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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 OSTI2022

QGLab v0.0.1

A software program for experimenting with holographic teleportation protocol on quantum computers. The code implements the protocol that was proposed in https://arxiv.org/abs/1911.06314. The code is an end-to-end software solution that facilitates conducting the holographic teleportation experiments on state-of-the-art and emergent generations of QPUs supported by the Qiskit and tket SDKs. The code bundles all stages of an experiment as a single configurable workflow allowing faster development and experimentation cycles. Features: 1. Easy switching between Qiskit and tket quantum compilers. 2. Semi-automatic facilities for finding optimal compilation solutions beyond what Qiskit and tket provide by default. 3. Experiment resolution scaling (based on automatic jobs' batching). 4. Automatic experiment scaling over qubits. 5. Automatic readout error mitigation. 6. Automatic reproducibility analysis. 7. Standalone error-mitigation tools (randomized compiling, mitigation with estimation circuits, zero-noise extrapolation)

Shapoval, Illya↗
DOE OSTI2018

Extending Dynamic Range for Untargeted Ion Mobility-MS Workflows

Preprocessing and identification algorithms can boost the quality and reproducibility of results in untargeted ion mobility (IM)-MS workflows. For this CRADA we developed two methods for pre-processing to improve the quality of Agilent metabolomics data: i) a new saturation repair preprocessing method and ii) multi-dimensional smoothing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Nuclear Quality Assurance Certification of the MCNP-ORIGEN Activation Automation tool

The MCNP-ORIGEN Activation Automation (MOAA) tool couples MCNP and ORIGEN-S, allowing to support ATR and TREAT experiments by predicting their composition and activity before their irradiation takes place. MOAA is developed and maintained by the Irradiation Experiment Neutronics Analysis group (C-150) at INL. This poster presents the motivation behind the development of MOAA, its main workflow, as well as the software quality assurance (SQA) efforts required by LWP-13620, "Managing Information Technology Assets."

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Digital Twin for Optimizing Real-time Economy of the Integrated Energy Systems

Economic and safe operation of integrated energy systems (IES) requires real-time optimization (RTO) of the control and actions conducted on each system component. In this regard, digital twins (DTs), which consist of a physical system, a virtual system, and the data communication that occurs between the two, are essential for effective RTO. Through the data warehouse, the virtual system is constantly updated with real-time data from the physical system, and functions as the model in the optimization framework. The reduced-order model of the dynamic process model in the virtual system is used in the optimization framework. The optimization results are then returned, via the data warehouse, as control actions to the physical system. This work demonstrates the software capabilities of DT assets for an IES in the context of preparing a DT for an experimental system comprised of Idaho National Laboratory (INL)’s Thermal Energy Delivery System and battery system. For the virtual demonstration, the DTs encompass (1) a physical system, including the Modelica models of the Thermal Energy Delivery System and the battery system; (2) virtual optimization via the Optimization of Real-Time Capacity Allocation (ORCA) platform; and (3) the open-source data warehouse software DeepLynx. This work assesses the performance of ORCA, which utilizes a reduced-order model built using the Risk Analysis Virtual Environment (RAVEN) and trained on the Modelica models and real-time data pipeline through the graph database hosted in DeepLynx. The proposed optimization workflow will be an RTO model based on DTs and the data they generate.

25 ENERGY STORAGE↗
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Realization of fermionic Laughlin state on a quantum processor

Strongly correlated topological phases of matter are central to modern condensed matter physics and quantum information technology but often challenging to probe and control in material systems. The experimental difficulty of accessing these phases has motivated the use of engineered quantum platforms for simulation and manipulation of exotic topological states. Among these, the Laughlin state stands as a cornerstone for topological matter, embodying fractionalization, anyonic excitations, and incompressibility. Although its bosonic analogs have been realized on programmable quantum simulators, a genuine fermionic Laughlin state has yet to be demonstrated on a quantum processor. Here, we realize the ν = 1/3 fermionic Laughlin state on IonQ’s trapped-ion quantum computer using an efficient and scalable Hamiltonian variational ansatz with 369 two-qubit gates on a 16-qubit circuit. Employing symmetry-verification error mitigation, we extract key observables that characterize the Laughlin state, including correlation hole, bulk-edge correspondence, and topological entanglement entropy, with strong agreement to exact diagonalization benchmarks. This work demonstrates an end-to-end workflow to simulate material-intrinsic topological orders and provides a starting point to explore its dynamics and excitations on digital quantum processors.

Shen, Lingnan [Univ. of Washington, Seattle, WA (U↗
DOE OSTI2014

SU-E-T-488: An Iso-Dose Curve Based Interactive IMRT Optimization System for Physician-Driven Plan Tuning

Purpose: In treatment plan optimization for Intensity Modulated Radiation Therapy (IMRT), after a plan is initially developed by a dosimetrist, the attending physician evaluates its quality and often would like to improve it. As opposed to having the dosimetrist implement the improvements, it is desirable to have the physician directly and efficiently modify the plan for a more streamlined and effective workflow. In this project, we developed an interactive optimization system for physicians to conveniently and efficiently fine-tune iso-dose curves. Methods: An interactive interface is developed under C++/Qt. The physician first examines iso-dose lines. S/he then picks an iso-dose curve to be improved and drags it to a more desired configuration using a computer mouse or touchpad. Once the mouse is released, a voxel-based optimization engine is launched. The weighting factors corresponding to voxels between the iso-dose lines before and after the dragging are modified. The underlying algorithm then takes these factors as input to re-optimize the plan in near real-time on a GPU platform, yielding a new plan best matching the physician's desire. The re-optimized DVHs and iso-dose curves are then updated for the next iteration of modifications. This process is repeated until a physician satisfactory plan is achieved. Results: We have tested this system for a series of IMRT plans. Results indicate that our system provides the physicians an intuitive and efficient tool to edit the iso-dose curves according to their preference. The input information is used to guide plan re-optimization, which is achieved in near real-time using our GPU-based optimization engine. Typically, a satisfactory plan can be developed by a physician in a few minutes using this tool. Conclusion: With our system, physicians are able to manipulate iso-dose curves according to their preferences. Preliminary results demonstrate the feasibility and effectiveness of this tool.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

powersqueeze

powersqueeze (psqz) is a truncated power iteration library intended for high-performance computing platforms. psqz efficiently produces low-dimensional, linear measurements of graph matrix spectra by combining classical power iteration with sparse Johnson-Lindenstrauss transforms. psqz is intended to produce high-quality, fast, data-oblivious low-dimensional representations of high-dimensional sparse data such as graphs and term-document matrices. psqz is intended to replace similar workflows that depend on directly approximating a truncated eigendecomposition (e.g., the first step of spectral clustering), which is a much more expensive operation.

Priest, BenjaminW [Lawrence Livermore National Lab↗
DOE OSTI2018

Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method

Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well-calibrated model, based on a predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

ECP Workflow Survey Report 2017

The ECP applications teams were invited to participate in an online survey from April 10-17, 2017. The survey was hosted on Google Forms and consisted of questions determined by representatives from ECP Software Technology teams and the CODAR codesign center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network’s various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. Furthermore, these elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Parallel Battery: The Framework and Process for an Intelligent and Ecological Battery System and Related Services

The concept, framework, process methodology and applications of parallel battery were proposed from both virtual and real aspects.The parallel battery was an application of ACP-based parallel intelligence in battery and related energy system areas.The real battery system was running with its equivalent, and the artificial battery system was in a virtual space, in a parallel and interactive manner.The artificial battery system contained the descriptive, predictive, and prescriptive functions on the real battery and related energy systems.There was a closed-loop workflow between the real battery system and the artificial battery system, which iteratively optimizes the battery and related energy systems, leading to a new paradigm of intelligent and ecological parallel battery system management.

ACP approach↗
DOE OSTI2023

High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications

Abstract Calculations of point defect energetics with Density Functional Theory (DFT) can provide valuable insight into several optoelectronic, thermodynamic, and kinetic properties. These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches. For applications of DFT-based high-throughput computation for data-driven materials discovery, point defect properties are of interest, yet are currently excluded from available materials databases. This work presents a benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We consider three different a-posteriori correction sets implemented in an automated workflow, and evaluate the qualitative and quantitative differences among four different categories of defect information: thermodynamic transition levels, formation energies, Fermi levels, and dopability limits. We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron

Boron, an element of captivating chemical intricacy, has been surrounded by controversies ever since its discovery in 1808. The complexities of boron stem from its unique position between metals and insulators in the Periodic Table. Recent computational studies have shed light on some of the stable boron allotropes. However, the demand for multifunctionality necessitates the need to go beyond the stable phases into the realm of metastability and explore the potentially vast but elusive metastable phases of boron. Traditional search for stable phases of materials has focused on identifying materials with the lowest enthalpy. Here, we introduce a workflow that uses reinforcement learning coupled with decision trees, such as Monte Carlo tree search, to search for stable and metastable boron phases, with enthalpy as the objective. We discover new boron metastable phases and construct a phase diagram that locates their phase space (T, P) at different levels of metastability (ΔG) from the ground state and provides useful information on the domains of relative stability of the various stable and metastable boron phases.

42 ENGINEERING↗
DOE OSTI2012

IC Workflow Project (Final Report)

The Workflow Project is a joint research task between the CASL Advanced Modeling Applications (AMA) and the Virtual Reactor Integration (VRI) focus areas, and the Industry Council. The purpose of the project is to solicit the expertise of the IC member organizations to inform CASL staff and provide feedback for the development of CASL products. The goal is for CASL staff to understand existing workflows at end-user organizations in order to achieve compatibility of CASL products with current needs and future plans. CASL is committed to developing and delivering advanced capabilities and solutions that will allow the nuclear power industry to solve complex problems and improve the operating performance and efficiency of light water reactors. Through the R&D being performed by CASL, advanced computational methods and software is being developed and embodied in the Virtual Environment for Reactor Applications (VERA). The information collected through this project will provide additional considerations that must be included in the development of VERA to ensure that it meets the needs of the nuclear industry. As will be discussed below, key additional areas that must be considered is the usability of the system in an engineering environment including the user input, quality assurance, output of results, computational runtime, and integration into existing engineering analysis processes. This document contains a summary of information gathered through a series of interviews and meetings with IC organizations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method

Traditional trial-and-error tuning of uncertain parameters in global atmospheric General Circulation Models (GCM) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well calibrated model, based on a pre-defined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to about a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Accelerating kinetic plasma simulations with machine-learning-generated initial conditions

Computational models of plasma technologies often solve for the system operating conditions by time-stepping an initial value problem to a quasi-steady solution. However, the strongly nonlinear and multi-timescale nature of plasma dynamics often necessitate millions, or even hundreds of millions, of steps to reach convergence, reducing the effectiveness of these simulations for computer-aided engineering. We consider acceleration of kinetic plasma simulations via data-driven machine-learning-generated initial conditions, which initialize the simulations close to their final quasi-steady-state, thereby reducing the number of steps to reach convergence. Three machine-learning models are developed to predict the density and ion kinetic profiles of capacitively coupled plasma discharges relevant to the microelectronics industry. The models are trained on kinetic simulations over a range of device operating frequencies and pressures. Best performance was observed when simulations were initialized with ion kinetic profiles generated by a convolutional neural network, reducing the mean number of steps to reach convergence by 17.1× when compared to initialization with a zero-dimensional global model. We also outline a workflow for continuous data-driven model improvement and simulation speedup, with the aim of generating sufficient data for full device digital twins.

Artificial neural networks↗