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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Barren Plateaus Preclude Learning Scramblers

Scrambling processes, which rapidly spread entanglement through many-body quantum systems, are difficult to investigate using standard techniques, but are relevant to quantum chaos and thermalization. In this Letter, we ask if quantum machine learning (QML) could be used to investigate such processes. In this study we prove a no-go theorem for learning an unknown scrambling process with QML, showing that it is highly probable for any variational Ansatz to have a barren plateau landscape, i.e., cost gradients that vanish exponentially in the system size. This implies that the required resources scale exponentially even when strategies to avoid such scaling (e.g., from Ansatz -based barren plateaus or no-free-lunch theorems) are employed. Furthermore, we numerically and analytically extend our results to approximate scramblers. Hence, our work places generic limits on the learnability of unitaries when lacking prior information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Resilient Control of Networked Microgrids Using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations

Improving system-level resiliency of networked microgrids against adversarial cyber-attacks is an important aspect in the current regime of increased inverter-based resources (IBRs). To achieve that, this paper contributes in designing a hierarchical control layer, in conjunction with the existing control layers, resilient to adversarial attack signals. Considering model complexities, unknown dynamical behaviors of IBRs, and privacy issues regarding data sharing in multi-party-owned microgrids, designing such a control layer is non-trivial. Here, to tackle these issues, a novel federated reinforcement learning (Fed-RL) method is proposed. To grasp the interconnected dynamics of networked microgrids, the paper develops Federated Soft Actor-Critic (FedSAC) algorithm following the vertical structure of implementing Fed-RL. Next, utilizing the OpenAI Gym interface, we built a custom set-up in GridLAB-D/HELICS co-simulation platform, named Resilient RL Co-simulation (ResRLCoSIM), to train the RL agents with IEEE 123-bus benchmark comprising 3 interconnected microgrids. Finally, the learned policies in the simulation are transferred to the real-time hardware-in-the-loop (HIL) test-bed developed using the high-fidelity Hypersim platform. Finally, experiments show that the simulator-trained RL controllers achieve desirable performance with the test-bed platform, validating the minimization of the sim-to-real gap.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions: Preprint

With the growing size and complexity of turbulent flow models, data compression approaches are of the utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches have been proposed and shown to be effective at producing reduced representations of turbulent flow data. However, these approaches focus solely on training the model using point-wise sample reconstruction losses that do not take advantage of the physical properties of turbulent flows. In this paper, we show that training autoencoders with additional physics-informed regularizations, e.g., enforcing incompressibility and preserving enstrophy, improves the compression model in three ways: (i) the compressed data better conform to known physics for homogeneous isotropic turbulence without negatively impacting point-wise reconstruction quality, (ii) inspection of the gradients of the trained model uncovers changes to the learned compression mapping that can facilitate the use of explainability techniques, and (iii) as a performance byproduct, training losses are shown to converge up to 12x faster than the baseline model.

auto-encoders↗
NASA NTRS2002

Rapid Spacecraft Development: Results and Lessons Learned

The Rapid Spacecraft Development Office (RSDO) at NASA's Goddard Space Flight Center is responsible for the management and direction of a dynamic and versatile program for the definition, competition, and acquisition of multiple indefinite delivery and indefinite quantity contracts - resulting in a catalog of spacecraft buses. Five spacecraft delivery orders have been placed by the RSDO and one spacecraft has been launched. Numerous concept and design studies have been performed, most with the intent of leading to a future spacecraft acquisition. A collection of results and lessons learned is recorded to highlight management techniques, methods and processes employed in the conduct of spacecraft acquisition. Topics include working relationships under fixed price delivery orders, price and value, risk management, contingency reserves, and information restrictions.

Watson, William A.↗
DOE OSTI2019

Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection

The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy landscapes for relating structure and structural dynamics to function. Energy landscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins in them do not always correspond to stable structural states but are instead the result of inherent inaccuracies in semi-empirical molecular energy functions. Due to these challenges, energetics is typically ignored in computational approaches addressing long-standing central questions in computational biology, such as protein decoy selection. In the latter, the goal is to determine over a possibly large number of computationally-generated three-dimensional structures of a protein those structures that are biologically-active/native. In recent work, we have recast our attention on the protein energy landscape and its role in helping us to advance decoy selection. Here, we summarize some of our successes so far in this direction via unsupervised learning. More importantly, we further advance the argument that the energy landscape holds valuable information to aid and advance the state of protein decoy selection via novel machine learning methodologies that leverage supervised learning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitative evaluation of how leveraging protein energy landscapes advances an important problem in protein modeling. However, the ideas and concepts presented here are generally useful to make discoveries in studies aiming to relate molecular structure and structural dynamics to function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Markov Chain Monte Carlo Predictions of Neutron-rich Lanthanide Properties as a Probe of r -process Dynamics

Lanthanide element signatures are key to understanding many astrophysical observables, from merger kilonova light curves to stellar and solar abundances. To learn about the lanthanide element synthesis that enriched our solar system, we apply the statistical method of Markov Chain Monte Carlo to examine the nuclear masses capable of forming the r-process rare-earth abundance peak. We describe the physical constraints we implement with this statistical approach and demonstrate the use of the parallel chains method to explore the multidimensional parameter space. We apply our procedure to three moderately neutron-rich astrophysical outflows with distinct types of r-process dynamics. We show that the mass solutions found are dependent on outflow conditions and are related to the r-process path. We describe in detail the mechanism behind peak formation in each case. We then compare our mass predictions for neutron-rich neodymium and samarium isotopes to the latest experimental data from the CPT at CARIBU. Here, we find our mass predictions given outflows that undergo an extended (n,γ)⇌(γ,n) equilibrium to be those most compatible with both observational solar abundances and neutron-rich mass measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Sulfur-enhanced dynamics of coinage metal(111) surfaces: Step edges versus terraces as locations for metal-sulfur complex formation

The propensity of trace amounts of sulfur adsorbed on coinage metal(111) surfaces to dramatically enhance surface dynamics has been demonstrated by STM observations of accelerated 2D island decay for Cu and Ag. It is generally accepted that this enhancement is due to the formation of adsorbed metal-sulfur complexes, which facilitate surface mass transport of the metal. These complexes were originally proposed to form on terraces following the extraction of metal atoms from step edges and subsequent combination with sulfur on the terraces. However, even when thermodynamically feasible, this mechanism may not be kinetically viable for some complexes due to limited coupling of the complex concentration to the surface diffusion flux of metal atoms. Focusing on the case of Cu, we assess various scenarios where complexes are formed either on terraces or instead directly at step edges, the latter being a new paradigm. In this work, a new pathway is proposed for the formation on terraces. A rich variety of structures incorporating S at step edges exist, which could provide a viable source for complexes, at least from a thermodynamic perspective. However, it is necessary to also assess the activation barrier for complex formation and detachment from step edges. This is facilitated by the nudged-elastic-band analysis of the minimum energy path for this process utilizing machine-learning derived potentials based on density functional theory energetics for the metal-sulfur system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases

Molecular dynamics simulations using an interatomic potential developed by artificial neural network deep machine learning are performed to study the local structural order in Al90Tb10 metallic glass. We show that more than 80% of the Tb-centered clusters in Al90Tb10 glass have short-range order (SRO) with their 17 first coordination shell atoms stacked in a ‘3661’ or ‘15551’ sequence. Medium-range order (MRO) in Bergman-type packing extended out to the second and third coordination shells is also clearly observed. Analysis of the network formed by the ‘3661’ and ‘15551’ clusters show that ~82% of such SRO units share their faces or vertexes, while only ~6% of neighboring SRO pairs are interpenetrating. Such a network topology is consistent with the Bergman-type MRO around the Tb-centers. Moreover, crystal structure searches using genetic algorithm and the neural network interatomic potential reveal several low-energy metastable crystalline structures in the composition range close to Al 90 Tb 10 . Some of these crystalline structures have the ‘3661’ SRO while others have the ‘15551’ SRO. While the crystalline structures with the ‘3661’ SRO also exhibit the MRO very similar to that observed in the glass, the ones with the ‘15551’ SRO have very different atomic packing in the second and third shells around the Tb centers from that of the Bergman-type MRO observed in the glassy phase.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electronic and thermal properties of the phase-change memory material, Ge 2 Sb 2 Te 5 , and results from spatially resolved transport calculations

Here, we report new insights into the electronic, structural, and transport (heat and charge) properties of the phase-change memory material amorphous Ge 2 Sb 2 Te 5 . Using realistic structural models of Konstantinou et al., (2019), we analyze the topology, electronic states, and lattice dynamics with density functional methods, including hybrid-functional calculations and machine-learned interatomic potentials. The Kohn–Sham orbitals near the Fermi level display a strong electron–phonon coupling, and exhibit large energy fluctuations at room temperature. The conduction tail states exhibit larger phonon-induced fluctuations than the valence tail states. To resolve transport at the atomic scale, we employ space-projected electronic conductivity and site-projected thermal conductivity methods. Local analysis of heat transport highlights the role of filamentary networks dominated by Te, with Sb and Ge making progressively smaller contributions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Diameter-dependent multiple proton jumps dictate hydronium and hydroxide transport in carbon nanotubes

Nanofluidic channels impose extreme confinement on water, giving rise to unusual transport phenomena of the liquid. However, how the transport of hydroxide and hydronium ions is influenced by such confinement is still not fully understood. This study employs machine learning-accelerated simulations, based on the SCAN density functional, to investigate proton transfer dynamics in CNTs of varying diameters (0.8 nm to 2.8 nm). The extreme confinement of water inside a 0.8 nm CNT not only enhances the probability of multiple consecutive proton jumps, but also reverses the relative diffusion coefficient of hydronium and hydroxide ions in bulk water. In CNTs with diameters larger than 0.8 nm, hydronium diffuses slightly faster than in bulk water, whereas hydroxide diffusion slows because of its localization near CNT walls, hindering multiple proton jumps. This work highlights the significant impact of nanoscale confinement on proton transfer dynamics, with implications for designing nanoscale systems with controlled proton transport.

Chemistry↗
DOE OSTI2021

pnnl/neural_ODE_ICLR2020

We show how to model discrete ordinary differential equations (ODE) with algebraic nonlinearities as deep neural networks with varying degrees of prior knowledge. We derive the stability guarantees of the network layers based on the implicit constraints imposed on the weight's eigenvalues. Moreover, we show how to use barrier methods to generically handle additional inequality constraints. We demonstrate the prediction accuracy of learned neural ODEs evaluated on open-loop simulations compared to ground truth dynamics with bi-linear terms.

Tuor, Aaron↗
DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗
DOE OSTI2020

Simple and efficient algorithms for training machine learning potentials to force data

Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Uncertainty Aware Deep Learning for Particle Accelerators

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

Rajput, Kishansingh↗
DOE OSTI2025

Landscaper v1

Understanding the inner workings of machine learning models through their loss landscapes offers crucial insights into model properties, optimization dynamics, and generalizability. However, accessing these insights has traditionally required specialized mathematical expertise, limiting broader adoption. Landscaper is an open-source Python package designed to bridge this gap. Landscaper seamlessly integrates a suite of multi-dimensional loss landscape analyses with cutting-edge topological data analysis (TDA) methods. This powerful combination makes both fundamental loss landscape analysis and advanced TDA techniques accessible to the broader scientific ML community, without requiring deep pre-existing mathematical knowledge. Landscaper offers three key functionalities: * Construction: Builds detailed loss landscape representations through versatile low and high-dimensional sampling techniques. * Quantification: Applies advanced metrics, including a novel topological data analysis (TDA) based smoothness metric, enabling new perspectives on model behavior. * Visualization: Offers intuitive tools to visualize and interpret loss landscapes, providing actionable insights beyond traditional performance metrics.

Weber, Gunther [Lawrence Berkeley National Laborat↗
NASA NTRS1992

Technology test bed review

The topics are presented in viewgraph form and include the following: (1) Space Shuttle Main Engine (SSME) technology test bed (TTB) history; (2) TTB objectives; (3) TTB major accomplishments; (4) TTB contributions to SSME; (5) major impacts of 3001 testing; (6) some challenges to computational fluid dynamics (CFD); (7) the high pressure fuel turbopump (HPFTP); and (8) 3001 lessons learned in design and operations.

Mcconnaughey, H. V.↗
NASA NTRS2004

Planning for rover opportunistic science

The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.

artificial intelligence↗