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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Sustainability of water uses in managed hydrosystems: human- and climate-induced changes for the mid-21st century

This paper assesses the sustainability of planned water uses in mesoscale river basins under multiple climate change scenarios, and contributes to determining the possible causes of unsustainability. We propose an assessment grounded in real-world water management issues, with water management scenarios built in collaboration with local water agencies. Furthermore, we present an analysis through indicators that relate to management goals and present the implications of climate uncertainty for our results, furthering the significance of our study for water management. A modeling framework integrating hydro-climatic and human dynamics and accounting for interactions between resource and demand was applied in two basins of different scales and with contrasting water uses: the Herault (2500 km 2 , France) and the Ebro (85 000 km 2 , Spain) basins. Natural streamflow was evaluated using a conceptual hydrological model. A demand-driven reservoir management model was designed to account for streamflow regulations from the main dams. Human water demand was estimated from time series of demographic, socioeconomic and climatic data. Environmental flows were accounted for by defining streamflow thresholds under which withdrawals were strictly limited. Finally indicators comparing water availability to demand at strategic resource and demand nodes were computed. This framework was applied under different combinations of climatic and water use scenarios for the mid-21st to differentiate the impacts of climate- and human-induced changes on streamflow and water balance. Results showed that objective monthly environmental flows would be guaranteed in current climate conditions in both basins, yet in several areas this could imply limiting human water uses more than once every 5 years. The impact of the tested climate projections on both water availability and demand could question the water allocations and environmental requirements currently planned for the coming decades. Water shortages for human use could become more frequent and intense, and the pressure on water resources and aquatic ecosystems could intensify. Furthermore, the causes of unsustainability vary across sub-basins and scenarios, and in most areas results are highly dependent on the climate change scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Component separation of a isotropic Gravitational Wave Background

A Gravitational Wave Background (GWB) is expected in the universe from the superposition of a large number of unresolved astrophysical sources and phenomena in the early universe. Each component of the background (e.g., from primordial metric perturbations, binary neutron stars, milli-second pulsars etc.) has its own spectral shape. Many ongoing experiments aim to probe GWB at a variety of frequency bands. In the last two decades, using data from ground-based laser interferometric gravitational wave (GW) observatories, upper limits on GWB were placed in the frequency range of 0∼ 50−100 Hz, considering one spectral shape at a time. However, one strong component can significantly enhance the estimated strength of another component. Hence, estimation of the amplitudes of the components with different spectral shapes should be done jointly. Here we propose a method for 'component separation' of a statistically isotropic background, that can, for the first time, jointly estimate the amplitudes of many components and place upper limits. The method is rather straightforward and needs negligible amount of computation. It utilises the linear relationship between the measurements and the amplitudes of the actual components, alleviating the need for a sampling based method, e.g., Markov Chain Monte Carlo (MCMC) or matched filtering, which are computationally intensive and cumbersome in a multi-dimensional parameter space. Using this formalism we could also study how many independent components can be separated using a given dataset from a network of current and upcoming ground based interferometric detectors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

TU-G-BRD-02: Automated Systematic Quality Assurance Program for Radiation Oncology Information System Upgrades

Purpose: To: (1) describe an independent, automated, systematic software-based protocol for verifying clinical data accuracy/integrity for mitigation of data corruption/loss risks following radiation oncology information system (ROIS) upgrades; and (2) report on application of this approach in an academic/community practice environment. Methods: We propose a robust approach to perform quality assurance on the ROIS after an upgrade, targeting four data sources: (1) ROIS relational database; (2) ROIS DICOM interface; (3) ROIS treatment machine data configuration; and (4) ROIS-generated clinical reports. We investigated the database schema for differences between pre-/post-upgrade states. Paired DICOM data streams for the same object (such as RT-Plan/Treatment Record) were compared between pre-/post-upgrade states for data corruption. We examined machine configuration and related commissioning data files for changes and corruption. ROIS-generated treatment appointment and treatment parameter reports were compared to ensure patient encounter and treatment plan accuracy. This protocol was supplemented by an end-to-end clinical workflow test to verify essential ROI functionality and integrity of components interfaced during patient care chain of activities. We describe the implementation of this protocol during a Varian ARIA system upgrade at our clinic. Results: We verified 1,638 data tables with 2.4 billion data records. For 222 under-treatment patients, 605 DICOM RT plans and 13,480 DICOM treatment records retrieved from the ROIS DICOM interface were compared, with no differences in fractions, doses delivered, or treatment parameters. We identified 82 new data tables and 78 amended/deleted tables consistent with the upgrade. Reports for 5,073 patient encounters over a 2-week horizon were compared and were identical to those before the upgrade. Content in 12,237 xml machine files was compared, with no differences identified. Conclusion: An independent QA/validation approach for ROIS upgrades was developed and implemented at our clinic. The success of this approach ensures a robust QA of ROIS upgrades without manual paper/electronic checks and associated intensive labor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events

Climate and earth system models have limited skill in predicting water cycle and associated extremes such as intense rain- and wind-producing storms and resulting hazards across all timescales. Water cycle predictions are inherently limited by the predictability of the chaotic system, but large potential for improving predictions remains untapped due to limited use of data-model integration to improve predictive modeling. This white paper proposes a hybrid data-physics paradigm based on physics-informed neural networks (PINNs) to improve the Energy Exascale Earth System Model (E3SM). In particular, we aim to predict and quantify uncertainties of water cycle processes and low probability high impact events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.

36 MATERIALS SCIENCE↗
DOE OSTI2016

WE-AB-BRA-12: Post-Implant Dosimetry in Prostate Brachytherapy by X-Ray and MRI Fusion

Purpose: For post-implant dosimetric assessment after prostate brachytherapy, CT-MR fusion approach has been advocated due to the superior accuracy on both seeds localization and soft tissue delineation. However, CT deposits additional radiation to the patient, and seed identification in CT requires manual review and correction. In this study, we propose an accurate, low-dose, and cost-effective post-implant dosimetry approach based on X-ray and MRI. Methods: Implanted seeds are reconstructed using only three X-ray fluoroscopy images by solving a combinatorial optimization problem. The reconstructed seeds are then registered to MR images using an intensity-based points-to-volume registration. MR images are first pre-processed by geometric and Gaussian filtering, yielding smooth candidate seed-only images. To accommodate potential soft tissue deformation, our registration is performed in two steps, an initial affine followed by local deformable registrations. An evolutionary optimizer in conjunction with a points-to-volume similarity metric is used for the affine registration. Local prostate deformation and seed migration are then adjusted by the deformable registration step with external and internal force constraints. Results: We tested our algorithm on twenty patient data sets. For quantitative evaluation, we obtained ground truth seed positions by fusing the post-implant CT-MR images. Seeds were semi-automatically extracted from CT and manually corrected and then registered to the MR images. Target registration error (TRE) was computed by measuring the Euclidean distances from the ground truth to the closest registered X-ray seeds. The overall TREs (mean±standard deviation in mm) are 1.6±1.1 (affine) and 1.3±0.8 (affine+deformable). The overall computation takes less than 1 minute. Conclusion: It has been reported that the CT-based seed localization error is ∼1.6mm and the seed localization uncertainty of 2mm results in less than 5% deviation of prostate D90. The average error of 1.3mm with our system outperforms the CT-based approach and is considered well within the clinically acceptable limit. Supported in part by NIH/NCI grant 5R01CA151395. The X-ray-based implant reconstruction method (US patent No. 8,233,686) was licensed to Acoustic MedSystems Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Bioplastic design using multitask deep neural networks

Abstract Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.

36 MATERIALS SCIENCE↗
DOE OSTI2014

TU-A-9A-10: Verification of Photoacoustic Computed Tomography Perfusion Imaging Using DCE-CT

Purpose: We propose to verify quantifiable perfusion information generated by a Photoacoustic Computed Tomography (PCT) scanner using Dynamic Contrast-Enhanced CT (DCE-CT), and to investigate physicsbased models of acoustic properties of tissue and photon transport to improve quantification. These corrections first necessitate a skin identifying algorithms to reduce speed-of-sound blurring and empirical photon correction methods. Methods: Xenograft mice (n=5) of breast cancer was imaged using DCE-CT which was followed by DCE-PCT. To obtain CT perfusion data, each mouse was i.v. injected (0.2mL Isovue @0.5mL/min) and subsequent radio-opaque time curves fit to a 2-compartmental model on a voxel-wise basis. For DCE-PCT, different concentrations of ICG (250, 125, and 62.5 micro-Molar) were injected at the same rate, but also acquired at different sampling rates (3, 6, and 12 seconds). The time intensity curves from PCT were fit to a 1-compartmental model on a voxel by voxel basis. The images were coregistered (Oncentra) based on the structural similarities of the tumor vasculature after which we compared both the contrastenhanced dynamics and the vascular physiology. Results: Moderate to high doses of ICG impact the washin phase of the PCT contrast due to photon losses as a function of depth. A semi-automatic algorithm has been developed to identify the skin margin, and subsequent MC and empirical models of photon transport and variations in speed-of-sound are being evaluated. Conclusion: From our results we find that there is a need to apply photon and speed-of-sound corrections to our PCT data to improve the quantifiable image data at depth in the tumor for PCT. The dose and injection rate may help in reducing large systematic effects. Our project is partially funded by a NIH SBIR grant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Development of TDLAS for Water Vapor Concentration Measurement during VHTR Steam Ingress

Very-high-temperature reactors (VHTR) utilize a graphite-moderated, helium-cooled design that provides higher efficiencies and a low-carbon source of process heat (>700 deg. C) for many industrial applications. Safety concerns have led to extensive accident condition investigations. Steam generator heat-exchanger tube rupture accidents (SGTR) can lead to increased reactor core steam density, which causes neutron slowing-down and chemical corrosion of the graphite fuel elements and structural columns. It is important to be able to computationally model the propagation of steam throughout the lower plenum and nuclear reactor core, but experimental data is needed to validate such models. The primary objective of this project is to instrument a scaled down experiment of a VHTR designed by Ohio State University (OSU), with tunable diode laser absorption spectroscopy (TDLAS) diagnostics to measure steam concentration changes and map how water vapor propagates through the test facility's lower plenum and core. The basic principle of absorption spectroscopy is based on a molecular species ability to absorb photons of specific energies. A collimated laser beam of known energy is passed through a test section containing a sample gas. When a transition of the absorbing gas species resonates with the laser frequency, a certain amount of energy will be absorbed based on the rovibrational characteristics of the molecule. The intensity of the transmitted laser light can be measured by a detector after exiting the test section. The Beer-Lambert Law of absorbance is used to recover this information relating beam attenuation to the system dimensions, gas composition, pressure, and temperature. The line-strength is a function of temperature alone and relates the fraction of molecules in a given rovibrational state through the partition function. The partition function describes the statistical properties of a system in thermodynamic equilibrium where the states obey a Boltzmann distribution. Thus, the number density of molecules in a particular state can be determined from the total internal partition function at a particular temperature. The lineshape function of a given absorption transition is derived from the physical interaction between the absorbing species and is dependent on temperature and pressure. Random thermal motion of absorbing molecules result in broadening of the linewidth, called Doppler broadening, which follows a Gaussian distribution. Molecular collisions result in broadening and shifting of the linewidth, called pressure broadening, which follows a Lorentzian distribution. The overall lineshape results in a Voigt profile, which is a convolution of the Gaussian and Lorentzian distributions. The Voigt profile does not have a simple analytical form and is most often calculated numerically or approximated by complex functions. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Split-field vs extended-field intensity-modulated radiation therapy plans for oropharyngeal cancer: Which spares the larynx? Which spares the thyroid?

Radiation of the low neck can be accomplished using split-field intensity-modulated radiation therapy (sf-IMRT) or extended-field intensity-modulated radiation therapy (ef-IMRT). We evaluated the effect of these treatment choices on target coverage and thyroid and larynx doses. Using data from 14 patients with cancers of the oropharynx, we compared the following 3 strategies for radiating the low neck: (1) extended-field IMRT, (2) traditional split-field IMRT with an initial cord-junction block to 40 Gy, followed by a full-cord block to 50 Gy, and (3) split-field IMRT with a full-cord block to 50 Gy. Patients were planned using each of these 3 techniques. To facilitate comparison, extended-field plans were normalized to deliver 50 Gy to 95% of the neck volume. Target coverage was assessed using the dose to 95% of the neck volume (D{sub 95}). Mean thyroid and larynx doses were computed. Extended-field IMRT was used as the reference arm; the mean larynx dose was 25.7 ± 7.4 Gy, and the mean thyroid dose was 28.6 ± 2.4 Gy. Split-field IMRT with 2-step blocking reduced laryngeal dose (mean larynx dose 15.2 ± 5.1 Gy) at the cost of a moderate reduction in target coverage (D{sub 95} 41.4 ± 14 Gy) and much higher thyroid dose (mean thyroid dose 44.7 ± 3.7 Gy). Split-field IMRT with initial full-cord block resulted in greater laryngeal sparing (mean larynx dose 14.2 ± 5.1 Gy) and only a moderately higher thyroid dose (mean thyroid dose 31 ± 8 Gy) but resulted in a significant reduction in target coverage (D{sub 95} 34.4 ± 15 Gy). Extended-field IMRT comprehensively covers the low neck and achieves acceptable thyroid and laryngeal sparing. Split-field IMRT with a full-cord block reduces laryngeal doses to less than 20 Gy and spares the thyroid, at the cost of substantially reduced coverage of the low neck. Traditional 2-step split-field IMRT similarly reduces the laryngeal dose but also reduces low-neck coverage and delivers very high doses to the thyroid.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2026

Computer vision-based rock bolt detection in orthomosaic imagery obtained in the Waste Isolation Pilot Plant underground facility

Assessing structural integrity of large underground tunnel facilities is often a time consuming and human-labor intensive task. Thus, research using various modes of sensing and automated detection of key structural components in mines is posed to aid in safety assessments and establishing overall structural health. We propose an approach utilizing off-the-shelf camera and lidar technology fixed to a custom sensing platform, image stitching techniques, fine-tuned object detection models, and specialized model-inference methods to automatically detect, count, and map roof bolts for assessment of structural safety in man-made underground tunnels. Results show a novel workflow for effective object counting in orthomosaic tunnel ceiling images generated from collections in GPS-denied mining environments. Additionally, we demonstrate effective fine-tuning of EfficientDet object detectors utilizing state-of-the-art image augmentation techniques known as the mosaic and mixup transformations. Our work is demonstrated on sensed data and imagery collected from the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) where miles of tunnel ceiling must be assessed for structural integrity.

42 ENGINEERING↗
DOE OSTI2023

Insights From Dayflow: A Historical Streamflow Reanalysis Dataset for the Conterminous United States

Abstract Reconstructed historical streamflow time series can supplement limited streamflow gauge observations. However, there are common challenges of typical modeling approaches: process‐based hydrologic models can be data/computation‐intensive, and statistics‐based models can be region/stream‐specific. Here we present a nationally scalable modeling framework integrating the simulated runoff from the Variable Infiltration Capacity (VIC) model with the Routing Application for Parallel computatIon of Discharge (RAPID) routing model leveraging high‐performance computing. We demonstrate an efficient method of assimilating streamflow at US Geological Survey (USGS) streamflow monitoring sites using a simple hierarchical approach in the VIC‐RAPID framework. The result is a reconstructed 36‐year (1980–2015) daily and monthly streamflow dataset (Dayflow) at ∼2.7 million NHDPlusV2 stream reaches in the conterminous US (CONUS). We perform a comprehensive evaluation at 7,526 USGS sites and characterize their error statistics. The results demonstrate that 49% of the USGS sites demonstrate Kling–Gupta Efficiency (KGE) > 0.5 and 58% of the sites show percentage bias within ±20% for the daily naturalized streamflow. Streamflow data assimilation across CONUS shows an overall improvement over naturalized streamflow, notably in the western semiarid‐to‐arid regions. Comparison to other national and global streamflow reanalysis datasets such as the National Water Model and Global Reach‐scale A priori Discharge Estimates for SWOT demonstrates improved KGE, reduced bias, and directions for Dayflow improvements. Investigations of error statistics with key hydrologic, hydroclimatic, and geomorphologic basin characteristics reveal region‐specific patterns which may help improve future framework applications. Overall, Dayflow may enable a better understanding of hydrologic conditions in a changing environment, especially in locations currently not represented by streamflow monitoring networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Climatology and Life‐Cycle Characteristics of Atmospheric Fronts and Their Associated Precipitation

Abstract Atmospheric fronts are one of the main sources of mid‐latitude variability. We employ a novel method for identifying and tracking fronts and frontal precipitation. Thermal and dynamical variables are used to identify fronts as areal objects in space, which are tracked in time using the open‐source TempestExtremes software package. Precipitation objects are co‐located to identify frontal precipitation. The method is subjected to validation and sensitivity tests using manually curated data from the National Weather Service. Climatologies of fronts and frontal precipitation are computed from reanalysis and observations; fronts are present upwards of 14% of the time in the storm tracks, and represent the majority (up to 90%) of total and extreme precipitation. Novel aspects of the method are showcased through the lifetime characteristics of fronts across North America. Three sets of warm and cold fronts were discovered, and their duration, distance‐traveled, and translation velocity are examined. Plain Language Summary Mid‐latitude low‐pressure systems and weather fronts are important for our day‐to‐day experience of weather events, particularly in the mid‐latitudes. This work makes use of standardized atmospheric data and creates a method of automatically tracking these important atmospheric features and their precipitation to quantify their relative role in global precipitation. Weather fronts are persistent in the mid‐latitudes and are associated with the majority of precipitation–particularly the most intense precipitation. Trajectories of fronts over North America are categorized to create a set of archetypal fronts that occur in that region. The differences between these types of fronts are characterized. Key Points An automated, efficient, and skillful frontal detection algorithm is developed and validated Fronts contribute a larger fraction of extreme precipitation than all precipitation in mid‐latitude storm tracks Fronts across North America have substantial variation in characteristics depending on their origin location

extratropical cyclone↗
DOE OSTI2017

Small-angle X-ray scattering characteristics of mouse brain: Planar imaging measurements and tomographic imaging simulations

Small-angle x-ray scattering (SAXS) imaging can differentiate tissue types based on their nanoscale molecular structure. However, characterization of the coherent scattering cross-section profile of relevant tissues is needed to optimally design SAXS imaging techniques for a variety of biomedical applications. Reported measured nervous tissue x-ray scattering cross sections under a synchrotron source have had limited agreement. We report a set of x-ray cross-section measurements obtained from planar SAXS imaging of 1 mm thick mouse brain (APP/PS1 wild-type) coronal slices using an 8 keV laboratory x-ray source. Two characteristic peaks were found at 0.96 and 1.60 nm -1 attributed to myelin. The peak intensities varied by location in the slice. We found that regions of gray matter, white matter, and corpus callosum could be segmented by their increasing intensities of myelin peaks respectively. Measured small-angle x-ray scattering cross sections were then used to define brain tissue scattering properties in a GPU-accelerated Monte Carlo simulation of SAXS computed tomography (CT) using a higher monochromatic x-ray energy (20 keV) to study design trade-offs for noninvasive in vivo SAXS imaging on a small-animal head including radiation dose, signal-to-noise ratio (SNR), and the effect of skull presence on the previous two metrics. Simulation results show the estimated total dose to the mouse head for a single SAXS-CT slice was 149.4 mGy. The pixel SNR was approximately 30.8 for white matter material whether or not a skull was present. In this early-stage proof-of-principle work, we have demonstrated our brain cross-section data and simulation tools can be used to assess optimal instrument parameters for dedicated small-animal SAXS-CT prototypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

From Vehicles to Systems: Understanding Freight Transportation as a Connected Energy, Infrastructure, and Operations System

The U.S. freight system may need to handle 50% more cargo by 2050. Upgrading our freight system requires modernizing capital-intensive, long-lived assets including freight trains, ports, and terminal infrastructure. NLR is advancing freight system solutions spanning ALTRIOS, the first digital twin for the full freight rail system; ALTRIOS-LIFTS, which can create digital twins of freight terminals; INFORMES, the first national model of the intermodal freight system; MARINESim, used to simulate and optimize ocean-going vessel operations; and more. These modeling and simulation tools enable data-driven decision-making across freight modes and systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Multicode benchmark on simulated Ti K-edge x-ray absorption spectra of Ti-O compounds

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret experimental spectra and draw physical insights. Recently, there has also been growing interest in building computational XAS databases to enable data analytics and machine learning applications. However, there are non-trivial differences among commonly used XAS simulation codes, both in underlying theoretical formalism and in technical implementation. Reliable and reproducible computational XAS databases require systematic benchmark studies. In this work, we benchmarked Ti K-edge XAS simulations of ten representative Ti-O binary compounds, which we refer to as the Ti-O-10 dataset, using three state-of-the-art codes: XSPECTRA, OCEAN and exciting. We systematically studied the convergence behavior with respect to the input parameters and developed a workflow to automate and standardize the calculations to ensure converged spectra. Our benchmark comparison considers a 35 eV spectral range starting from the K-edge onset, representative of widely used near-edge spectra. Quantitative comparison over this range is based on Spearman’s rank correlation score (r sp ). Our results show: (1) the two Bethe-Salpeter equation (BSE) codes (OCEAN and exciting) have excellent agreement with an average $r$ $sp$ of 0.998; (2) good agreement is obtained between the core-hole potential code (XSPECTRA) and BSE codes (OCEAN and exciting) with an average $r$ $sp$ of 0.990, and this smaller $r$ $sp$ reflects the noticeable differences in the main edge spectral shape that can be primarily attributed to the difference in the strength of the screened core-hole potential; (3) simulations from both methods overall reproduce well the main experimental spectral features of rutile and anatase, and the different treatments of the screened core-hole potential have visible impact on pre-edge intensities and the peak ratio of the main edge; (4) there exist moderate differences in the relative edge alignment of the three codes with a standard deviation of about 0.2 eV, which arise from multiple contributions including the frozen core approximation, final state effects, and different approximations used for the self-energy correction. In conclusion, our benchmark study provides important standards for first-principles XAS simulations with broad impact in data-driven XAS analysis.

36 MATERIALS SCIENCE↗