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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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At least 289 records · Page 16

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DOE OSTI2020

Electron transport in gaseous detectors with a Python-based Monte Carlo simulation code

Understanding electron drift and diffusion in gases and gas mixtures is a topic of central importance for the development of modern particle detection instrumentation. The industry-standard MagBoltz code has become an invaluable tool during its 20 years of development, providing capability to solve for electron transport (‘swarm’) properties based on a growing encyclopedia of built-in collision cross sections. We have made a refactorization of this code from FORTRAN into Cython, and studied a range of gas mixtures of interest in high energy and nuclear physics. The results from the new open source PyBoltz package match the outputs from the original MagBoltz code, with comparable simulation speed. An extension to the capabilities of the original code is demonstrated, in implementation of a new Modified Effective Range Theory interface. We hope that the versatility afforded by the new Python code-base will encourage continued use and development of the MagBoltz tools by the particle physics community.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The fastVFP code for solution of the Vlasov–Fokker–Planck equation

Abstract We describe the fast VFP code for solution of the Vlasov–Fokker–Planck equation for non-local electron transport and the generation of magnetic field, especially for application to laser-produced plasmas. We describe the essential features of the code that make it fast and robust and suitable for inclusion as a transport package in a fluid simulation. We present a few sample results that demonstrate the abilities of the code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

The Measurement of Grain Size by Electron Beam Backscattered Diffraction

The method of electron back-scatter diffraction provides an analytic means to identify crystalline phase and the grain size of structural materials. The need to utilize imaging parameters consistent with revealing the contributing microstructural scale is often overlooked through packaged imaging scripts. It is shown that imaging parameters consistent with a post processing assessment of low misorientation angles between boundaries are achievable as using pixel sizes on the order of 10 1 nm.

36 MATERIALS SCIENCE↗

DOE OSTI2021

PyCCE: A python package for custer correlation expansion simulations of spin qubit dynamics.

PyCCE, an open-source Python library for simulating the dynamics of spin qubits in a spin bath, is presented using the cluster-correlation expansion (CCE) method. PyCCE includes modules to generate realistic spin baths, employing coupling parameters computed from first principles with electronic structure codes, and enables the user to run simulations with either the conventional or generalized CCE method. Three use cases of the Python library are illustrated: the calculation of the Hahn-echo coherence time of the nitrogen-vacancy in diamond; the calculation of the coherence time of the basal divacancy in silicon carbide at avoided crossings; and the calculation for magnetic field orientation-dependent dynamics of a shallow donor in silicon. The complete documentation, downloadable tutorials, and installation instructions are available at https://pycce.readthedocs.io/en/latest/.

coherence↗

DOE OSTI2022

High-current electron sources for Superconducting Radiofrequency injectors

Accelerators are finding increasing use in numerous fields such as medicine, industry accelerator-based nuclear energy programs and others. A recent survey reveals only 20 high-energy accelerators worldwide serve elementary-particle physics while thousands of accelerators are routinely used in hospitals and industry (packaging, food sterilization, etc.).

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

36 MATERIALS SCIENCE↗

DOE OSTI2023

Block2 : A comprehensive open source framework to develop and apply state-of-the-art DMRG algorithms in electronic structure and beyond

block2 is an open source framework to implement and perform density matrix renormalization group and matrix product state algorithms. Out-of-the-box it supports the eigenstate, time-dependent, response, and finite-temperature algorithms. In addition, it carries special optimizations for ab initio electronic structure Hamiltonians and implements many quantum chemistry extensions to the density matrix renormalization group, such as dynamical correlation theories. The code is designed with an emphasis on flexibility, extensibility, and efficiency and to support integration with external numerical packages. Here, we explain the design principles and currently supported features and present numerical examples in a range of applications.

Chemistry↗

DOE OSTI2021

Determine Oxidation State of Transition Metals in Molten Salt Corrosion using Electron Energy Loss Spectroscopy

This work utilizes electron energy loss spectroscopy (EELS) to identify oxidation state of alloying elements in Ni-based alloys after exposure to molten chloride salt systems. Pure Ni and Ni 20Cr model alloy were corroded in molten ZnCl2 and KCl-MgCl2 under argon atmosphere at various temperatures. Oxidation states of Cr (Cr3+) and Ni (Ni2+) in the molten salt after corrosion were determined by monitoring changes in the L2,3 edges of corresponding EELS spectra. Oxidation state mapping technique using principal component analysis and multiple linear least squares fitting in HyperSpy Python package was developed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

An automatically curated first-principles database of ferroelectrics

Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. Finally, we contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Non-invasive authentication of mail packages using nuclear quadrupole resonance spectroscopy

The international postal network is one of the most widely used methods for correspondence throughout the world. Most postal traffic across the globe consists of legitimate interpersonal, business-consumer, and business-business communications. However, the global postal system is also utilized for criminal activity. In particular, it is often utilized to ship and distribute contraband, including illegal psychoactive drugs such as fentanyl and heroin, to consumers. Existing technological solutions are capable of identifying synthetic opioids and other illegal drugs within packages, but are accompanied by several disadvantages that make them unsuitable for large-scale authentication of international mail traffic. This paper presents a novel method for non-invasive authentication of mail packages that overcomes these challenges. The approach uses nuclear quadrupole resonance (NQR) spectroscopy to detect and quantify the presence of known active pharmaceutical ingredients (APIs) within the package. It has been experimentally demonstrated using a bench top prototype. Test results from a variety of package types demonstrate the effectiveness of the proposed authentication approach.

42 ENGINEERING↗

DOE OSTI2021

Lyman-α constraints on cosmic heating from dark matter annihilation and decay

We derive new constraints on models of decaying and annihilating dark matter (DM) by requiring that the energy injected into the intergalactic medium (IGM) not overheat it at late times, when measurements of the Lyman-α forest constrain the IGM temperature. We improve upon previous analyses by using the recently developed DarkHistory code package, which self-consistently takes into account additional photoionization and photoheating processes due to reionization and DM sources. Our constraints are robust to the uncertainties of reionization and competitive with leading limits on sub-GeV DM that decays preferentially to electrons

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Two-Phase Cooling for High Packaging Density Planar Inverter: Cooperative Research and Development Final Report, CRADA Number CRD-15-00592

The project objective was to utilize two-phase heat transfer to enable compact and power-dense inverters. The high heat transfer capacity and near isothermal characteristics of two-phase cooling can enable higher power densities, reducing the size and cost of the power electronics system. This project focused on developing efficient, passive (no pump or compressor) two-phase cooling strategies that consist of an air-cooled condenser and evaporator. This report describes the modeling work conducted to design the evaporator and air-cooled condenser for a prototype inverter development work underway in the John Deere. Prototypes of the final evaporator and condenser designs were then fabricated via 3D printing for testing. Experiments on the evaporator confirmed that this component can meet the heat load and junction temperatures requirements for Si IGBT (insulated-gate bipolar transistor) inverter. Experiments with the air-cooled condenser were not possible due to leaks through the condenser walls.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

A Processing and Analytics System for Microscopy Data Workflows: The Pycroscopy Ecosystem of Packages

Major advancements in fields as diverse as biology and quantum computing have relied on a multitude of microscopy techniques. Despite the considerable proliferation of these instruments, significant bottlenecks remain in terms of processing, analysis, storage, and retrieval of the acquired datasets. Aside from lack of file standards, individual domain-specific analysis packages are often disjoint from the underlying datasets, and thus keeping track of analysis and processing steps remains tedious for the end-user, hampering reproducibility. Here, in this study, the pycroscopy ecosystem of packages is introduced, an open-source python-based ecosystem underpinned by a common data model. The data model, termed the N-dimensional spectral imaging data format, is realized in pycroscopy's sidpy package. This package is built on top of dask arrays, thus leveraging dask array attributes, but expanding them to accelerate microscopy relevant analysis and visualization. Several examples of the use of the pycroscopy ecosystem to create workflows for data ingestion and analysis of scanning transmission electron microscopy (STEM) and scanning probe microscopy data are shown. Adoption of such standardized routines will be critical to usher in the next generation of autonomous instruments where processing, computation, and meta-data storage will be critical to overall experimental operations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Isoprene, Chlorophyll fluorescence, and leaf temperature data from Manaus, Brazil, 2017 - 2018

The data included in this data package are a demonstration of an inverse relationship in the early successional species Vismia guianensis in the central Amazon between stomatal conductance (gs) and leaf temperature, while net photosynthesis (Pn) showed an optimum value of 32.6 ± 0.4°C. In contrast to Pn, photosynthetic electron transport rates (ETR) and the QA oxidation state (qL) increased linearly with leaf temperature. Leaf isoprene emissions, a primary product of photosynthesis and strongly linked to plant high temperature stress tolerance, showed strong linear correlations with ETR (ƿ = 0.98) and qL (ƿ = 0.99). Furthermore, inhibition of isoprenoid biosynthesis repressed Pn at high temperatures through a mechanism that was independent of stomatal closure. Data is used in the publication, "Stimulation of isoprene emissions and electron transport rates as a key mechanism of thermal tolerance in the tropical species Vismia guianensis." See the below Dataset References field for the full citation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Particle transport constraints via Bayesian spectral fitting of multiple atomic lines

Optimized operation of fusion devices demands detailed understanding of plasma transport, a problem that must be addressed with advances in both measurement and data analysis techniques. In this work, we adopt Bayesian inference methods to determine experimental particle transport, leveraging opportunities from high-resolution He-like ion spectra in a tokamak plasma. The Bayesian spectral fitting code is used to analyze resonance (w), forbidden (z), intercombination (x, y), and satellite (k, j) lines of He-like Ca following laser blow-off injections on Alcator C-Mod. This offers powerful transport constraints since these lines depend differently on electron temperature and density, but also differ in their relation to Li-like, He-like, and H-like ion densities, often the dominant Ca charge states over most of the C-Mod plasma radius. Using synthetic diagnostics based on the AURORA package, we demonstrate improved effectiveness of impurity transport inferences when spectroscopic data from a progressively larger number of lines are included.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Particle transport constraints via Bayesian spectral fitting of multiple atomic lines

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Performance of Muon Neutrino Reconstruction at ICARUS using SPINE Machine Learning.

ICARUS is a liquid argon time projection chamber (LArTPC) neutrino experiment at Fermilab. Located ~600 m from the Booster Neutrino Beam (BNB) target, it serves as the far detector in the Short-Baseline Neutrino (SBN) Program. The primary objective of the SBN Program is to probe neutrino oscillation physics at short baselines using multiple detectors, including measurements of both muon neutrino disappearance and electron neutrino appearance. A key requirement for the neutrino oscillation measurements is robust event reconstruction, including accurate and precise particle identification (PID) and kinematic reconstruction across a wide range of topologies. To achieve the required performance, we employ the SPINE (“Scalable Particle Imaging with Neural Embeddings”) package, a state-of-the-art machine learning reconstruction framework. This talk presents the status of muon neutrino reconstruction using SPINE at ICARUS, including the evaluation of reconstruction performance that will enable the full physics program at ICARUS and at the SBN Program.

Totani, Dante [Colorado State U.] (ORCID:000000019↗