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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials

Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.

14 SOLAR ENERGY↗
DOE OSTI2026

Evaluation of Compton suppression for enhancing trace element identification in neutron activation analysis of reference materials

Neutron activation analysis (NAA) is a powerful technique for identifying and quantifying trace elements in materials. However, challenges such as high dead times, spectral interferences, and high Compton continuum often arise. This study employs a Compton suppression system (CSS) to enhance NAA sensitivity by reducing the Compton continuum, thereby improving the peak-to-Compton ratio. National Institute of Standards and Technology certified reference materials 1632d, 1633c, and Canadian National Research Council TORT-1 were irradiated in a thermal and epithermal neutron flux under various irradiation, decay and counting times and analyzed using high-resolution gamma-ray spectroscopy with and without Compton suppression. The reduction factor was calculated to evaluate the effectiveness of the CSS, demonstrating significant background reduction and improved detection limits for trace elements. Additional experiments with a 137Cs point source demonstrated the impact of detector-source geometry on system performance, showing a decrease in the reduction factor as the source was moved further from the NaI detector. An optimum distance between the source and HPGe detector was observed, yielding the highest peak-to-Compton ratio. The results highlight the CSS's ability to minimize spectral interference and enhance elemental identification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

rlmolecule: A library for general-purpose material and molecular optimization using AlphaZero-style reinforcement learning

This library includes in-progress code for the optimization of materials and molecules using reinforcement learning. This project seeks to develop a generalized machine learning approach for optimizing targeted, and often complex, functional properties over the space of possible material structures, allowing faster identification of promising candidates. Electronic and transport properties of materials are determined by their molecular structure (for molecules) or their crystal structure (for in-organic crystalline materials). The atomic composition, bonding, and 3-D-spatial arrangement in these systems therefore represent a vast, discontinuous search space for optimal candidates. This library implements AlphaZero style reinforcement learning applied to two worked examples: the optimization of stable organic radicals for redox flow batteries, and the determination of stable electrolyte species for solid state batteries. The current code contains proof-of-concept results run on the Eagle HPC system, while the completed package will be a flexible tool for material optimization across of number of energy-relevant fields, which leverages cloud compute resources for the majority of the computational heavy lifting.

Biagioni, David↗
DOE OSTI2024

General algorithm for characterization of donor-acceptor pair recombination processes in solid-state materials

Radiative recombination processes can occur in solid-state systems through the pairing of donor and acceptor defects of the lattice. Recently, donor-acceptor pairs (DAP) have been proposed as promising candidates for quantum applications, and their signature has been observed in emerging low-dimensional materials. Therefore, the identification of such processes is gaining interest and requires methods to efficiently and reliably characterize them. Here, we introduce a general algorithm to identify DAP processes starting from the experimental photoluminescence (PL) emission spectrum and basic material parameters, including the lattice structure and dielectric constant. The algorithm recognizes possible DAP transitions from the emission pattern in the spectrum and returns the characteristic energy of the DAP transition and the separation between the donor and acceptor sites. By testing the algorithm on the photoluminescence spectrum of hexagonal boron nitride (hBN), we show that our method is robust against experimental errors and adds new capabilities to the investigation toolbox of semiconductors and their optical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Review of Nuclear Forensics: 2016-2020

The study of nuclear forensics harkens back to the Manhattan Project-era, when scientists first started to analyze the debris from the 1945 Trinity test. Political turmoil stemming from the Cold War and the rehabilitation of Germany following WWII has led to new challenges in international security involving nuclear proliferation. Nuclear materials have, on occasion, been lost, misplaced, or stolen from former Soviet countries, and illicit materials have been interdicted all over the world. The National Technical Nuclear Forensics Center (NTNFC) was established in 2006, and has been at the forefront of drive to advance nuclear forensic capabilities in the United States. The ultimate goal of nuclear forensics is to examine nuclear and other radioactive materials using analytical techniques to determine origin and history of the material, particularly in the context of law enforcement investigations. Nuclear forensics can be divided into two parts: predetonation and post-detonation. Pre-detonation forensics, as the name implies, is the investigation of a nuclear material or weapon that has not been detonated or involved in an explosion, whereas postdetonation forensics is the study of activation or fission products in debris or the environment following the use of a nuclear or radiological dispersal device (RDD). Both parts require a number of analytical chemical and radiochemical techniques to determine identification of the material. Many advancements in analytical techniques, including rapidity, sample size, and forensic signatures have been made in recent years. The analytical methods that can be used in a nuclear forensic investigation, such as mass spectrometry and gamma spectroscopy, have been described in detail in previous reviews, including Straub et.al, and will not be explained here. This review will discuss recent publications (from 2016 to present) describing advancements of techniques such as radiochronometry, morphology, development of novel reference materials, and inter-laboratory collaborations for both pre- and post-detonation nuclear forensics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Connecting Material Properties and Redox Flow Cell Cycling Performance through Zero-Dimensional Models

Improvements in redox flow battery (RFB) performance and durability can be achieved through the development of new active materials, electrolytes, and membranes. While a rich design space exists for emerging materials, complex tradeoffs challenge the articulation of unambiguous target criteria, as the relationships between component selection and cycling performance are multifaceted. Here, we derive zero-dimensional, analytical expressions for mass balances and cell voltages under galvanostatic cycling, enabling direct connections between material/electrolyte properties, cell operating conditions, and resulting performance metrics (e.g., energy efficiency, capacity fade). To demonstrate the utility of this modeling framework, we highlight several considerations for RFB design, including upper bound estimation, active species decay, and membrane/separator conductivity-selectivity tradeoffs. Furthermore, we also discuss modalities for extending this framework to incorporate kinetic losses, distributed ohmic losses, and multiple spatial domains. Importantly, because the mass balances are solved analytically, hundreds of cycles can be simulated in seconds, potentially facilitating detailed parametric sweeps, system optimization, and parameter estimation from cycling experiments. More broadly, this approach provides a means for assessing the impact of cell components that simultaneously influence multiple performance-defining processes, aiding in the elucidation of key descriptors and the identification of favorable materials combinations for specific applications.

25 ENERGY STORAGE↗
DOE OSTI2020

Quadrupole topological photonic crystals

Quadrupole topological phases, exhibiting protected boundary states that are themselves topological insulators of lower dimensions, have recently been of great interest. Extensions of these ideas from current tight binding models to continuum theories for realistic materials require the identification of quantized invariants describing the bulk quadrupole order. Here we identify the analog of quadrupole order in Maxwell’s equations for a gyromagnetic photonic crystal (PhC) through a double-band-inversion process. The quadrupole moment is quantized by the simultaneous presence of crystalline symmetry and broken time-reversal symmetry, which is confirmed using three independent methods: analysis of symmetry eigenvalues, numerical calculations of the nested Wannier bands and the expectation value of the quadrupole operator. Furthermore, we reveal the boundary manifestations of quadrupole phases as quantized edge polarizations and fractional corner charges. The latter are the consequence of a filling anomaly of energy bands as first predicted in electronic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Crystal structure of silver pentazolates AgN 5 and AgN 6

Silver pentazolate, a high energy density compound containing the cyclo-N 5 – anion, has recently been synthesized under ambient conditions. However, due to high sensitivity to irradiation, its crystal structure has not been determined. In this work, silver–nitrogen crystalline compounds under ambient conditions and at high pressures, up to 100 GPa, are predicted and characterized by performing first-principles evolutionary crystal structure searching with variable stoichiometry. It is found that newly discovered AgN 5 and AgN 6 are the only thermodynamically stable silver–nitrogen compounds at pressures between 42 and 80 GPa. In contrast to AgN 5 , the pentazolate AgN 6 compound contains N 2 diatomic molecules in addition to cyclo-N 5 – . These AgN 5 and AgN 6 crystals are metastable under ambient conditions with positive formation enthalpies of 54.95 kJ mol –1 and 46.24 kJ mol –1 , respectively. Here, the underlying cause of the stability of cyclo-N 5 – silver pentazolates is the enhanced aromaticity enabled by the charge transfer from silver atoms to nitrogen rings. To aid in the experimental identification of these materials, calculated Raman spectra are reported at ambient pressure: the frequencies of N 5 – vibrational modes of AgN 5 are in good agreement with those measured in the experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Automated Threat Recognition For Aviation Security Applications

We have developed a framework for automated threat recognition (ATR) of explosive threat materials for both single-energy and dual-energy X-ray CT systems. Under this framework, two different types of ATR have been developed. The first type of ATR employs supervised machine learning with statistical characterization of target materials for threat identification training. The reliance only on statistical characterization information for threat training uniquely enables this style of ATR to adapt quickly to evolving threats. The second type of ATR employs deep learning through convolutional neural networks. Convolutional neural networks are attractive due to their human-like capacity for learning and strong ability to identify trends and patterns. Although this method is more powerful than the first type, it requires large amounts of training data and therefore is less agile. Each ATR performs threat characterization at the voxel neighborhood level. This approach avoids the use of threat shape as a detection criterion, which is prohibited by DHS and TSA guidelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

ERDE: The Earth-Field Resonance Detection and Evaluation Devices

Researchers at Los Alamos have developed the Earth-field Resonance Detection and Evaluation devices, or ERDE (German for “Earth”) which brings portable chemical analysis and identification into the field. ERDE uses the Earth’s magnetic field to perform nuclear magnetic resonance measurements without the need for sample preparation, costly cryogens, or calibration procedures, meaning it is extremely user-friendly. In fact, using the Earth’s magnetic field allows detection and identification of chemical species through non-magnetic pipes without sampling, reducing the likelihood of costly biofouling. This innovative new device brings together these outstanding benefits to improve processes in the detection of noxious chemicals in water supplies as well as the identification of toxic materials anywhere. There are two fully-functional ERDE prototypes and efforts are underway to miniaturize the system further for improved portability, increased sensitivity, and expanded capabilities. The Laboratory is seeking qualified licensing or CRADA partners to finalize development of this system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Quantum Dot Based Solid-State Radiation Detectors

The detection and accurate identification of nuclear material is critical to the advancement of national security, medical technology, and environmental prosperity. The gold standard solid-state detector HPGe, suffers from high manufacturing costs and the need to operate at cryogenic temperatures to ensure reliable energy resolution. The other most common solid-state detector, cadmium zinc telluride (CZT), has the ability to operate at room temperature, but the grow process results in impurities decreasing performance. In this paper we have developed a continuous flow synthesis able to mass produce targeted size cadmium selenide quantum dots. The implementation of these QDs in QD/MEH-PPV composites has the ability to rival HPGe energy resolution at room temperature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Material sorting using a vision system

A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification. The material sorting system may include an x-ray fluorescence system to perform a classification of the materials in combination with the vision system, whereby the classification efforts of the vision system and x-ray fluorescence system are combined in order to classify and sort the materials.

Kumar, Nalin↗
DOE OSTI2026

Atomic-Scale Imaging of Lithium Vacancies in a Battery Cathode by Multislice Electron Ptychography

Atomic-resolution imaging of battery materials is critical for identification of local defects and structural variations, which are tied to battery performance. However, since battery materials are, by design, optimized to allow ion motion in response to an applied electric field, they are also very sensitive to radiation damage by an electron beam. Image resolution is therefore severely constrained by the dose applied. Here, we show that multislice electron ptychography (MEP) can provide sub-ångström lateral resolution images of both light and heavy elements of a Li-ion battery cathode, along with nanometer-scale depth information and greater dose efficiency than conventional electron microscopy methods. Using the depth-sectioning capability of MEP, we have been able to obtain direct visualizations of Li vacancy clusters, atom column by atom column, in Li x Ni 0.33 Mn 0.33 Co 0.33 O 2 (NMC111) cathodes. This capability to track Li distributions will be valuable in understanding, informing, and optimizing electrode material design for ion storage and transfer.

batteries↗
DOE OSTI2023

Toward Machine Learning-Driven Mass Spectrometric Identification of Trichothecenes in the Absence of Standard Reference Materials

While a significant body of work exists on the detection of commonly known trichothecene toxins, biological, environmental, and other transformational processes can generate many under-characterized and unknown modified trichothecenes. Lacking both analytical reference standards and associated mass spectral databases, identification of these modified compounds reflects both a challenge and a critical gap from forensic and public health perspectives. Here we report here the application of machine learning (ML) techniques toward identification of discriminative fragment ions from mass spectrometric data that can be exploited to detect evidence of type A and B trichothecenes. The goal of this work is to establish a new method for the identification of unknown, though structurally similar trichothecenes, by leveraging objective ML techniques. Discriminative fragments derived from a series of gradient-boosted machine learners are then used to develop ML-driven precursor ion scan (PIS) methods on a triple quadrupole mass spectrometer (QQQ) for screening of “unknown unknown” trichothecenes. Specifically, we apply the PIS method to a laboratory-synthesized trichothecene, a first step in demonstrating the power of alternative, machine learning-driven mass spectrometric methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

GADRAS↗
DOE OSTI2020

Discriminating Uranium Isotopes Based on Fission Signatures Induced by Delayed Neutrons

The use of active interrogation to induce delayed neutron emission is a well-established technique for the characterization of special nuclear materials. Delayed neutrons have isotope-characteristic spectral and temporal signatures, which provide the basis for isotope identification. However, in bulk materials that contain an appreciable fissile (e.g., 235 U or 233 U) fraction, such as highly enriched uranium (HEU), delayed neutrons have a high probability of inducing additional fissions. As a result, the overall delayed neutron signature consists of two distinct components: the “primary” delayed neutrons (emitted directly by fission fragments) and the “secondary prompt” fission neutrons produced in fission induced by primary delayed neutrons. These prompt products differ from “primary” delayed neutrons both in their energy spectra and in the presence of coincident radiation released by the parent fission event. The presence and relative quantity of prompt products from delayed fission depend on the cross section of the material in the energy range of delayed neutrons, which may differ significantly between isotopes, thus providing an exploitable means for isotope differentiation. Here, we demonstrate two experimental approaches for discriminating between 235 U and 238 U isotopes based on the measurement of delayed neutron-induced fission products. First, HEU and depleted uranium objects are differentiated through the detection of high-energy prompt neutrons from delayed fission using both recoil-based organic liquid scintillators and thermalization spectra from a custom-built capture-gated composite detector. Secondly, coincident radiation measurements are used as the basis for discrimination by comparing the overall rates and time evolution of fission events when delayed neutrons are present.

07 ISOTOPE AND RADIATION SOURCES↗