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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys

Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. Furthermore, we demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.

density functional theory↗
DOE OSTI2019

Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel

Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.

Sainju, Rajat↗
DOE OSTI2021

Nature of Oxygen Adsorption on Defective Carbonaceous Materials

Here, plane-wave density functional theory has been used to study oxygen adsorption on graphene, graphite, and (12,0) zigzag single-walled carbon nanotubes with and without Stone–Wales (SW) and single-vacancy (SV) defects to understand the role of defects on carbonaceous material reactivity. Atomic oxygen adsorption leads to the formation of an epoxide on defect-free graphene and graphite and an ether on the exterior wall of carbon nanotubes and SW-defected materials. O 2 chemisorption is endothermic on defect-free graphene and graphite and slightly exothermic on defect-free nanotubes. O 2 chemisorption energies are predicted to be –1.1 to –1.4 eV on an SW defect and –6.0 to –8.0 eV on an SV defect. An SW defect lowers the energy barriers by 0.90 and 0.50 eV for O 2 chemisorption on graphene and nanotubes, respectively. The formation of a C–O–O–C group is important for O 2 dissociation on defect-free and SW-defected materials. The energy barrier is less than 0.30 eV on an SV defect. The more reactive SW defect toward O adsorption on graphene is mostly due to the strained defective carbon atoms being able to donate more electrons to an O to form an ether. The larger 2s character in the hybrid orbitals in an ether than in an epoxide makes the ether C–O bond stronger. Stronger C–O binding on an SW-defective carbon nanotube than on a defect-free nanotube is in part due to more flexibility of the defect to release the epoxide ring strain to form an ether.

25 ENERGY STORAGE↗
DOE OSTI2015

Evaluating Printability of Buried Native EUV Mask Phase Defects through a Modeling and Simulation Approach

The availability of defect-free masks is considered to be a critical issue for enabling extreme ultraviolet lithography (EUVL) as the next generation technology. Since completely defect-free masks will be hard to achieve, it is essential to have a good understanding of the printability of the native EUV mask defects. In this work, we performed a systematic study of native mask defects to understand the defect printability caused by them. The multilayer growth over native substrate mask blank defects was correlated to the multilayer growth over regular-shaped defects having similar profiles in terms of their width and height. To model the multilayer growth over the defects, a novel level-set multilayer growth model was used that took into account the tool deposition conditions of the Veeco Nexus ion beam deposition tool. The same tool was used for performing the actual deposition of the multilayer stack over the characterized native defects, thus ensuring a fair comparison between the actual multilayer growth over native defects, and modeled multilayer growth over regular-shaped defects. Further, the printability of the characterized native defects was studied with the SEMATECH-Berkeley Actinic Inspection Tool (AIT), an EUV mask-imaging microscope at Lawrence Berkeley National Laboratory (LBNL). Printability of the modeled regular-shaped defects, which were propagated up the multilayer stack using level-set growth model was studied using defect printability simulations implementing the waveguide algorithm. Good comparison was observed between AIT and the simulation results, thus demonstrating that multilayer growth over a defect is primarily a function of a defect’s width and height, irrespective of its shape. This would allow us to predict printability of the arbitrarily-shaped native EUV mask defects in a systematic and robust manner.

36 MATERIALS SCIENCE↗