The interplay between microstructure and deformation twinning in a WE43-T6 Mg alloy under uniaxial compression was investigated using a combination of scanning electron microscopy with digital image correlation (SEM-DIC) and crystal plasticity finite element (CPFE) simulation. To improve understanding of the statistical characteristics of deformation twin formation, microstructural effects were characterized in over 1000 grains through metrics including the nominal Schmid Factor, grain size, geometric compatibility factor (m'), residual Burgers vector, and the strain accommodated by neighboring grains. There was a strong correlation between the nominal Schmid Factor and both twin activation and variant selection, but this was not fully deterministic. Deformation twinning also exhibited a strong dependence on existing slip and twinning in the neighboring grain and on the m' value. Within the range of grain sizes present in this condition, grain size was determined to have minimal effect on deformation twinning. Finally, statistical analysis of CPFE simulations was used to further investigate microstructural effects on twinning, and qualitatively captured the effect of the nominal Schmid Factor.

36 MATERIALS SCIENCE↗

Imaging the dynamics of initial laser-driven shocks and blowoff plasmas in polystyrene under laser-direct-drive fusion conditions

The dynamics of laser-driven shock propagation in solid ablator material, along with the concomitant processes of shinethrough and blowoff plasma plume expansion, at conditions relevant to laser-direct-drive fusion are measured using a tabletop system capable of supporting rapid dataset development with significantly higher resolution compared to existing methods. The experimental results are directly compared with current radiation-hydrodynamic simulations using the two-dimensional code . Discrepancies between experiments and simulations are evidenced and attributed to limitation of simulation to account for important processes involved in the solid-to-plasma transition. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗

Impact of Grafting Density on the Assembly and Mechanical Properties of Self-Assembled Metal–Organic Framework Monolayers

Polymer-grafted metal–organic frameworks (MOFs) can be used to form free-standing self-assembled MOF monolayers (SAMMs). Polymer chains can be introduced onto MOF surfaces through either the ligands or metal nodes using both grafting-to and grafting-from approaches. However, controlling the grafting density of polymer-grafted MOFs has not yet been achieved, because a means to control the density of grafting sites on the MOF surface has not been developed. In this study, the grafting density of polymer-grafted UiO-66 (UiO = University of Oslo) was controlled by functionalizing a portion of the Zr(IV) secondary building units (SBUs) on a UiO-66 surface with a so-called blocking agent. The remaining sites on the UiO-66 SBUs were functionalized with polymerization initiation groups, and polymers were grown from these sites to obtain particles with variable grafting densities and chain lengths that form SAMMs at an air–water interface. Even under conditions of low grafting density, these materials retain the ability to form SAMMs and their free-standing ability. Changes in particle arrangement within the monolayers were investigated using SEM imaging, and the toughness of the monolayers was evaluated using a film-on-water (FOW) method. Furthermore, coarse-grained molecular dynamics simulations were carried out to elucidate the morphology and mechanical properties of the monolayers. Findings from both experiments and simulations indicate that the toughness of SAMMs is more heavily influenced by the chain length of the grafted polymers than by the overall polymer content in the composite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Alignment of Polarization against an Electric Field in van der Waals Ferroelectrics

Polarization in ferroelectrics can be switched in the direction of an applied electric field by dipole reorientation, enabling numerous applications and fundamental phenomena. In this study, we demonstrate that, in the van der Waals (vdW) layered ferrielectric Cu In P 2 S 6 , a unique mechanism exists where polarization aligns against the direction of the applied electric field, seemingly in violation of the fundamental properties of a dipolar solid. The mechanism is the result of the electric field driving the Cu atoms unidirectionally across the vdW gaps, which is distinctively different from dipole reorientation. The crossing of Cu atoms is the fundamental process of ionic conductivity, yet it is compatible with the existence of polarization. These phenomena are confirmed by nanoscale imaging and spectroscopy of ferroelectric capacitors, coupled with dynamic density-functional-theory simulations. The symbiotic relationship of ferroelectric and ionic phenomena enables alternative approaches to control polarization and necessitates a change in perspective on nucleation, domain-wall dynamics, and other ferroelectric and electromechanical characteristics in material systems where ionic and ferroelectric phenomena manifest.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗