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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing degradation at solid-state battery interfaces using machine-learning interatomic potential

Solid-state batteries featuring fast ion-conducting solid electrolytes are promising next-generation energy storage technologies, yet challenges remain for practical deployment due to electro-chemo-mechanical instabilities at solid-solid interfaces. These interfaces, which include homogeneous/internal interfaces such as grain boundaries (GBs) and heterogeneous/external interfaces between solid-electrolyte and electrode materials, can impede Li-ion transport, deteriorate performance, and eventually lead to cell failure. Here, in this study, we leverage large-scale molecular simulations, enabled by validated machine-learning interatomic potentials, to directly probe the onset of interfacial degradation at the garnet Li 7 La 3 Zr 2 O 12 (LLZO) solid-electrolyte/LiCoO 2 (LCO) cathode interface. By surveying different interfacial geometries and compositions, it is found that Li-deficient interfaces can lead to severe interfacial disordering with cation mixing and Co interdiffusion from LCO into LLZO. By contrast, Li-sufficient interfaces are less disordered, although elemental segregation with local ordering is observed. As a consequence of Co interdiffusion, Co-rich regions are formed at the GBs of LLZO due to cation segregation and trapping effects. This behavior is independent of the GB tilting axis, degree of disorder at the GBs, and Co concentration, which implies Co clustering at GBs is a general phenomenon in polycrystalline LLZO and can dictate its overall transport and mechanical properties. Our findings elucidate the underlying fundamental mechanisms that give rise to experimentally observed physicochemical properties and provide guidelines for interface design that can mitigate interfacial degradation and improve cycling performance.

25 ENERGY STORAGE↗
NASA NTRS1997

Planetary Systems Around Neutron Stars

This project was initiated in 1993, about one year after the announcement of two planets around PSR B1257+12. Its goal was to investigate planetary systems around neutron stars using high precision timing of radio pulsars as a tool. A microsecond precision of the pulse timing analysis, which is equivalent to a millimeter-per-second radial velocity resolution, makes it possible to detect asteroid-mass bodies in orbit around pulsars and to study the dynamics of pulsar planetary systems. The project originally consisted of two longterm efforts: (i) routine observations and timing analysis of the millisecond pulsar PSR B1257+12 which was found to be orbited by at least two earth-mass bodies (Wolszczan and Frail, Nature, 355, 145) and (ii) a sensitive all-sky search for millisecond pulsars to detect further examples of neutron stars with planetary systems. In the third year of the project, it was expanded to include long-term timing observations of slow pulsars in search for planetary systems around these younger neutron stars. The instrumentation used to conduct these investigations included the 305-m Arecibo antenna with the Penn State Pulsar Machine (PSPM-1), the 100-m Effelsberg telescope with the local pulse timing hardware, and the 32-m paraboloid of the Torun Centre for Astronomy in Torun, Poland (TCFA) with the PSPM-2, the second pulsar machine built at Penn State. The PI's collaborators included pulsar groups led by D. Backer (Berkeley), R. Foster (NRL), S. Kulkarni (Caltech), J. Taylor (Princeton) and R. Wielebinski (Bonn). One postdoc (Stuart Anderson), one graduate student (Brian Cadwell) and several undergraduates have been engaged in various aspects of research related to this project.

Wolszczan, Alexander↗
DOE OSTI2022

Predicting battery capacity from impedance at varying temperature and state of charge using machine learning

Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.

25 ENERGY STORAGE↗
DOE OSTI2020

Cost Effective 6.5% Silicon Steel Laminate for Electric Machines

Iowa State University teamed with United Technologies Research Center and the University of Delaware and demonstrated a high switching frequency non-rare earth permanent magnet motor. A prototype motor with 400 Hz switching frequency, dummy MnBi permanent magnet with 8 MGOe energy density, and 6.5%Si steel as the laminate materials was designed, built, and tested. The performance of the prototype met the expectations showing a higher frequency motor enabled by 6.5%Si steel is a viable approach to meet DOE 2022 cost and performance targets for electric vehicle drive. Adoption of 6.5%Si steel will result in 3% improvement in energy conversion efficiency of the soft magnetic materials. In addition, the ability to use non-rare earth magnet for high power density application effectively mitigate the risk of EV traction motor from the rising rare earth price.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Permutation codes for the state assignment of fault tolerant sequential machines

A new fault-tolerant state assignment method is suggested for synchronous sequential machines. It is assumed that the inputs are fault free and that for no input it is possible to reach all or most of the states, whose number may be fairly large. Error correcting codes for the state assignment are generated by permutations of a chosen linear code. A state assignment algorithm is developed and its computational complexity is estimated. Examples are given.

Chen, M.↗
DOE OSTI2019

Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

Seismogenic plate boundaries are posited to behave in a similar manner to a densely packed granular medium, where fault and block systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. Machine learning is used to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick-slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model and discuss the physical basis behind the decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. We demonstrate here novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems.

58 GEOSCIENCES↗
DOE OSTI2020

Synchrotron Imaging of Pore Formation in Li Metal Solid-State Batteries Aided by Machine Learning

High-rate capable, reversible lithium metal anodes are necessary for next generation energy storage systems. In situ tomography of Li/LLZO/Li cells is carried out to track morphological transformations in Li metal electrodes. Machine learning enables tracking the lithium metal morphology during galvanostatic cycling. Nonuniform lithium electrode kinetics are observed at both electrodes during cycling. Hot spots in lithium metal are correlated with microstructural anisotropy in LLZO. Mesoscale modeling reveals that regions with lower effective properties (transport and mechanical) are nuclei for failure. Advanced visualization combined with electrochemistry represents an important pathway toward resolving non-equilibrium effects that limit rate capabilities of solid-state batteries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Dimension-adaptive machine learning-based quantum state reconstruction

Here, we introduce an approach for performing quantum state reconstruction on systems of n qubits using a machine learning-based reconstruction system trained exclusively on m qubits, where m ≥ n. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We demonstrate our technique by performing quantum state reconstruction on randomly sampled systems of one, two, and three qubits using machine learning-based methods trained exclusively on systems containing at least one additional qubit. The reconstruction time required for machine learning-based methods scales significantly more favorably than the training time; hence this technique can offer an overall saving of resources by leveraging a single neural network for dimension-variable state reconstruction, obviating the need to train dedicated machine learning systems for each Hilbert space.

42 ENGINEERING↗
DOE OSTI2019

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

The Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation-based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviate from the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.

42 ENGINEERING↗
NASA NTRS2017

Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated to a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

Quantum Annealing↗
DOE OSTI2026

Multimodal Approaches for Leveraging Domain Knowledge with State-of-the-Art Machine Learning to Engineer Biocatalysts

This grant aimed to accelerate the development of specialized enzymes—biological catalysts essential for sustainable manufacturing and medicine—by integrating traditional laboratory evolution with cutting-edge artificial intelligence. To achieve this, we developed a suite of high-throughput sequencing tools and a centralized database to bridge the gap between a protein’s genetic "code" and its physical function. By training machine learning models on large datasets, we also demonstrated the ability to move beyond slow, trial-and-error testing to a "generative" approach, where AI can independently design new, versatile enzymes like tryptophan synthases. Ultimately, these findings demonstrate that combining laboratory data with computer-guided design enables the engineering of highly efficient biological tools with unprecedented speed and precision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC ( R BC , the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use R BC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust R BC to make the modeled BC mixing state index ( χ mode ) match the one predicted by the ML model ( χ ML ). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χ mode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χ ML (66%). The R BC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.

Meteorology & Atmospheric Sciences↗
DOE OSTI2018

Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKFGPS) is proposed and compared with five existing approaches, including UKFschol, UKF-kappa, UKFmodified, UKF-Delta Q, and the squareroot UKF (SRUKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKFschol, UKF-kappa, and UKF-Delta Q do not work well in some estimations while UKFGPS works well in most cases. UKFmodified and SRUKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.

dynamic state estimation↗
DOE OSTI2022

Machine-Learning for Excited-State Dynamics

The primary objective of this computational chemistry sciences team is to design a machine learning NAMD environment that will utilize current petascale and future exascale computational capabilities to advance understanding of charge and energy flow in materials. Our machine-learning NAMD environment will 1) integrate advanced NAMD capabilities directly into electronic structure software (e.g., ABINIT, Quantum Espresso, VASP, etc.); 2) merge the preparatory tools of Pychemia into PYXAID and Avogadro environments so that massive data collection from NAMD simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness

Here, machine learning regression can predict macroscopic fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. Here we show that a similar approach is successful using event catalogs derived from the continuous data. Our methods are applicable to catalogs of arbitrary scale and magnitude of completeness. We investigate how machine learning regression from an event catalog of laboratory earthquakes performs as a function of the catalog magnitude of completeness. We find that strong model performance requires a sufficiently low magnitude of completeness, and below this magnitude of completeness, model performance saturates.

58 GEOSCIENCES↗