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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Field-Deployable Quantum Memory for Quantum Networking

High-performance quantum memories are an essential component for regulating temporal events in quantum networks. As a component in quantum-repeaters, they have the potential to support the distribution of entanglement beyond the physical limitations of fiber loss. This will enable key applications such as quantum key distribution, network-enhanced quantum sensing, and distributed quantum computing. Here, we present a quantum memory engineered to meet real-world deployment and scaling challenges. The memory technology utilizes a warm rubidium vapor as the storage medium, and operates at room temperature, without the need for vacuum- and/or cryogenic- support. Here, we demonstrate performance specifications of high-fidelity retrieval (95%) and low operation error (10 –2 ) at a storage time of 160 µs for single-photon level quantum memory operations. We further show a substantially improved storage time (with classical-level light) of up to 1ms by suppressing atomic diffusions. The device is housed in an enclosure with a standard 2U rackmount form factor, and can robustly operate on a day scale in a noisy environment. This result marks an important step toward implementing quantum networks in the field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Memory and rejuvenation in glassy systems

Here, the memory effect in a single crystal spin glass (Cu 0.92 Mn 0.08 ) has been measured using 1 Hz ac susceptibility measurements over a reduced temperature range of 0.4 - 0.7 T g and a model of the memory effect has been developed. A double-waiting-time protocol is carried out where the spin glass is first allowed to age at a temperature below T g , T w$_{1}$ , followed by a second aging 4 K lower, T w$_{2}$ . The 4 K separation is sufficient to ensure rejuvenation has occurred. The model is based on calculating overlaps between the growth of the correlation lengths at the two temperatures. It accounts for the absolute magnitude of the memory effect as a function of both waiting times and temperatures. The data can be explained by the memory loss being a function of the relative change in the correlated volume at the first waiting temperature due to growth in the correlations at the second waiting temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Custodial Chiral Symmetry in a Su-Schrieffer-Heeger Electrical Circuit with Memory

Custodial symmetries are common in the standard model of particle physics. They arise when quantum corrections to a parameter are proportional to the parameter itself. Here, we show that a custodial symmetry of the chiral type is also present in a classical Su-Schrieffer-Heeger (SSH) electrical circuit with memory. In the absence of memory, the SSH circuit supports a symmetry-protected topological edge state. Memory induces nonlinearities that break chiral symmetry explicitly and spread the state across the circuit. However, the resulting state is still protected against perturbations by the ensuing custodial chiral symmetry. Furthermore, these predictions can be verified experimentally and demonstrate the interplay between symmetry and memory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

UNITY: Unified Memory and Storage Space

UNITY is a 36-month project focused on providing design and evaluate a new distributed storage paradigm that unifies the traditionally distinct application views of memory- and file-based data storage into a single scalable and resilient environment. The project is a collaboration among Oak Ridge National Laboratory (ORNL, Lead institution), Los Alamos National Labs (LANL), and Georgia Tech (GT). The main contributions of the GT team have been around development of low level systems software for best leveraging the capabilities of new types of persistent memory technologies, and for development of methods for intelligent data management across memory/storage substrates with heterogeneous components. GT contributed the Phoenix library for optimized checkpoint/restart for HPC I/O for systems with non-volatile memory (NVM), the NVStream library for NVM-specialized streaming I/O for HPC workflows, the CoMerge, Mnemo and Kleio solutions for intelligent data management on NVM-based systems. These contributions result in significant improvements in both application performance and system efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Memorial Pools Energy Efficiency Retrofits

The National September 11 Memorial & Museum completed a retrofit of the submergible LED lighting fixtures in its North and South Memorial Pools, located at the World Trade Center site in New York City. The two one-acre pools are illuminated nightly by custom LED fixtures set into their base, and the original lights — in place since 2011 — had begun reaching the end of their useful life, leaving sections of the pools dim or dark. Rather than fully replacing the fixtures, the project team worked with the original manufacturer, Acuity Brands, to retrofit and reuse existing components across all eight pool walls (34 fixtures per wall), reducing cost and waste. Fixtures were shipped in custom crates to Minnesota for retrofitting, then reinstalled onsite by Memorial & Museum staff and contractor ABM. The team addressed two key technical challenges during the project: early leakage in retrofitted fixtures, resolved by introducing vacuum-sealing and nitrogen-fill testing before shipment; and chord damage during transit, resolved with custom-designed shipping crates. All eight pool walls were successfully retrofitted and reinstalled by August 27, 2025, ahead of the 25th anniversary of the September 11, 2001 attacks. Sitewide electrical consumption in September 2025 reflected an approximate 7% reduction, contributing to the institution's broader net-zero and LEED Gold sustainability goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Total-Ionizing-Dose Effects on Long-term Data Retention Characteristics of Commercial 3-D NAND Memories

This article evaluates the data retention characteristics of irradiated multi-level cell (MLC) 3-D NAND flash memories. We irradiate the memory chips by a Co-60 gamma-ray source for up to 50 krad(Si) and then write a random data pattern on the irradiated chip to find its retention characteristics. The experimental results show that the data retention property of the irradiated chips is significantly degraded compared to the un-irradiated ones. We evaluate two independent strategies to improve the data retention characteristics of the irradiated chips. Furthermore, the first method involves high-temperature annealing of the irradiated chip while the second method suggests pre-programming the memory modules before deploying them in radiation-prone environments.

3-D NAND flash↗
DOE OSTI2022

Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices

Metal halide perovskite-based materials have emerged over the past few decades as remarkable solution-processable opto-electronic materials with many intriguing properties and potential applications. Notably, these emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, we review recent progress towards memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality.

14 SOLAR ENERGY↗
DOE OSTI2022

Nonvolatile Electrochemical Random‐Access Memory under Short Circuit

Abstract Electrochemical random‐access memory (ECRAM) is a recently developed and highly promising analog resistive memory element for in‐memory computing. One longstanding challenge of ECRAM is attaining retention time beyond a few hours. This short retention has precluded ECRAM from being considered for inference classification in deep neural networks, which is likely the largest opportunity for in‐memory computing. In this work, an ECRAM cell with orders of magnitude longer retention than previously achieved is developed, and which is anticipated to exceed ten years at 85 °C. This study hypothesizes that the origin of this exceptional retention is phase separation, which enables the formation of multiple effectively equilibrium resistance states. This work highlights the promises and opportunities to use phase separation to yield ECRAM cells with exceptionally long, and potentially permanent, retention times.

Kim, Diana S.↗
DOE OSTI2024

From shockwaves to the gravitational memory effect

We study the relationship between shockwave geometries and the gravitational memory effect in four-dimensional asymptotically flat spacetime. In particular, we show the ’t Hooft commutation relations of shockwave operators are equivalent to the commutation relation between soft and Goldstone modes parametrizing a sector of the gravitational phase space. We demonstrate this equivalence via a diffeomorphism that takes a shockwave metric to a metric whose transverse traceless component is the gravitational memory. The shockwave momentum in ’t Hooft’s analysis is related to the soft graviton mode, which is responsible for the memory effect, while the shift in the shockwave position is related to the Goldstone mode. This equivalence opens new directions to utilize the gravitational memory effect to explore the observational implications of shockwave geometries in flat space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Speeding up and reducing memory usage for scientific machine learning via mixed precision

Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets require significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. Here, we also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FLAMES─Fast, Low-Storage, Accurate, and Memory-Efficient Adaptive Sampling─Approach to Resolve Spatially Dependent Dynamics of Molecular Liquids

Many critical phenomena in soft matter occur at large length scales, necessitating the resolution of their structure and dynamics at low wavenumbers. However, resolving wavenumber-dependent dynamics computationally via molecular dynamics simulations presents significant challenges, as these phenomena span several orders of magnitude in both time and length scales, resulting in high computational costs and memory demands. Here, this work highlights the computational and memory challenges associated with analyzing molecular trajectories in reciprocal space and demonstrates a method to address them. We introduce FLAMESFast, Low-storage, Accurate, and Memory-Efficient adaptive Sampling, which is a direct method for calculation of structure factors, allowing us to select only the required number of wavevectors for binning. We also use wavenumber-dependent time steps to extract dynamics. Our FLAMES approach effectively mitigates computational and memory/storage bottlenecks. We demonstrate the method using simulations of a model system, liquid octane, at various temperatures. Comparisons with experimental data and real space computation show that the FLAMES technique achieves high accuracy in resolving temperature- and spatially dependent dynamics while being significantly more computationally efficient and requiring less memory and storage than methods based on a uniform wavevector grid and fixed temporal spacing.

Chen, Guang [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2025

Neuronal Plasma Membranes as Supramolecular Assemblies for Biological Memory

Biological memory is the ability to develop, retain, and retrieve information over time. Currently, it is widely accepted that memories are stored in synapses (i.e., connections between brain cells throughout the brain) through a process known as synaptic plasticity, which leads to either long-term potentiation (LTP) or long-term depression (LTD). However, the strengthening (LTP) and weakening (LTD) of synapses involve post-translational modifications to neural networks requiring de novo gene expression, a lengthy and energetically expensive process. Recently, we observed that lipid bilayers in the absence of peptides/proteins are capable of LTP, not unlike what has been observed in mammals and birds. As such, this finding has prompted us to postulate that the lipid bilayer provides a good model for understanding the molecular basis of biological memory. Here, in this article, we discuss the status, challenges, and opportunities of neuronal plasma membranes as structures for biological memory and learning, therapeutic targets for various brain disorders, and platforms for neural network developments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Ma, Zhan↗
DOE OSTI2025

Linking structural and rheological memory in disordered soft materials

Linking the macroscopic flow properties and nanoscopic structure is a fundamental challenge to understanding, predicting, and designing disordered soft materials. Under small stresses, these materials are soft solids, while larger loads can lead to yielding and the acquisition of plastic strain, which adds complexity to the task. In this work, we connect the transient structure and rheological memory of a colloidal gel under cyclic shearing across a range of amplitudes via a generalized memory function using rheo-X-ray photon correlation spectroscopy (rheo-XPCS). Our rheo-XPCS data show that the nanometer scale aggregate-level structure recorrelates whenever the change in recoverable strain over some interval is zero. The macroscopic recoverable strain is therefore a measure of the nano-scale structural memory. We further show that yielding in disordered colloidal materials is strongly heterogeneous and that memories of prior deformation can exist even after the material has been subjected to flow.

Kamani, Krutarth M. [Univ. of Illinois at Urbana-C↗
DOE OSTI2021

Domain wall-magnetic tunnel junction spin–orbit torque devices and circuits for in-memory computing

There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction memory elements can be used for computation by manipulating a domain wall, a transition region between magnetic domains, but the experimental study of such devices has been limited by high current densities and low tunnel magnetoresistance. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy, spin–orbit torque switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 171% and average resistance-area product RA = 29 Ω μm2, close to the RA of the unpatterned film. Device initialization variation in switching voltage is shown to be curtailed to 7%–10% by controlling the domain wall initial position, which we show corresponds to 90%–96% accuracy in a domain wall-magnetic tunnel junction full adder simulation. Repeatability of writing and resetting the device is shown. A circuit shows an inverter operation between two devices, showing that a voltage window is large enough, compared to the variation noise, to repeatably operate a domain wall-magnetic tunnel junction circuit. These results make strides in using magnetic tunnel junctions and domain walls for in-memory and neuromorphic computing applications.

Alamdar, Mahshid (ORCID:0000000221732935)↗
DOE OSTI2024

Active oscillatory associative memory

Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions.

Chemistry↗
DOE OSTI2024

Nonvolatile memory cells from hafnium zirconium oxide ferroelectric tunnel junctions using Nb and NbN electrodes

Ferroelectric tunnel junctions (FTJs) utilizing hafnium zirconium oxide (HZO) have attracted interest as non-volatile memory for microelectronics due to ease of integration into back-end-of-line (BEOL) complementary metal oxide semiconductor fabrication. This work examines asymmetric electrode NbN/HZO/Nb devices with 7 nm thick HZO as FTJs in a memory structure, with an output resistance that can be controlled by read and write voltages. The individual FTJs are measured to have a tunneling electroresistance of 10 during the read state without significant filament conduction formation and reasonable ferroelectric performance. Endurance and remanent polarizations of up to 10 5 cycles and 20 μC/cm 2 , respectively, are measured and are shown to be dependent on the cycling voltage. Electrical measurements demonstrate how magnitude of the write pulse can modulate the high state resistance and the read pulse influences both resistance values as well as separation of resistance states. Then, by using two opposite switching FTJ devices in series, a programmable nonvolatile resistor divider is demonstrated. Measurements of these two FTJ unit memory cells show wide applicability to a BEOL microfabrication process for a re-readable, rewritable, and nonvolatile memory cell.

42 ENGINEERING↗
DOE OSTI2026

A fractional calculus framework for open quantum dynamics: From Liouville to Lindblad to memory kernels

Open quantum systems exhibit dynamics ranging from unitary evolution to irreversible dissipation. While the Gorini–Kossakowski–Sudarshan–Lindblad equation uniquely characterizes Markovian completely positive and trace-preserving (CPTP) evolution, many physical platforms display non-Markovian features such as algebraic relaxation and coherence backflow. Fractional calculus provides a natural way to model such long-memory behavior through power-law temporal kernels introduced by fractional time derivatives. Here, we develop a unified framework that embeds fractional master equations within the broader hierarchy of open-system formalisms. The fractional equation forms a structured subclass of memory-kernel models, reduces to the Lindblad form at unit order, and, through Bochner–Phillips subordination, admits a CPTP representation as an average over Lindblad semigroups. Its resolvent structure further connects fractional dynamics to established non-Markovian approaches, including Nakajima–Zwanzig kernels and hierarchical equations of motion, providing a compact surrogate for long-memory effects. This formulation positions fractional calculus as a rigorous and practical language for modeling non-Markovian quantum dynamics in chemical physics and physical chemistry, providing a CPTP-preserving, computationally efficient surrogate for structured condensed-phase environments where long-time memory and dissipation play a central role.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗