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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method

Abstract A new phase-field modeling framework with an emphasis on performance, flexibility, and ease of use is presented. Foremost among the strategies employed to fulfill these objectives are the use of a matrix-free finite element method and a modular, application-centric code structure. This approach is implemented in the new open-source PRISMS-PF framework. Its performance is enabled by the combination of a matrix-free variant of the finite element method with adaptive mesh refinement, explicit time integration, and multilevel parallelism. Benchmark testing with a particle growth problem shows PRISMS-PF with adaptive mesh refinement and higher-order elements to be up to 12 times faster than a finite difference code employing a second-order-accurate spatial discretization and first-order-accurate explicit time integration. Furthermore, for a two-dimensional solidification benchmark problem, the performance of PRISMS-PF meets or exceeds that of phase-field frameworks that focus on implicit/semi-implicit time stepping, even though the benchmark problem’s small computational size reduces the scalability advantage of explicit time-integration schemes. PRISMS-PF supports an arbitrary number of coupled governing equations. The code structure simplifies the modification of these governing equations by separating their definition from the implementation of the numerical methods used to solve them. As part of its modular design, the framework includes functionality for nucleation and polycrystalline systems available in any application to further broaden the phenomena that can be used to study. The versatility of this approach is demonstrated with examples from several common types of phase-field simulations, including coarsening subsequent to spinodal decomposition, solidification, precipitation, grain growth, and corrosion.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi-objective parametrization of interatomic potentials for large deformation pathways and fracture of two-dimensional materials

Abstract This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property sets, and correlation and principal component analyses. The framework enables iterative definition of properties in the training and screening sets, guided by correlation relationships between properties, aiming to achieve optimal parametrizations for properties of interest. Specifically, the performance of increasingly complex potentials, Buckingham, Stillinger-Weber, Tersoff, and modified reactive empirical bond-order potentials are compared. Using MoSe 2 as a case study, we demonstrate good reproducibility of training/screening properties and superior transferability. For MoSe 2 , the best performance is achieved using the Tersoff potential, which is ascribed to its apparent higher flexibility embedded in its functional form. These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.

Zhang, Xu↗
DOE OSTI2022

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

25 ENERGY STORAGE↗
DOE OSTI2023

A rule-free workflow for the automated generation of databases from scientific literature

Abstract In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Proximity to a critical point driven by electronic entropy in URu2Si2

Abstract The strongly correlated actinide metal URu 2 Si 2 exhibits a mean field-like second order phase transition at T o ≈ 17 K, yet lacks definitive signatures of a broken symmetry. Meanwhile, various experiments have also shown the electronic energy gap to closely resemble that resulting from hybridization between conduction electron and 5 f -electron states. We argue here, using thermodynamic measurements, that the above seemingly incompatible observations can be jointly understood by way of proximity to an entropy-driven critical point, in which the latent heat of a valence-type electronic instability is quenched by thermal excitations across a gap, driving the transition second order. Salient features of such a transition include a robust gap spanning highly degenerate features in the electronic density of states, that is weakly (if at all) suppressed by temperature on approaching T o , and an elliptical phase boundary in magnetic field and temperature that is Pauli paramagnetically limited at its critical magnetic field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Superconductivity in the doped quantum spin liquid on the triangular lattice

Abstract Broad interest in quantum spin liquid (QSL) phases was triggered by the notion that they can be viewed as insulating phases with preexisting electron pairs, such that upon light doping they might automatically yield high temperature superconductivity. Yet despite intense experimental and numerical efforts, definitive evidence showing that doping QSLs leads to superconductivity has been lacking. We address the problem of a lightly doped QSL through a large-scale density-matrix renormalization group study of the t - J model on finite-circumference triangular cylinders with a small but nonzero concentration of doped holes. We provide direct evidences that doping QSL can naturally give rise to d -wave superconductivity. Specifically, we find power-law superconducting correlations with a Luttinger exponent, K s c ≈ 1, which is consistent with a strongly diverging superconducting susceptibility, $${\chi }_{sc} \,\sim\, {T}^{-(2\,-\,{K}_{sc})}$$ χ s c ~ T − ( 2 − K s c ) as the temperature T → 0. The spin–spin correlations—as in the undoped QSL state—fall exponentially which suggests that the superconducting pair-pair correlations evolve smoothly from the insulating parent state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Electromagnetic signatures of a chiral quantum spin liquid

Quantum spin liquids (QSL) have emerged as a captivating subject within interacting spin systems that exhibit no magnetic ordering even at the lowest temperature accessible experimentally. However, definitive experimental evidence remains elusive. In light of the recent surge in theoretical and experimental interest in the half-filled Hubbard model on a triangular lattice, which offers the potential for stabilizing a chiral QSL, we investigate the electromagnetic signatures of this phase to facilitate experimental detection. Utilizing a combination of parton mean-field theory and unbiased density-matrix renormalization group calculations, we systematically examine the electrical charge and orbital electrical current associated with a spinon excitation in the chiral QSL. Additionally, we calculate the longitudinal and transverse optical conductivities below the Mott gap. Furthermore, employing quantum field theory analysis, we unravel the connection between spinon excitations and emergent as well as physical gauge fields. Our results demonstrate that the chiral QSL phase exhibits a distinct electromagnetic response, even within a Mott insulator regime. This finding holds great potential for enabling the experimental detection of this long-sought-after phase.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A human antibody epitope map of the malaria vaccine antigen Pfs25

Pfs25 is a leading antigen for a malaria transmission-blocking vaccine and shows moderate transmission-blocking activity and induction of rapidly decreasing antibody titers in clinical trials. A comprehensive definition of all transmission-reducing epitopes of Pfs25 will inform structure-guided design to enhance Pfs25-based vaccines, leading to potent transmission-blocking activity. Here, we compiled a detailed human antibody epitope map comprising epitope binning data and structures of multiple human monoclonal antibodies, including three new crystal structures of Pfs25 in complex with transmission-reducing antibodies from Malian volunteers immunized with Pfs25 conjugated to EPA and adjuvanted with AS01. These structures revealed additional epitopes in Pfs25 capable of reducing transmission and expanded this characterization to malaria-exposed humans. This work informs immunogen design to focus the antibody response to transmission-reducing epitopes of Pfs25, enabling development of more potent transmission-blocking vaccines for malaria.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Flash droughts present a new challenge for subseasonal-to-seasonal prediction

Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. In this perspective we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Critical adjustment of land mitigation pathways for assessing countries' climate progress

Collective progress towards the Paris Agreement’s temperature targets will be assessed by the Agreement’s Global Stocktake process. Global emission pathways by Integrated Assessment Models (IAMs) are a likely benchmark against which aggregated countries’ greenhouse gas mitigation pledges will be assessed. However, this approach requires comparability, which is currently hampered by a 5 GtCO2yr-1 mismatch between the estimated global land anthropogenic flux of IAMs and countries’ GHG inventories (GHGIs) for 2005-2015. We show that this gap is mostly the result of differences in how the anthropogenic forest sink is defined. Countries define managed forest more broadly than IAMs, and on this larger area typically consider anthropogenic also fluxes due to human-induced environmental change, which IAMs do not include their pathways. Using five IAMs and a dynamic global vegetation model (DGVM), we present and apply a novel method that adjusts IAM results to the countries’ definition, closing the gap with GHGIs at global and regional scale. When expressed in a way compatible with GHGIs, adjusted 1.5°C and well-below 2°C pathways have a cumulative CO2 balance until carbon neutrality that is 110–176 GtCO2 lower than the original IAMs pathways. The use of these country-compatible emission pathways under the Global Stocktake would therefore be essential for an accurate assessment of the collective progress towards the Paris Agreement’s climate goals.

Grassi, Giacomo↗
DOE OSTI2024

Observational evidence for Criegee intermediate oligomerization reactions relevant to aerosol formation in the troposphere

Criegee intermediates are reactive intermediates that are implicated in transforming the composition of Earth’s troposphere and in the formation of secondary organic aerosol, impacting Earth’s radiation balance, air quality and human health. Yet, direct identification of their signatures in the field remains elusive. Here, from particulate and gas-phase mass-spectrometric measurements in the Amazon rainforest, we identify sequences of masses consistent with the expected signatures of oligomerization of the CH 2 OO Criegee intermediate, a process implicated in ozonolysis-driven aerosol formation. In this study, we assess the potential contributions of oligomerization through laboratory ozonolysis experiments, direct kinetic studies of Criegee intermediate reactions, and high-level theoretical calculations. Global atmospheric models built on these kinetics results indicate that Criegee intermediate chemistry may play a larger role in altering the composition of Earth’s troposphere than is captured in current atmospheric models, especially in areas of high humidity. However, the models still capture only a relatively small fraction of the observed signatures, suggesting considerable underestimates of Criegee intermediate concentrations and reactivity and/or the dominance of other, presently uncharacterized, oxidation mechanisms. Resolving the remaining uncertainties in emission inventories and the effects of atmospheric water vapour on key chemical reactions will be required to definitively assess the role of Criegee intermediate oligomerization reactions.

58 GEOSCIENCES↗
DOE OSTI2024

Origins of enhanced oxygen reduction activity of transition metal nitrides

Transition metal nitride (TMN-) based materials have recently emerged as promising non-precious-metal-containing electrocatalysts for the oxygen reduction reaction (ORR) in alkaline media. However, the lack of fundamental understanding of the oxide surface has limited insights into structure–(re)activity relationships and rational catalyst design. Here, in this work, we demonstrate how a well-defined TMN can dictate/control the as-formed oxide surface and the resulting ORR electrocatalytic activity. Structural characterization of MnN nanocuboids revealed that an electrocatalytically active Mn 3 O 4 shell grew epitaxially on the MnN core, with an expansive strain along the [010] direction to the surface Mn 3 O 4 . The strained Mn 3 O 4 shell on the MnN core exhibited an intrinsic activity that was over 300% higher than that of pure Mn 3 O 4 . A combined electrochemical and computational investigation indicated/suggested that the enhancement probably originates from a more hydroxylated oxide surface resulting from the expansive strain. This work establishes a clear and definitive atomistic picture of the nitride/oxide interface and provides a comprehensive mechanistic understanding of the structure–reactivity relationship in TMNs, critical for other catalytic interfaces for different electrochemical processes.

electrocatalysis↗
DOE OSTI2024

Bragg glass signatures in Pd x ErTe 3 with X-ray diffraction temperature clustering

The Bragg glass phase is a nearly perfect crystal with glassy features predicted to occur in vortex lattices and charge-density-wave systems in the presence of disorder. Detecting it has been challenging, despite its sharp theoretical definition in terms of diverging correlation lengths. Here we present bulk probe evidence supporting a Bragg glass phase in the systematically disordered charge-density-wave material of Pd x ErTe 3 . We do this by using comprehensive X-ray data and a machine-learning-based analysis tool called X-ray diffraction temperature clustering (X-TEC). We establish a diverging correlation length in samples with moderate intercalation over a wide temperature range. To enable this analysis, we introduced a high-throughput measure of inverse correlation length that we call peak spread. The detection of Bragg glass order and the resulting phase diagram advance our understanding of the complex interplay between disorder and fluctuations. Moreover, the use of our analysis technique to target fluctuations through a high-throughput measure of peak spread can revolutionize the study of fluctuations in scattering experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A resonant valence bond spin liquid in the dilute limit of doped frustrated Mott insulators

Abstract Ideas about resonant valence bond liquids and spin–charge separation have led to key concepts in physics such as quantum spin liquids, emergent gauge symmetries, topological order and fractionalization. Despite extensive efforts to demonstrate the existence of a resonant valence bond phase in the Hubbard model that originally motivated the concept, a definitive realization has yet to be achieved. Here we present a solution to this long-standing problem by uncovering a resonant valence bond phase exhibiting spin–charge separation in realistic Hamiltonians. We show analytically that this ground state emerges in the dilute-doping limit of a half-filled Mott insulator on corner-sharing tetrahedral lattices with frustrated hopping, in the absence of exchange interactions. We confirm numerically that the results extend to finite exchange interactions, finite-sized systems and finite dopant density. Although much attention has been devoted to the emergence of unconventional states from geometrically frustrated interactions, our work demonstrates that kinetic energy frustration in doped Mott insulators may be essential for stabilizing robust, topologically ordered states in real materials.

Physics↗
DOE OSTI2024

Accessing monomers from lignin through carbon–carbon bond cleavage

Lignin, the heterogeneous aromatic macromolecule found in the cell walls of vascular plants, is an abundant feedstock for the production of biochemicals and biofuels. Here, many valorization schemes rely on lignin depolymerization, with decades of research focused on accessing monomers through C–O bond cleavage, given the abundance of β–O–4 bonds in lignin and the large number of available C–O bond cleavage strategies. Monomer yields are, however, invariably lower than desired, owing to the presence of recalcitrant C–C bonds whose selective cleavage remains a major challenge in catalysis. In this Review, we highlight lignin C–C cleavage reactions, including those of linkages arising from biosynthesis (β–1, β–5, β–β and 5–5) and industrial processing (5–CH 2 –5 and α–5). We examine multiple approaches to C–C cleavage, including homogeneous and heterogeneous catalysis, photocatalysis and biocatalysis, to identify promising strategies for further research and provide guidelines for definitive measurements of lignin C–C bond cleavage.

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

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient’s phenotype. Here, the development of two new ontologies — the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation — will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A universal language for finding mass spectrometry data patterns

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here, in this study, we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

Damiani, Tito [Czech Academy of Sciences (CAS), Pr↗
DOE OSTI2023

FAIR for AI: An interdisciplinary and international community building perspective

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

97 MATHEMATICS AND COMPUTING↗