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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Addressing bias in bagging and boosting regression models

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function. Our methodology measures bias rigorously and extends the ML model's loss function with a regularization term to penalize high correlations between model errors and protected attributes. We applied our approach to three popular tree-based ensemble models: a random forest model (RF), a gradient-boosted model (GBT), and an extreme gradient boosting model (XGBoost). We implemented our methodology on a case study for predicting road-level traffic volume, where RF, GBT, and XGBoost models were shown to have high accuracy. Despite high accuracy, the ML models were shown to perform poorly on roads in minority-populated areas. Our bias mitigation approach reduced minority-related bias by over 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

LumaCam: a novel class of position-sensitive event mode particle detectors using scintillator screens

A new type of position-sensitive detectors is gaining attention in the neutron community. They are scintillator based detectors that detect the scintillation light on an individual photon basis via an image intensifier and a fast image sensor. Their readout operates in event mode i.e. it produces information about individual neutron interactions, reconstructed from the sensor data, thus enabling to achieve superior spatial and temporal resolutions compared to regular detectors. Although the development of current detectors is focused on neutrons, the concept is also applicable to the detection of other particles such as high-energy photons. This document provides a description on how these detectors are built, how they operate, and what their characteristics are. An example of a detector implementation based on a Timepix3 chip is described to illustrate the detector concept. This includes a detailed description of the algorithm that reconstructs the neutron interactions from the sensor data, one of the core components that sets it apart from established scintillator-based imaging detectors. Energy-resolved epithermal neutron radiography was performed at the ISIS EMMA beamline with this detector, illustrating some of the fundamental differences in the data that can be produced with the new type of detector compared to more established types of scintillator based neutron detectors. The term LumaCam is proposed to refer to this new class of position-sensitive event-mode detectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. However, the effectiveness of KAEs is hindered by limited and noisy training datasets, leading to poor generalizability. To address this, we introduce the Temporally-Consistent Koopman Autoencoder (tcKAE), designed to generate accurate long-term predictions even with limited and noisy training data. This is achieved through a consistency regularization term that enforces prediction coherence across different time steps, thus enhancing the robustness and generalizability of tcKAE over existing models. We provide analytical justification for this approach based on Koopman spectral theory and empirically demonstrate tcKAE’s superior performance over state-of-the-art KAE models across a variety of test cases, including simple pendulum oscillations, kinetic plasma, and fluid flow data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Efficient online quantum circuit learning with no upfront training

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Impacts, processes and projections of the quasi-biennial oscillation

In the tropical stratosphere, deep layers of eastward and westward winds encircle the globe and descend regularly from the upper stratosphere to the tropical tropopause. With a complete cycle typically lasting almost 2.5 years, this quasi-biennial oscillation (QBO) is arguably the most predictable mode of atmospheric variability that is not linked to the changing seasons. Here, the QBO affects climate phenomena outside the tropical stratosphere, including ozone transport, the North Atlantic Oscillation and the Madden–Julian Oscillation, and its high predictability could enable better forecasts of these phenomena if models can accurately represent the coupling processes. Climate and forecasting models are increasingly able to simulate stratospheric oscillations resembling the QBO, but exhibit common systematic errors such as weak amplitude in the lowermost tropical stratosphere. Uncertainties about the waves that force the oscillation, particularly the momentum fluxes from small-scale gravity waves excited by deep convection, make its simulation challenging. Improved representation of the processes governing the QBO is expected to lead to better forecasts of the oscillation and its impacts, increased understanding of unusual events such as the two QBO disruptions observed since 2016, and more reliable future projections of QBO behaviour under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Accelerating error correction in tomographic reconstruction

Abstract Spurred by recent advances in detector technology and X-ray optics, upgrades to scanning-probe-based tomographic imaging have led to an exponential growth in the amount and complexity of experimental data and have created a clear opportunity for tomographic imaging to approach single-atom sensitivity. The improved spatial resolution, however, is highly susceptible to systematic and random experimental errors, such as center of rotation drifts, which may lead to imaging artifacts and prevent reliable data extraction. Here, we present a model-based approach that simultaneously optimizes the reconstructed specimen and sinogram alignment as a single optimization problem for tomographic reconstruction with center of rotation error correction. Our algorithm utilizes an adaptive regularizer that is dynamically adjusted at each alternating iteration step. Furthermore, we describe its implementation in a software package targeting high-throughput workflows for execution on distributed-memory clusters. We demonstrate the performance of our solver on large-scale synthetic problems and show that it is robust to a wide range of noise and experimental drifts with near-ideal throughput.

Ali, Sajid (ORCID:0000000321864636)↗
DOE OSTI2023

Uncertainty-driven dynamics for active learning of interatomic potentials

Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effect of the chlorine substitution position of the end-group on intermolecular interactions and photovoltaic performance of small molecule acceptors

The structure–property relationships of small molecular acceptors (SMAs) are a key issue in the molecular design of new-generation acceptor materials for further improving the device efficiencies of polymer solar cells (PSCs). Herein, three couples of SMA isomers were synthesized, based on three central fused ring units and two 1,1-dicyanomethylene-3-indanone (IC) isomer electron-withdrawing terminal units with chlorine substitutions in different positions of its benzene ring: Cl-1 with chlorine on the same side as a C=O group of IC and Cl-2 with chlorine on the same side as the CN groups of IC. Through systematical investigation, we found that the chlorine substitution position of the terminal groups has a regular and significant influence on the molecular packing and photovoltaic performance of the SMAs. The molecular packing behavior of the SMAs is closely related to and determined by the configuration of their terminal groups, no matter which central fused ring of the SMAs is used. In particular, the Cl-1-based SMAs possess a stronger crystallinity with long range ordering packing in their molecular plane direction, while the more abundant and stereoscopic π–π intermolecular interaction in the Cl-2-based SMAs promotes the molecules to form three-dimensional charge transporting channels and leads to their red-shifted absorption and higher electron mobilities. Therefore, the Cl-2-based PSCs exhibit a higher power conversion efficiency (PCE) compared to that of the Cl-1-based devices, and the best PCE of a Cl-2 SMA-based PSC reached 16.42%. These results highlight the importance of the investigation of intermolecular interactions, packing and the arrangement of the SMAs in the solid-state, which may provide direct insights for exploring the relationship between the molecular structure and property of the photovoltaic materials. Moreover, we envision that if fragments such as end groups or side chains with more diverse molecular interactions are added into the design and the subsequent synthesis of the SMAs, this may be beneficial to promoting molecular π–π accumulation and further improving the molecular order, forming suitable molecular packing and morphology in the resulting blend films, and finally affecting the efficiency of the PSCs.

14 SOLAR ENERGY↗
DOE OSTI2020

Photo-driven growth of a monolayer of platinum spherical-nanocrowns uniformly coated on a membrane toward fuel cell applications

Membrane electrode assemblies (MEAs) serve as the core units of polymer electrolyte membrane fuel cells (PEMFCs) and are regularly fabricated by painting a slurry of commercial Pt/C onto a membrane, leading to thick and disordered electrocatalyst layers (CLs). Herein, we report a photo-driven fabrication of MEAs with ultra-thin and ordered CLs by growing a monolayer of dendritic platinum spherical nanocrowns onto each side of a photocatalyst-modified membrane. This approach allows us to control the thickness and platinum loading of ordered CLs by simply varying the concentration of the platinum precursor under tungsten light irradiation. The resultant MEAs exhibit an excellent fuel cell power density of 1.01 ± 0.05 W cm –2 at a platinum loading of 53 ± 1.5 μg cm cathode –2 . Furthermore, this exceptional activity likely arises from the nanostructured platinum crowns, efficient mass transport, and uniform reaction rate in the ultrathin (59 ± 12 nm thick) and ordered CLs according to electrochemical and theoretical investigations.

25 ENERGY STORAGE↗
DOE OSTI2021

The electron-transfer intermediates of the oxygen evolution reaction (OER) as polarons by in situ spectroscopy

The conversion of diffusive forms of energy (electrical and light) into short, compact chemical bonds by catalytic reactions regularly involves moving a carrier from an environment that favors delocalization to one that favors localization. While delocalization lowers the energy of the carrier through its kinetic energy, localization creates a polarization around the carrier that traps it in a potential energy minimum. The trapped carrier and its local distortion—termed a polaron in solids—can play a role as a highly reactive intermediate within energy-storing catalytic reactions but is rarely discussed as such. Here, we present this perspective of the polaron as a catalytic intermediate through recent in situ and time-resolved spectroscopic investigations of photo-triggered electrochemical reactions at material surfaces. The focus is on hole-trapping at metal–oxygen bonds, denoted M–OH*, in the context of the oxygen evolution reaction (OER) from water. The potential energy surface for the hole-polaron defines the structural distortions from the periodic lattice and the resulting “active” site of catalysis. This perspective will highlight how current and future time-resolved, multi-modal probes can use spectroscopic signatures of M–OH* polarons to obtain kinetic and structural information on the individual reaction steps of OER. A particular motivation is to provide the background needed for eventually relating this information to relevant catalytic descriptors by free energies. Lastly, the formation of the O–O chemical bond from the consumption of M–OH*, required to release O 2 and store energy in H 2 , will be discussed as the next target for experimental investigations.

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

A comparative study of Bi, Sb, and BiSb for electrochemical nitrogen reduction leading to a new catalyst design strategy

Recent studies identified Bi as one of the most promising non-noble metal elements that can promote the electrochemical N 2 reduction reaction (ENRR) to produce NH 3 . The electronic features that make Bi a promising ENRR catalyst may also be owned by Sb that belongs to the same group as Bi. Thus, the ENRR properties of Bi, Sb, and a BiSb alloy were investigated comparatively to identify common characteristics that facilitate the ENRR. These catalysts were prepared as uniform coating layers on high surface area carbon felt electrodes, which could serve as both regular electrodes and pseudo-gas diffusion electrodes. The experimental results demonstrated that while Bi and Sb show comparable ENRR performances, the formation of a BiSb alloy distinctively increases the faradic efficiency for NH 3 production. Additionally, the X-ray photoelectron spectroscopy results revealed that Bi in BiSb possesses a partial positive charge while Sb in BiSb possesses a partial negative charge, which can impact the way the catalyst surface interacts with the reactants and reaction intermediates of the ENRR and hydrogen evolution reaction (HER), the major competing reaction with the ENRR. Computational investigations including the Bader charge analysis and Gibbs free energy calculations for the elemental steps of the ENRR and HER provided an explanation of how the formation of a BiSb alloy can change the selectivity for the ENRR. The combined experimental and theoretical results and discussion contained in this study lead to a new strategy for designing efficient metal catalysts for the ENRR. Additionally, this study investigated how the use of gas phase and dissolved N 2 affected the ENRR performances of the Bi, Sb, and BiSb catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atomically dispersed Ag on δ-MnO 2 via cation vacancy trapping for toluene catalytic oxidation

The regulation of metal–support interaction (MSI) and the improvement of noble metal utilization are the frontiers of volatile organic compound (VOC) catalytic oxidation. Here, we report a H 2 O 2 -assisted synthesis process for trapping Ag + onto the engineered Mn vacancies of δ-MnO 2 for the catalytic oxidation of toluene. Due to the strong MSI between Ag and δ-MnO 2 , the monoatomic Ag + anchored on cation vacancies forms longer and weakened Ag–O bonds compared with Ag 2 O, which can extra promote the formation of oxygen vacancies and the activation of adjacent lattice oxygen in Ag–O–Mn units compared with regular nanoparticle counterparts. It is beneficial to the steps of methyl dehydrogenation, demethylation, and benzene ring cleavage. The catalyst yields excellent toluene oxidation activity, enhanced turnover frequency in terms of Ag, and good stability. Finally, we propose in this study a facile design strategy for fabricating stably atomically dispersed metal sites, as well as clarify the structure–activity relationships during toluene catalytic oxidation, especially the important effect of single-atom Ag on the adjacent local structure.

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

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Description of quantum interference using mixed quantum/classical theory of inelastic scattering

Manifestation of the quantum interference effect in the oscillation of scattering cross section is explored using the N 2 + O system as a case study. Calculations are carried out for two electronic PESs of the system, for various initial rotational states of N 2 , in a broad range of N 2 + O collision energies and using three theoretical methods: two versions of the approximate mixed quantum/classical theory (MQCT and AT-MQCT) and the accurate full-quantum coupled-channel method (implemented in MOLSCAT). A good agreement between different methods is observed, especially at high energies. Elastic scattering cross-sections oscillate as a function of collision energy, which is the result of quantum interference. The effects of initial rotational excitation and of the PES properties are studied in detail. For the final (thermally averaged) cross sections, both MOLSCAT and MQCT calculations predict a rather regular pattern of quantum oscillations that persist through a broad range of collision energies and expand into the low-energy regime where quantum scattering resonances are common. The difference between cross sections predicted by MQCT and MOLSCAT decreases from ~8% at low energies to ~2% at high energies. Finally, experimental data available at high collision energies are well reproduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Cutting-edge methods for amplifying the oxygen evolution reaction during seawater electrolysis: a brief synopsis

Electrochemical water splitting has been considered a clean and continual way for hydrogen (H 2 ) production. Direct seawater electrolysis is a potentially attractive technology due to the ample access to seawater and scarce freshwater resources in some regions. However, the presence of impurities (e.g., Cl - , Mg 2+ ) and the resulting corrosion and side reactions, such as the chloride oxidation reaction (ClOR), makes seawater electrocatalysis more challenging than that of fresh or alkaline water due to competition with the oxygen evolution reaction (OER) at the anode. Consequently, much effort has been devoted to developing approaches to enhance OER performance and suppress the ClOR. In this minireview, we summarize three general strategies for enhancing OER activity and selectivity in seawater electrolysis based on three different concepts: (1) the sole development of robust and high-performance OER catalysts in pure seawater electrolytes, (2) the introduction of additives to seawater electrolytes (e.g., alkalis and/or salts without chloride) to enhance the potential equilibrium gap between the ClOR and OER in combination with regular highly active OER catalysts, and (3) a combination of approaches (1) and (2). Finally, the current challenges and potential opportunities for green H 2 production from seawater electrolysis are briefly presented.

08 HYDROGEN↗
DOE OSTI2023

Sequence-dependent self-assembly of supramolecular nanofibers in periodic dynamic block copolymers

Block copolymers (BCPs) can spontaneously self-assemble into various nanostructured morphologies which enable their diverse applications in selective membranes, polymer electrolytes, and optoelectronics. To expand the range of nanostructures accessible to block copolymers, we designed dynamic block copolymers (DBCPs) that combine the phase separation of traditional block copolymers with the supramolecular self-assembly of periodic dynamic polymers. We demonstrate that DBCPs synthesized with a periodic block sequence self-assemble into high aspect ratio supramolecular nanofibers with well-ordered PEG and PDMS domains, in contrast to those synthesized with a random sequence which do not form nanofibers but have disordered morphologies. The periodicity of the block sequence ensures regular placement of dynamic bonds along the polymer chain, which enables stacking of the hydrogen bonding units into ordered 1D supramolecular nanofibers and delays the onset of terminal flow by up to 60 °C compared to the random block sequence. The hierarchically assembled supramolecular nanofibers display complex mechanical and thermal phase behavior arising from the interplay between phase separation of the dissimilar polymer backbones and supramolecular interactions between dynamic bonds. Despite identical bulk composition, the periodic DBCPs demonstrate ionic conductivity values over two orders of magnitude higher than their random counterparts due to the formation of well-ordered, interconnected, high aspect ratio ion-transporting PEG domains. In conclusion, these results highlight the potential for DBCPs as an emerging material platform to achieve advanced, self-assembled nanostructured morphologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Low-chromophore lignin isolation from natural biomass with polyol-based deep eutectic solvents

When attempting to obtain light-color lignin from lignocellulosic biomass or industrial lignin, the available options based on chemical or morphological modification suffer from low yield, high cost, and lack of availability at the required scales. In this study, we adopted a polyhydric-alcohol-based deep eutectic solvent (PA-DES) to directly extract light-color lignin from natural biomass, which is even whiter than native cellulolytic enzyme lignin (CEL). The isolated lignin possessed a high recovery yield (97.36%), regular micro-spherical morphology, enriched β-ether linkage of 58/100Ar, low phenolic hydroxyl content of 1.25 mmol g -1 , minimal carbonyl content of 0.70 mmol g -1 , and less condensed structures, thus yielding a lower content of chromophores. Further, this lignin showed excellent sunscreen effects, which could enhance the SPF of a commercial sunscreen from 15 to 40 with only 5 wt% addition. This study can provide essential guidance for the scale-up production of light-color lignin and obtaining near-complete digestible cellulose for further saccharification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗