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DOE OSTI2024

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Geochemistry & Geophysics↗

DOE OSTI2026

Generating entangled steady states in multistable open quantum systems via initial state control

Entanglement underpins the power of quantum technologies, yet it is fragile and typically destroyed by dissipation. Paradoxically, the same dissipation, when carefully engineered, can drive a system toward robust entangled steady states. However, this engineering task is nontrivial, as dissipative many-body systems are complex, particularly when they support multiple steady states. Here, we derive analytic expressions that predict how the steady state of a system evolving under a Lindblad equation depends on the initial state, without requiring integration of the dynamics. These results extend Refs. [V. V. Albert and L. Jiang, Phys. Rev. A 89, 022118 (2014); V. V. Albert et al., Phys. Rev. X 6, 041031 (2016)], showing that while the steady-state manifold is determined by the Liouvillian kernel, the weights within it depend on both the Liouvillian and the initial state. We identify a special class of Liouvillians for which the steady state depends only on the initial overlap with the kernel. Our framework provides analytical insight and a computationally efficient tool for predicting steady states in open quantum systems. As an application, we propose schemes to generate metrologically useful entangled steady states in spin ensembles via balanced collective decay.

Dissipative dynamics↗

DOE OSTI2024

A Novel Framework to Quantify Power Grid Resilience

The quantification of an operating power grid’s resilience is highly significant today, given its criticality as an enabler of other infrastructures, complexity, and the threat it faces due to a wide range of detrimental events, from extreme climate to cyber attacks. Currently, there exist no standardized definitions and metrics for measuring the resilience of an operating grid. In this paper, we introduce a novel resilience quantification framework and demonstrate a method to measure the flexibility towards topological/structural changes due to potential failures in the power grid to assess operational resilience. We start with the state estimation data from a large utility and use the graph analysis methods and power flow simulation tools to compute the identified resilience parameters.

Yoginath, Srikanth↗

DOE OSTI2025

Entanglement Benchmarking in Quantum Simulations of Spin Systems

We simulate quantum spin systems and measure entanglement using circuits tailored for near-term quantum computers. Traditional tools like entanglement entropy are limited to pure states and require full state tomography, making them impractical on current hardware. Instead, we employ the novel approach, Positive Partial Transpose (PPT) criterion to efficiently detect pairwise entanglement from two-spin reduced density matrices, applicable to both pure and mixed states. This method enables scalable entanglement detection, providing a practical route to study quantum correlations, phase transitions, and benchmark quantum devices.

Baul, Anshumitra [ORNL] (ORCID:0000000268947191)↗

DOE OSTI2023

A Prediction Method for Catchment Efficiency Loss due to Coaxial Nozzle Wear in Powder Fed Directed Energy Deposition Systems

Abstract Powder fed laser directed energy deposition systems show significant promise and their widespread adoption could greatly advance manufacturing technology; however, a significant amount of gas atomized metal powder is wasted during material deposition. As contaminated powder must be collected and disposed of as hazardous waste, this process is often criticized for its low catchment efficiency. This presents potential implementation and scaling issues, as widespread use of this technology would directly increase the amount of contaminated powder in need of safe disposal. A direct contribution to this waste issue was identified in past work as wearing exterior and interior surfaces on a replaceable nozzle component within the additive head of such a system integrated into a hybrid manufacturing machine tool. A computational fluid dynamics simulation is developed specifically for use within the manufacturing process by predicting catchment efficiency losses before a deposition operation is performed. The model is robust enough to calculate a catchment efficiency estimate within a reasonable transition time before deposition and is designed to replace visual estimation of nozzle deterioration by the machine operator. Both of two previously identified wear modes are represented in the simulation. Their effects on powder stream geometry are discussed in detail and the results are compared with in-situ measurements of powder stream geometry and concentrations. The resulting work contributes to the growing understanding of using prediction and prevention methods on catchment efficiency related part defects within these systems.

DeWitte, Lisa↗

DOE OSTI2021

Dynamic Interconversion of Metal Active Site Ensembles in Zeolite Catalysis

Catalysis science is founded on understanding the structure, number, and reactivity of active sites. Kinetic models that consider active sites to be static and noninteracting entities are routinely successful in describing the behavior of heterogeneous catalysts. Yet, active site ensembles often restructure in response to their external environment and even during steady-state catalytic turnover, sometimes requiring non-mean-field kinetic treatments to describe distance-dependent interactions among sites. Such behavior is being recognized more frequently in modern catalysis research, with the advent of experimental methods to quantify turnover rates with increasing precision, an expanding arsenal of operando characterization tools, and computational descriptions of atomic structure and motion at chemical potentials and timescales increasingly relevant to reaction conditions. This review focuses on dynamic changes to metal active site ensembles on zeolite supports, which are silica-based crystalline materials substituted with Al that generate binding sites for isolated and low-nuclearity metal site ensembles. Metal sites can become solvated and mobilized during reaction, facilitating interactions among sites that change their nuclearity and function. Such intersite communication can be regulated by the zeolite support, resulting in non-single-site and potentially non-mean-field kinetic behavior arising from mechanisms of catalytic action that combine elements of those canonically associated with homogeneous and heterogeneous catalysis. Here, we discuss recent literature examples that document dynamic active site behavior in metal-zeolites and outline methodologies to identify and interpret such behavior. We conclude with our outlook on future research directions to develop this evolving branch of catalysis science and harness it for practical applications.

operando characterization↗

DOE OSTI2018

Lagomorph

Existing tools for computational anatomy and image registration are not aimed at HPC environments. As such they are not easily adapted to multimode platforms and contain inefficient batch processing modes. Lagomorph addresses this by providing an easy to install and use pure python implementation of very efficient image registration and atlas building which is aimed at extreme-scale anatomical studies using modern HPC and GPU resources.

Hinkle, JacobD↗

DOE OSTI2020

Automatic Report Generator (ARG)

ARG is a computational simulation tool. It generates user-configurable reports, including graphs and images, from user data in various formats.SAND2019-9525 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Clay, Robert↗

DOE OSTI2024

UOWDetection

The code provides machine learning, data collection, and computer science tools used to detect undocumented orphaned wells in United States from aerial imagery.

Kim, Anastasiia↗

DOE OSTI2024

Anchoring

This software provides methods and functions for training deep image classification models based on the principle of anchoring. It features a user-friendly PyTorch wrapper that facilitates the easy conversion of any model into an anchored model. The software supports various standard datasets and includes scripts for conducting evaluations. Developed with PyTorch, it is compatible with common neural network architectures used for image data. Additionally, it offers tools for computing evaluation metrics to assess model performance.

Narayanaswamy, Vivek Sivaraman↗

DOE OSTI2025

MJO CP Tensor Decompositions

SAND2025-14273O MJO CP Tensor Decompositions is a Python tool for computing canonical polyadic (CP) decompositions of MERRA2 observational data to understand the onset and evolution of the Madden-Julian Oscillation (MJO). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Bull, Diana [Sandia National Lab. (SNL-CA), Liverm↗

DOE OSTI2017

Real-Space Multiple-Scattering Theory and Its Applications at Exascale

In recent decades, the ab initio methods based on density functional theory (DFT) (Hohenberg and Kohn 1964, Kohn and Sham 1965) have become a widely used tool in computational materials science, which allows theoretical prediction of physical properties of materials from the first principles and theoretical interpretation of new physical phenomena found in experiments. In the framework of DFT, the original problem that requires solving a quantum mechanical equation for a many-electron system is reduced to a one-electron problem that involves an electron moving in an effective field, while the effective field potential is made up of an electrostatic potential, also known as Hartree potential, arising from the electronic and ion charge distribution in space and an exchange–correlation potential, which is a function of the electron density and encapsulates the exchange and correlation effects of the many-electron system. Even though the exact functional form of the exchange-correlation potential is formally unknown, a local density approximation (LDA) or a generalized gradient approximation (GGA) is usually applied so that the calculation of the exchange–correlation potential, as well as the exchange–correlation energy, becomes tractable while a required accuracy is retained. Based on DFT, ab initio electronic structure calculations for a material generally involve a self-consistent process that iterates between two computational tasks: (1) solving an one-electron Schrödinger equation, also known as Kohn–Sham equation, to obtain the electron density and, if needed, the magnetic moment density, and (2) solving the Poisson equation to obtain the electrostatic potential corresponding to the electron density and constructing the effective potential by adding the exchange–correlation potential to the electrostatic potential. This self-consistent process proceeds until a convergence criteria is reached.

Eisenbach, Markus↗

DOE OSTI2016

Process Modeling for Additive Manufacturing

Additive Manufacturing (AM) provides a new avenue to design innovative materials and components that cannot be created using traditional machining operations. With current AM capabilities, complex designs (such as those required in weapon systems) can be readily manufactured with laser powder forming (or Laser-Engineered Net Shaping (LENSTM)) [1] that would be otherwise cost prohibitive or impossible to produce. However, before an AM product can be qualified for weapon applications, the characteristics of the metals produced by additive manufacturing processes need to be well understood. This work focuses on the development of computational simulation tools to model the metal additive manufacturing process. This work extends and integrates existing Sandia National Laboratories tools to accomplish the following: (i) be able to better predict residual stresses in AM product, (ii) extend high-fidelity material models to capture material evolution during the formation process, leading to prediction of end-state material properties, and (iii) provide a basis for engineering tools to propose improvements to additive manufacturing process variables, including those that minimize process variation. While this work in its current state is directly applicable to additive manufacturing processes, the tools developed may also help enable modeling welding processes such as gas tungsten arc (GTA), electron beam, and laser welding.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2021

Assimilation of Multiscale Data into Multifidelity Biogeochemical Models (Final Report)

Quantitative predictions of subsurface processes rely on computational models that capture, with different degrees of fidelity, complex interactions between hydrologic and biogeochemical processes. Molecular- and pore-scale models provide a high-fidelity representation of these processes but are impractical at the field scale. Reduced complexity (e.g., field-scale or data-driven) models sacrifice some degree of fidelity in favor of computational efficiency. Uncertainty and assimilation of data into model predictions, pose a question of model selection: Given a significant difference in computational cost, can a lower-fidelity model be preferable to its higher-fidelity counterpart? Since a predictive model must be computable in reasonable time it has to operate at the field scale, with molecular- and pore-scale data (obtained either from simulations or measurements) determining both the model's structure and parameters. Availability of such multi-resolution data raises a question hitherto undressed in hydrogeology: Can a coarse model dynamically ``learn'' its own structure (e.g., adjust the reaction pathways in its transport module) as more fine-scale data become available during simulations? Our results in multifidelity simulations, data assimilation, and machine learning led us to hypothesize that the answer to these questions is ``yes''. The overarching goal of this project was to confirm this hypothesis by developing scalable, computationally efficient tools for assimilation of data into multiscale models, in which fine-scale data and simulations dynamically inform and autonomously modify coarse-scale models. Our research led to nine manuscripts, three of which have been published and the other six are currently under review.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Modeling the Effect of Film Morphology on the Performance of an OLED Device

Organic Light Emitting Diode (OLED) technology is replacing the liquid crystal displays (LCD) in cell phones and is also expected to impact television displays in the future. Dow has an active OLED research program. To complement and ultimately drive this effort, it is necessary to develop efficient computational screening tools for selection and optimization of target molecules. To this end we sought to identify and expand on existing models used by Dow that can better predict the mobility of electrons and holes in organic materials. We focused on N,N'- bis(1-naphthyl)-N,N'-diphenyl-1,1'-biphenyl-4,4'-diamine (NPD), frequently used in academic studies of organic light-emitting diodes; we also studied 4,4'-Bis(N-carbazolyl)-1,1'-biphenyl (CBP), tris(4-carbazoyl-9-ylphenyl)amine (TCTA), and bathophenanthroline (BPhen). We developed a workflow, using a combination of molecular dynamics and quantum chemistry calculations, to predict trends in the electronic structure that correlate with measured electron and hole mobilities of small molecule materials such as NPD for OLEDs, comparing with measurements in the literature and at Dow. This work comprised a first step toward predictive charge carrier mobilities in small-molecule electronic materials with complex morphologies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

LocOO3D User's Manual

Loc003D is a software tool that computes geographical locations for seismic events at regional to global scales. This software has a rich set of features, including the ability to use custom 3D velocity models, correlated observations and master event locations. The Loc003D software is especially useful for research related to seismic monitoring applications, since it allows users to easily explore a variety of location methods and scenarios and is compatible with the CSS3.0 software format used in monitoring applications. The Loc003D software is available on the web at: www.sandia.gov/salsa3d/Software.html The software is packaged with this user's manual and a set of example datasets, the use of which is described in this manual.

58 GEOSCIENCES↗

DOE OSTI2019

pCalc User's Manual

pCalc is a software tool that computes travel-time predictions, ray path geometry and model queries. This software has a rich set of features, including the ability to use custom 3D velocity models to compute predictions using a variety of geometries. The pCalc software is especially useful for research related to seismic monitoring applications. The pCalc software is available on the web at: www.sandia.gov/salsa3d/Software.html The software is packaged with this user's manual and a set of example datasets, the use of which is described in this manual.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

LocOO3D User's Manual

LocOO3D is a software tool that computes geographical locations for seismic events at regional to global scales. This software has a rich set of features, including the ability to use custom 3D velocity models, correlated observations and master event locations. The LocOO3D software is especially useful for research related to seismic monitoring applications, since it allows users to easily explore a variety of location methods and scenarios and is compatible with the CSS3.0 data format used in monitoring applications. The LocOO3D software, User's Manual, and Examples are available on the web at: https://github.com/sandialabs/LocOO3D For additional information on GeoTess, SALSA3D, RSTT, and other related software, please see: https://github.com/sandialabs/GeoTessJava, www.sandia.gov/geotess, www.sandia.gov/salsa3d, and www.sandia.gov/rstt

58 GEOSCIENCES↗