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DOE OSTI2019

Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation

Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology partly because of their potential for very low power computation. SNCs have primarily been demonstrated on machine learning and neural network applications, but they can also be used for applications beyond machine learning that can leverage SNC properties such as massively parallel computation and collocated processing and memory. Here, we demonstrate two graph problems (shortest path and neighborhood subgraph extraction) that can be solved using SNCs. We discuss the approach for mapping these applications to an SNC. We also estimate the performance of a memristive SNC for these applications on three real-world graphs.

Schuman, Catherine↗

DOE OSTI2018

Altered Binding of Tumor Antigenic Peptides to MHC Class I Affects CD8 + T Cell–Effector Responses

T-cell priming occurs when a naïve T cell recognizes cognate peptide–MHC complexes on an activated antigen-presenting cell. The circumstances of this initial priming have ramifications on the fate of the newly primed T cell. Newly primed CD8 + T cells can embark onto different trajectories, with some becoming short-lived effector cells and others adopting a tissue resident or memory cell fate. To determine whether T-cell priming influences the quality of the effector T-cell response to tumors, here we used transnuclear CD8 + T cells that recognize the melanoma antigen TRP1 using TRP1 high or TRP1 low TCRs that differ in both affinity and fine specificity. From a series of altered peptide ligands, we identified a point mutation (K8) in a nonanchor residue that, when analyzed crystallographically and biophysically, destabilized the peptide interaction with the MHC binding groove. In vitro, the K8 peptide induced robust proliferation of both TRP1 high and TRP1 low CD8 + T cells but did not induce expression of PD-1. Cytokine production from K8-stimulated TRP1 cells was minimal, whereas cytotoxicity was increased. Upon transfer into B16 tumor–bearing mice, the reference peptide (TRP1-M9)- and K8-stimulated TRP1 cells were equally effective at controlling tumor growth but accomplished this through different mechanisms. TRP1-M9–stimulated cells produced more IFNγ, whereas K8-stimulated cells accumulated to higher numbers and were more cytotoxic. We, therefore, conclude that TCR recognition of weakly binding peptides during priming can skew the effector function of tumor-specific CD8 + T cells.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

High-order methods for wind turbine simulations and moving to next-generation platforms

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary physics codes in the ExaWind project are Nalu-Wind, which is an unstructured-grid solver for the acoustically incompressible Navier-Stokes equations, and OpenFAST, which is a whole-turbine simulation code. The Nalu-Wind model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as reinitialization of matrices and recomputation of preconditioners is required at every time step. This milestone represents an effort to increase the fidelity of Nalu-Wind at a fixed resolution through the implementation of a tensor-product based, matrix-free high order scheme. High order finite element methods have increased local work per datum communicated and have the potential to provide significantly more accurate solutions at a fixed number of degrees of freedom. Previous to this milestone, Nalu-Wind had an arbitrary order Control Volume Finite Element Method discretization as a solver option, but it required too much memory and was too slow to be of practical use. The work in this milestone addresses these issues by first implementing an implicit, high order solver that only partially assembles the global system. This reduces the memory footprint of the high-order scheme by orders of magnitude for higher polynomial orders. Second, a faster, tensor-product based method for evaluating the action of the left-hand side was implemented. This reduces the amount of computational work required by the scheme and dramatically enhanced the time-to-solution on example problems. Finally, this milestone is an evaluation of the value of high order methods in the wind application space. With the enhancements to memory and computational cost, accuracy vs. time-to-solution was evaluated for several resolutions on an under-resolved Taylor Green vortex test case. Results show that the high order scheme is cost-competitive with the production low-order schemes in Nalu-Wind, being moderately more expensive than the production edge-based vertex centered finite volume scheme. The evaluation of accuracy on the test case shows a potential benefit to high order at the highest resolution while not deteriorating accuracy on the lowest tested resolution. More work is needed to show value in the wind application, but positive strides have been made.

17 WIND ENERGY↗

NASA NTRS1993

High-performance mass storage system for workstations

Reduced Instruction Set Computer (RISC) workstations and Personnel Computers (PC) are very popular tools for office automation, command and control, scientific analysis, database management, and many other applications. However, when using Input/Output (I/O) intensive applications, the RISC workstations and PC's are often overburdened with the tasks of collecting, staging, storing, and distributing data. Also, by using standard high-performance peripherals and storage devices, the I/O function can still be a common bottleneck process. Therefore, the high-performance mass storage system, developed by Loral AeroSys' Independent Research and Development (IR&D) engineers, can offload a RISC workstation of I/O related functions and provide high-performance I/O functions and external interfaces. The high-performance mass storage system has the capabilities to ingest high-speed real-time data, perform signal or image processing, and stage, archive, and distribute the data. This mass storage system uses a hierarchical storage structure, thus reducing the total data storage cost, while maintaining high-I/O performance. The high-performance mass storage system is a network of low-cost parallel processors and storage devices. The nodes in the network have special I/O functions such as: SCSI controller, Ethernet controller, gateway controller, RS232 controller, IEEE488 controller, and digital/analog converter. The nodes are interconnected through high-speed direct memory access links to form a network. The topology of the network is easily reconfigurable to maximize system throughput for various applications. This high-performance mass storage system takes advantage of a 'busless' architecture for maximum expandability. The mass storage system consists of magnetic disks, a WORM optical disk jukebox, and an 8mm helical scan tape to form a hierarchical storage structure. Commonly used files are kept in the magnetic disk for fast retrieval. The optical disks are used as archive media, and the tapes are used as backup media. The storage system is managed by the IEEE mass storage reference model-based UniTree software package. UniTree software will keep track of all files in the system, will automatically migrate the lesser used files to archive media, and will stage the files when needed by the system. The user can access the files without knowledge of their physical location. The high-performance mass storage system developed by Loral AeroSys will significantly boost the system I/O performance and reduce the overall data storage cost. This storage system provides a highly flexible and cost-effective architecture for a variety of applications (e.g., realtime data acquisition with a signal and image processing requirement, long-term data archiving and distribution, and image analysis and enhancement).

Chiang, T.↗

DOE OSTI2020

3D-ReG: A 3D ReRAM-based Heterogeneous Architecture for Training Deep Neural Networks

Deep neural network (DNN) models are being expanded to a broader range of applications. The computational capability of traditional hardware platforms cannot accommodate the growth of model complexity. Among recent technologies to accelerate DNN, resistive memory (ReRAM)-based processing-in-memory (PIM) emerged as a promising solution for DNN inference due to its high efficiency for matrix-based computation. We face two major technical challenges in extending the use of ReRAM-based accelerators for training: (1) full-precision data is essential in back-propagation; (2) the need to support both feed-forward and back-propagation aggravates the data-movement burden. We propose a heterogeneous architecture named as 3D-ReG, which leverages full-precision GPU to ensure training accuracy and low-overhead 3D integration to provide low-cost data movements. Moreover, we introduce conservative and aggressive task-mapping schemes, which partition the computation phases in different ways to balance execution efficiency and training accuracy. We evaluate 3D-ReG implemented with two 3D integration technologies, through-silicon vias (TSVs) and monolithic inter-tier vias (MIVs), and compare them with GPU-only and PIM-only counterparts. Various GPU-only platforms using two main-memory technologies (DRAM, ReRAM) and three interconnect technologies (2D, TSV, MIV) are evaluated as well. Experimental results show that 3D-ReG can achieve on average 5.64× training speedup and 3.56× higher energy efficiency compared with the GPU with DRAM as main memory, at the cost of 0.05%–3.39% accuracy drop. We define a new metric, gain-loss ratio (GLR), which quantitatively evaluates the capability of a DNN training hardware in terms of the model accuracy and hardware efficiency. The results of our comparison show that the aggressive task-mapping scheme on MIV-based 3D-ReG outperforms the other methods.

Computer Science↗

NASA NTRS2012

High-Temperature Shape Memory Polymers

physical conformation changes when exposed to an external stimulus, such as a change in temperature. Such materials have a permanent shape, but can be reshaped above a critical temperature and fixed into a temporary shape when cooled under stress to below the critical temperature. When reheated above the critical temperature (Tc, also sometimes called the triggering or switching temperature), the materials revert to the permanent shape. The current innovation involves a chemically treated (sulfonated, carboxylated, phosphonated, or other polar function group), high-temperature, semicrystalline thermoplastic poly(ether ether ketone) (Tg .140 C, Tm = 340 C) mix containing organometallic complexes (Zn++, Li+, or other metal, ammonium, or phosphonium salts), or high-temperature ionic liquids (e.g. hexafluorosilicate salt with 1-propyl-3- methyl imidazolium, Tm = 210 C) to form a network where dipolar or ionic interactions between the polymer and the low-molecular-weight or inorganic compound forms a complex that provides a physical crosslink. Hereafter, these compounds will be referred to as "additives". The polymer is semicrystalline, and the high-melt-point crystals provide a temporary crosslink that acts as a permanent crosslink just so long as the melting temperature is not exceeded. In this example case, the melting point is .340 C, and the shape memory critical temperature is between 150 and 250 C. PEEK is an engineering thermoplastic with a high Young fs modulus, nominally 3.6 GPa. An important aspect of the invention is the control of the PEEK functionalization (in this example, the sulfonation degree), and the thermal properties (i.e. melting point) of the additive, which determines the switching temperature. Because the compound is thermoplastic, it can be formed into the "permanent" shape by conventional plastics processing operations. In addition, the compound may be covalently cross - linked after forming the permanent shape by S-PEEK by applying ionizing radiation ( radiation, neutrons), or by chemical crosslinking to form a covalent permanent network. With respect to other shape memory polymers, this invention is novel in that it describes the use of a thermoplastic composition that can be thermally molded or solution-cast into complex "permanent" shapes, and then reheated or redissolved and recast from solution to prepare another shape. It is also unique in that the shape memory behavior is provided by a non-polymer additive.

Yoonessi, Mitra↗

DOE OSTI2021

Nanocrystalline strain glass TiNiPt and its superelastic behavior

TiNi-based shape-memory alloys are known to exhibit a strain glass state under certain conditions, generally in the presence of high-density defects such as excess solute atoms or alloying elements, dislocations, and nanoprecipitates. In this paper, we report a strain glass transition in a nanocrystalline Ti 50 Ni 35 Pt 15 alloy. The nanocrystalline strain glass state is achieved by a combined effect of high-density grain boundaries and high concentration doping of Pt atoms in the B2 matrix. The nanocrystalline Ti 50 Ni 35 Pt 15 strain glass alloy showed a large near-complete progressive superelasticity with a recovery strain of about 6% and a low apparent Young's modulus of about 30 GPa in a wide temperature range of over 200 °C. In situ synchrotron x-ray diffraction measurement showed that the strain glass B2 [B2(SG)] phase experienced B2(SG)→R→B19 transformation upon loading and B19→B2(SG) upon unloading. The findings of this study provide insight for the development of nanocrystalline strain glass shape-memory alloys.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2006

Implementing Access to Data Distributed on Many Processors

A reference architecture is defined for an object-oriented implementation of domains, arrays, and distributions written in the programming language Chapel. This technology primarily addresses domains that contain arrays that have regular index sets with the low-level implementation details being beyond the scope of this discussion. What is defined is a complete set of object-oriented operators that allows one to perform data distributions for domain arrays involving regular arithmetic index sets. What is unique is that these operators allow for the arbitrary regions of the arrays to be fragmented and distributed across multiple processors with a single point of access giving the programmer the illusion that all the elements are collocated on a single processor. Today's massively parallel High Productivity Computing Systems (HPCS) are characterized by a modular structure, with a large number of processing and memory units connected by a high-speed network. Locality of access as well as load balancing are primary concerns in these systems that are typically used for high-performance scientific computation. Data distributions address these issues by providing a range of methods for spreading large data sets across the components of a system. Over the past two decades, many languages, systems, tools, and libraries have been developed for the support of distributions. Since the performance of data parallel applications is directly influenced by the distribution strategy, users often resort to low-level programming models that allow fine-tuning of the distribution aspects affecting performance, but, at the same time, are tedious and error-prone. This technology presents a reusable design of a data-distribution framework for data parallel high-performance applications. Distributions are a means to express locality in systems composed of large numbers of processor and memory components connected by a network. Since distributions have a great effect on the performance of applications, it is important that the distribution strategy is flexible, so its behavior can change depending on the needs of the application. At the same time, high productivity concerns require that the user be shielded from error-prone, tedious details such as communication and synchronization.

James, Mark↗

DOE OSTI2024

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗

DOE OSTI2014

Current-driven domain wall motion enhanced by the microwave field

The magnetic domain wall (DW) motion driven by a spin-polarized current opens a new concept for memory and logic devices. However, the critical current density required to overcome the intrinsic and/or extrinsic pinning of DW remains too large for practical applications. Here, we show, by using micromagnetic simulations and analytical approaches, that the application of a microwave field offers an effective solution to this problem. When a transverse microwave field is applied, the adiabatic spin-transfer torque (STT) alone can sustain a steady-state DW motion without the sign of Walker breakdown, meaning that the intrinsic pinning disappears. The extrinsic pinning can also be effectively reduced. Moreover, the DW velocity is increased greatly for the microwave-assisted DW motion. This provides a new way to manipulate the DW motion at low current densities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2015

Large magnetic entropy change and magnetoresistance in a Ni 41 Co 9 Mn 40 Sn 10 magnetic shape memory alloy

A polycrystalline Ni 41 Co 9 Mn 40 Sn 10 (at. %) magnetic shape memory alloy was prepared by arc melting and characterized mainly by magnetic measurements, in-situ high-energy X-ray diffraction (HEXRD), and mechanical testing. A large magnetoresistance of 53.8% (under 5 T) and a large magnetic entropy change of 31.9 J/(kg K) (under 5 T) were simultaneously achieved. Both of these values are among the highest values reported so far in Ni-Mn-Sn-based Heusler alloys. The large magnetic entropy change, closely related to the structural entropy change, is attributed to the large unit cell volume change across martensitic transformation as revealed by our in-situ HEXRD experiment. Furthermore, good compressive properties were also obtained. Lastly, the combination of large magnetoresistance, large magnetic entropy change, and good compressive properties, as well as low cost makes this alloy a promising candidate for multifunctional applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

In Situ/Operando (Soft) X-ray Spectroscopy Study of Beyond Lithium-ion Batteries

The lightweight, rechargeable lithium-ion battery is one of the dominant energy storage devices globally in portable electronics due to its high energy density, no memory effect, wide operating voltage, lightweight, and good charge efficiency. However, due to safety concerns, the depletion of lithium reserves, and the corresponding increase of cost, an alternative battery system becomes more and more desirable. To develop alternative battery systems with low cost and high material abundance, for example, sodium, magnesium, zinc, and calcium, it is important to understand the chemical and electronic structure of materials. Additionally, soft X-ray spectroscopy, for example, X-ray absorption spectroscopy (XAS), X-ray emission spectroscopy (XES), and resonant inelastic soft X-ray scattering (RIXS), is an element-specific technique with sensitivity to the local chemical environment and structural order of the element of interest. Modern soft X-ray systems enable operando experiments that can be applied to amorphous and crystalline samples, making it a powerful tool for studying the electronic and structural changes in electrode and electrolyte species. In this article, the application of in situ/operando (soft) X-ray spectroscopy in beyond lithium-ion batteries is reviewed to demonstrate how such spectroscopic characterizations could facilitate the interpretation of interfacial phenomena under in situ/operando condition and subsequent development of the beyond lithium-ion batteries.

(soft) X-ray spectroscopy↗

DOE OSTI2025

Sylvester-preconditioned adaptive-rank implicit time integrators for advection-diffusion equations with variable coefficients

Here, we consider the adaptive-rank integration of multi-dimensional time-dependent advection-diffusion partial differential equations (PDEs) with variable coefficients. We employ a standard finite-difference method for spatial discretization coupled with high-order diagonally implicit Runge-Kutta temporal schemes. The discrete equation is a generalized Sylvester equation (GSE), which we solve with a projection-based adaptive-rank algorithm structured around two key strategies: (i) constructing dimension-wise subspaces using a novel atypical extended Krylov strategy, and (ii) efficiently solving the basis coefficient matrix with a preconditioned GMRES solver. The low-rank decomposition is performed in 2D using SVD and with high-order SVD (HOSVD) in 3D to represent the tensor in a compressed Tucker format. For d-dimensional problems (here, d = 2 or 3), the computational complexity and memory storage of the approach are found numerically to scale as and $\mathscr{O}(Nr^2) + \mathscr{O} (r^{d+1})$ and $\mathscr{O}(Nr) + \mathscr{O} (r^{d})$, respectively, with the one-dimensional resolution and the maximal rank during the Krylov iteration (which we find to be largely independent of on our numerical examples). We present numerical examples that illustrate the advertised properties of the algorithm.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Targeting Human Central Nervous System Protein Kinases: An Isoform Selective p38αMAPK Inhibitor That Attenuates Disease Progression in Alzheimer’s Disease Mouse Models

The first kinase inhibitor drug approval in 2001 initiated a remarkable decade of tyrosine kinase inhibitor drugs for oncology indications, but a void exists for serine/threonine protein kinase inhibitor drugs and central nervous system indications. Stress kinases are of special interest in neurological and neuropsychiatric disorders due to their involvement in synaptic dysfunction and complex disease susceptibility. Clinical and preclinical evidence implicates the stress related kinase p38αMAPK as a potential neurotherapeutic target, but isoform selective p38αMAPK inhibitor candidates are lacking and the mixed kinase inhibitor drugs that are promising in peripheral tissue disease indications have limitations for neurologic indications. Therefore, pursuit of the neurotherapeutic hypothesis requires kinase isoform selective inhibitors with appropriate neuropharmacology features. Synaptic dysfunction disorders offer a potential for enhanced pharmacological efficacy due to stress-induced activation of p38αMAPK in both neurons and glia, the interacting cellular components of the synaptic pathophysiological axis to be modulated. We report a novel isoform selective p38αMAPK inhibitor, MW01-18-150SRM (= MW150), that is efficacious in suppression of hippocampal-dependent associative and spatial memory deficits in two distinct synaptic dysfunction mouse models. A synthetic scheme for biocompatible product and positive outcomes from pharmacological screens are presented. The high-resolution crystallographic structure of the p38αMAPK:MW150 complex documents active site binding, reveals a potential low energy conformation of the bound inhibitor, and suggests a structural explanation for MW150’s exquisite target selectivity. As far as we are aware, MW150 is without precedent as an isoform selective p38MAPK inhibitor or as a kinase inhibitor capable of modulating in vivo stress related behavior

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Calculation of energy-barrier lowering by incoherent switching in spin-transfer torque magnetoresistive random-access memory

To make a useful spin-transfer torque magnetoresistive random-access memory (STT-MRAM) device, it is necessary to be able to calculate switching rates, which determine the error rates of the device. In a single-macrospin model, one can use a Fokker-Planck equation to obtain a low-current thermally activated rate ∝exp(−E{sub eff}/k{sub B}T). Here, the effective energy barrier E{sub eff} scales with the single-macrospin energy barrier KV, where K is the effective anisotropy energy density and V the volume. A long-standing paradox in this field is that the actual energy barrier appears to be much smaller than this. It has been suggested that incoherent motions may lower the barrier, but this has proved difficult to quantify. In the present paper, we show that the coherent precession has a magnetostatic instability, which allows quantitative estimation of the energy barrier and may resolve the paradox.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation

Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such large architectures lead to increased accuracy, this is usually accompanied by a larger increase in computation and memory requirements during inference. While this is a non-issue in traditional machine learning (ML) pipelines, the recent confluence of machine learning and fields like the Internet of Things (IoT) has rendered such large architectures infeasible for execution in low-resource settings. For some datasets, large monolithic pipelines may be overkill for simpler inputs. To address this problem, previous efforts have proposed decision cascades where inputs are passed through models of increasing complexity until the desired performance is achieved. However, we argue that cascaded prediction leads to sub-optimal throughput and increased computational cost due to wasteful intermediate computations. To address this, we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a non-cascading, cost-aware learning pipeline as an efficient alternative to cascaded decision architectures. Through experimental evaluation on both real-world and standard datasets, we demonstrate that PaSeR achieves better accuracy while minimizing computational cost relative to cascaded models. Further, we introduce a new metric IoU/GigaFlop to evaluate the balance between cost and performance. On the real-world task of battery material phase segmentation, PaSeR yields 179% improvement over SOTA MatPhase model and a 196% improvement over IDK Cascades under the IoU/GigaFlop metric. We also demonstrate PaSeR’s adaptability to complementary models trained on a noisy MNIST dataset, where it outperforms all baselines on IoU/GigaFlop by an average of 44%.

Srikshan, Bharat↗

DOE OSTI2015

Progress With Multi-Cell Nb3Sn Cavity Development Linked With Sample Materials Characterization

Exploiting both the new Nb3Sn coating system and the materials characterization tools nearby, we report our progress in low-loss Nb3Sn films development. Nb3Sn films a few micrometers thick were grown on Nb coupons as well as single- and multi-cell cavities by the Sn-diffusion technique. Films structure and composition were investigated on coated samples and cavity cutouts with characterization tools including SEM/EDS/EBSD, AFM, XPS, SIMS towards correlating film growth and RF loss to material properties and deposition parameters. Cavity coating efforts focused on establishing techniques for coating progressively more complicated RF structures, and understanding limiting mechanisms in coated cavities. Nb3Sn coated 1.5 GHz 1-cell and 1.3 GHz 2-cell cavities have shown quality factors of 1010 at 4.3 K, with several cavities reaching above Eacc = 10 MV/m. The dominant limiting mechanisms were low field quenches and quality factor degradation above 8 MV/m. The surface data indicates a near-stoichiometric Nb3Sn consistent with the transition temperature and gap measurements. The Nb3Sn layer is covered with Nb2O5 and SnO2 native oxides and has little memory of the pre-coating surface.

Eremeev, Grigory V.↗

DOE OSTI2018

Efficient thermal management of Li-ion batteries with a passive interfacial thermal regulator based on a shape memory alloy [A passive interfacial thermal regulator based on shape memory alloy and its application to battery thermal management]

The poor performance of lithium-ion batteries in extreme temperatures is hindering their wider adoption in the energy sector. A fundamental challenge in battery thermal management systems (BTMSs) is that hot and cold environments pose opposite requirements: thermal transmission at high temperature for battery cooling, and thermal isolation at low temperature to retain the batteries’ internally generated heat, leading to an inevitable compromise of either hot or cold performances. Here, we demonstrate a thermal regulator that adjusts its thermal conductance as a function of the temperature, just as desired for the BTMS. Without any external logic control, this thermal regulator increases battery capacity by a factor of 3 at an ambient temperature (T ambient ) of -20 °C in comparison to a baseline BTMS that is always thermally conducting, while also limiting the battery temperature rise to 5 °C in a very hot environment (T ambient = 45 °C) to ensure safety. The result expands the usability of lithium-ion batteries in extreme environments and opens up new applications of thermally functional devices.

25 ENERGY STORAGE↗