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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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DOE OSTI2022

A reusable neural network pipeline for unidirectional fiber segmentation

Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Mechanical performance of co-deposited immiscible Cu–Ta thin films

Abstract The immiscible alloy Cu–Ta has the potential for enhanced mechanical performance in applications as a functional coating. To establish baseline mechanical properties, four Cu–Ta films were co-sputtered at the temperatures 23, 400, 600, and 800 °C and tested with nanoindentation at strain rates 5 $$\times $$ × 10 −3 s −1 to 10 s −1 . Each film had a unique microstructure morphology. The hardness and elastic modulus of the four films were insensitive to strain rate changes. Instead, the measured properties were spatially dependent, particularly in the 600 and 800 °C films. In those two films, there is a bimodal deformation behavior due to Cu-agglomeration under protruding grains and planar Ta-rich regions. Increasing the indentation depth revealed shear band suppression which is related to a homogenous distribution of flow stresses for all four microstructure morphologies. Finally, the Cu–Ta hardness appeared to follow a rule-of-mixtures when compared to extrapolated data of Cu and Ta monolithic films.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Vickers hardness prediction from machine learning methods

Abstract The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Band gap predictions of double perovskite oxides using machine learning

Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.

Physics↗

DOE OSTI2025

Overcoming sparse datasets with multi-task learning as applied to high entropy alloys

Abstract The design of novel High Entropy Alloys for use in high-temperature applications is an area of active interest due to their potential to provide exceptional properties compared to conventional alloys. Since the increased popularity of machine learning, an important cog in the design process has been training surrogate models on alloy properties. However, these Single-Task models are trained on individual mechanical properties and do not take advantage of the relatedness between properties. Multi-Task models can capture the interdependencies between tasks, leading to potentially more accurate predictions for all tasks. In this paper, we investigate if Multi-Task models can show improvement over Single-Task models when used for predicting the mechanical properties of these alloys. To ensure fair evaluation between the models, we apply L 0 regularization and skip connections to the models, which allows them to adjust the number of model parameters and depth for optimal performance. We find that the Multi-Task models can leverage task relationships to perform better than Single-Task models, especially for high amounts of missing data in the tasks. Furthermore, adding simple auxiliary targets can boost Multi-Task performance even further despite not being effective as input descriptors to single-task models themselves. We anticipate that the proposed strategies can achieve more accurate predictions and consequently enable better design capabilities for such data-constrained domains without incurring much additional computational cost.

Debnath, Arindam (ORCID:0000000194274499)↗

DOE OSTI2022

Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms

Many extreme scale scientific applications have workloads comprised of a large number of individual highperformance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible Pilot enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show its ability to scalably execute workloads comprised of tens of thousands heterogeneous tasks on DOE and NSF leadership-class HPC platforms. Specifically, we investigate RP's weak/strong scaling with CPU/GPU, single/multi core, (non)MPI tasks and python functions when using most of ORNL Summit and TACC Frontera. RADICAL-Pilot can be used stand-alone, as well as the runtime for third-party workflow systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Location generalizability of image-based air quality models

This paper is to be submitted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Computer Vision for Earth Observation workshop. The full paper abstract is below: The ability to rapidly quantify atmospheric pollutants is important both for global emissions monitoring and for mitigating the adverse effects that follow a hazardous chemical release. In the aftermath of a chemical release, imagery is often the only available resource to assess local conditions. Recent work has demonstrated initial success in predicting particulate matter pollution from imagery; however, these results are tied to a specific site and do not generalize to new geographic locations. In this work, we seek to understand how easily deep learning models generalize to new locations in the context of image-based air quality assessments, targeting two distinct tasks: (1) broad measures of particulate matter pollution, and (2) the mass of a given chemical released in hazardous plumes. For the latter, we focus on sulfur dioxide, a toxic aerosol and a major component of particulate matter pollution caused by industrial fossil fuel consumption. To develop a model that operates in the widest possible range of environments, we test different training strategies, including the use of new geolocation foundation models. The best performing models achieve >80% accuracy when evaluating unseen imagery at previously seen sites, but we find significant drops in performance when evaluating imagery from unseen sites, at best 65%. Additionally, we present the public release of the National Parks Air Quality Index Dataset, a new medium-sized dataset that pairs imagery with sensor-based air quality measurements at 15 different national parks.

Byler, Eleanor B. [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2022

Interactively Assessing Disentanglement in GANs

Abstract Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo‐realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute‐based semantic scoring of images produced by GANs. Through use‐cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.

Jeong, Sangwon↗

DOE OSTI2023

Logical Activation Functions v.1.1

SAND2024-01501O Logical Activation Functions software is a PyTorch implementation from the paper, "Logical Activation Functions for Training Arbitrary Probabilistic Boolean Logic." The activation functions approximate logit-space marginalization of probabilistic truth tables from probabilistic interpretations of inputs. They also provide a general methodology to approximate logical relationships between abstract antecedents and consequents for machine learning architectures. They do not target any specific application or use-case. By training probabilistic truth tables, these activation functions can capture more expressive relationships in a neural network than typical elementwise activation functions. This code is only designed for a single compute node with a GPU and is limited to machine learning architectures than can fit within the memory of a single GPU. 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.

Duersch, Jed↗

DOE OSTI2023

EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment

Abstract Background Adding sequences into an existing (possibly user-provided) alignment has multiple applications, including updating a large alignment with new data, adding sequences into a constraint alignment constructed using biological knowledge, or computing alignments in the presence of sequence length heterogeneity. Although this is a natural problem, only a few tools have been developed to use this information with high fidelity. Results We present EMMA (Extending Multiple alignments using MAFFT--add) for the problem of adding a set of unaligned sequences into a multiple sequence alignment (i.e., a constraint alignment). EMMA builds on MAFFT--add, which is also designed to add sequences into a given constraint alignment. EMMA improves on MAFFT--add methods by using a divide-and-conquer framework to scale its most accurate version, MAFFT-linsi--add, to constraint alignments with many sequences. We show that EMMA has an accuracy advantage over other techniques for adding sequences into alignments under many realistic conditions and can scale to large datasets with high accuracy (hundreds of thousands of sequences). EMMA is available at https://github.com/c5shen/EMMA . Conclusions EMMA is a new tool that provides high accuracy and scalability for adding sequences into an existing alignment.

Shen, Chengze↗

DOE OSTI2022

Plasmonic sensors based on graphene and graphene hybrid materials

Abstract The past decade has witnessed a rapid growth of graphene plasmonics and their applications in different fields. Compared with conventional plasmonic materials, graphene enables highly confined plasmons with much longer lifetimes. Moreover, graphene plasmons work in an extended wavelength range, i.e., mid-infrared and terahertz regime, overlapping with the fingerprints of most organic and biomolecules, and have broadened their applications towards plasmonic biological and chemical sensors. In this review, we discuss intrinsic plasmonic properties of graphene and strategies both for tuning graphene plasmons as well as achieving higher performance by integrating graphene with plasmonic nanostructures. Next, we survey applications of graphene and graphene-hybrid materials in biosensors, chemical sensors, optical sensors, and sensors in other fields. Lastly, we conclude this review by providing a brief outlook and challenges of the field. Through this review, we aim to provide an overall picture of graphene plasmonic sensing and to suggest future trends of development of graphene plasmonics.

Zhang, Zhichao↗

DOE OSTI2024

Spatiotemporal imaging and manipulation of surface plasmons

Abstract Surface plasmon polaritons (SPPs) are travelling surface waves that have shown promise for applications in nanophotonics as they provide a direct route toward photon-mediated electrical signal generation – a central paradigm for speeding up and scaling down photonic elements. SPP waves have also generated fundamental interest due to their high-field strength and sub-wavelength confinement, properties that have enabled the surface-enhanced Raman effect. Over the last decade, photoemission electron microscopy (PEEM) has emerged as a pioneering technique for imaging surface electric fields through ultrashort laser pulse mediated electron emission and has therefore become an indispensable tool for characterizing plasmonic phenomena at interfaces in a variety of materials. PEEM offers nanometer spatial resolution and femtosecond temporal resolution, allowing SPPs to be prepared, monitored, and manipulated on the nanometer-femtosecond scale. Through a brief review of recent reports, we aim to introduce PEEM-based SPP imaging and manipulation modalities and highlight their utility in the context of emerging nanoscale and quantum materials science advancements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Abstract for CRADA between the National Energy Technology Laboratory and General Motors (AGMT-0876)

Lightweight aluminum shape castings have widespread applications for structural components in the automotive, aerospace, and general engineering industries because of their excellent castability, corrosion resistance, and particularly high strength-to-weight ratio in the heat-treated condition. In the automotive industry, cast aluminum alloys have been increasingly used to replace cast iron and steel in vehicle applications such as engine blocks, cylinder heads, intake manifolds, brackets, housings, chassis, transmission parts, and suspension systems to reduce mass. As many of the aluminum shape castings used in automobiles involve cyclic loading, fatigue properties of the castings are critical to their success. The increasing use of lightweight aluminum cast components in critical structures thus requires improved integrity, with more reliable and quantifiable performance. The objective of this project is to demonstrate and further develop GM’s patent-pending, novel casting process, “Pressure-Assistant Precision Sand Casting (PAPSC) for producing flawless cast aluminum cylinder heads with minimum manufacturing and energy costs”. The approach is to leverage NETL’s experience in producing high-quality metal castings through various microstructure control and defect elimination technologies as well as comprehensive quantification of microstructure characteristics and mechanical properties. A baseline (production) alloy together with the melt treatment will be initially evaluated with the new PAPSC process to develop and validate the process window for producing flawless cast aluminum heads.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Measurement report: Properties of aerosol and gases in the vertical profile during the LAPSE-RATE campaign

Abstract. Unmanned aerial systems (UASs) are increasingly being used as observation platforms for atmospheric applications. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) in Alamosa, Colorado, USA, on 14–20 July 2018 investigated and validated different UASs, measurement sensors and setup configurations. Flight teams from the Finnish Meteorological Institute (FMI) and Kansas State University (KSU) participated in LAPSE-RATE to measure and investigate properties of aerosol particles and gases in the lower atmosphere. During the experiment, the performance of different UAS configurations were investigated and confirmed to operate reliably, resulting in a scientifically sound observational dataset. As an example, concentration of aerosols – including two new particle formation events, CO2 and water vapor, and meteorological parameters in the atmospheric vertical profile were measured during the short experiment. Such observations characterizing atmospheric phenomena of this specific environment would have not been possible in any other way and, thus, demonstrate the power of UASs as new, promising tools in atmospheric and environmental research.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems

Koopman operator theory provides an alternative to study nonlinear networked dynamical systems by mapping the state space to an abstract higher dimensional space where the system evolution is linear. Recent works show the application of graph neural networks (GNNs) to learn state to object-centric embeddings and achieve centralized block-wise computation of Koopman operator (KO) under additional assumptions on the underlying agents properties and constraints on the KO structure. However, the computational complexity of learning the Koopman increases exponentially for networked systems where the number of possible system states grows in a combinatorial fashion with the number of nodes. The learning challenge is further amplified for sparse networks by two factors: 1) sample sparsity for learning the Koopman operator in the non-linear space, and 2) the divergence in the dynamics of individual nodes or from one subgraph to another. Our work aims to address these challenge by formulating the representation learning of networked dynamical systems into a multi-agent paradigm and learning the Koopman operator in a distributive manner. The computational as well as performance advantages of distributed Koopman is predominant for sparse networks whereas for fully connected networks, it is shown to coincide with the centralized one. The empirical study on rope system, network of oscillators and a synthetic power system show comparable and superior performance along with computational benefits with the state-of-the-art methods.

Mukherjee, Sayak↗

DOE OSTI2022

Methane Pyrolysis for CO2-free H2 and Carbon Nanomaterials (Abstract)

We propose to continue to develop a new process for producing CO2-free hydrogen (H2) from inexpensive and domestically-abundant natural gas (NG), while simultaneously reducing H2’s net production cost to $1.0/kg through the sale of valuable crystalline solid carbon co-product. Producing clean hydrogen at this price is a DOE Hydrogen Energy Earthshot goal. Cost effective production of clean H2 is also of commercial relevance to project partners Southern California Gas Company (SoCalGas) and startup company C4-MCP, who aim to further develop, demonstrate at scale, and ultimately deploy the new process technology developed on this project in order to meet regulatory demands in the State of California. In the current project we have focused on i) understanding the catalyst science for thermocatalytic decomposition of methane (TCD), which resulted in the development of a patent pending bimetallic catalyst offering favorable activity, stability, and selectivity under industrially relevant process conditions, ii) developing a novel, patent pending process to enable the separation of produced carbon and catalyst, and re-synthesis of the catalyst using recycled materials, iii) performing limited characterization of the produced carbon materials, and iv) performing detailed process modeling in order to perform techno economic assessment. The additional scope proposed here will accelerate the commercial deployment of TCD for CO2-free H2 and valuable solid carbon nanotubes (CNT) co-product, by i) scaling up the production of CNT co-product using a scalable, fluidized bed reactor (25 g catalyst scale versus the 1 g catalyst scale demonstrated to-date), ii) producing at least 40 g of CNT product, produced via multiple cycles of TCD, carbon-catalyst separation, and catalyst re-synthesis, to enable the production of sufficient quantities of solid carbon so as to explore its market potential, iii) understanding the quality of the co-product CNTs, produced at larger scale, through advanced characterization, and iv) beginning to explore multiple promising high volume carbon product applications (thermoplastics, automotive composites, battery, and cement reinforcement applications).

08 HYDROGEN↗

DOE OSTI2024

Determining the oxidation stability of SnSe under atmospheric exposure

Abstract Understanding surface stability becomes critical as 2D materials like SnSe are developed for piezoelectric and optical applications. SnSe thin films deposited by molecular beam epitaxy showed no structural changes after a two-year exposure to atmosphere, as confirmed by X-ray diffraction and Raman spectroscopy. X-ray photoelectron spectroscopy and reflectivity show a stable 3.5 nm surface oxide layer, indicating a self-arresting oxidative process. Resistivity measurements show an electrical response dominated by SnSe post-exposure. This work shows that SnSe films can be used in ambient conditions with minimal risk of long-term degradation, which is critical for the development of piezoelectric or photovoltaic devices. Graphical Abstract

Chin, Jonathan R. (ORCID:0000000273205678)↗