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DOE OSTI2025

Emerging applications: Neuromorphic computing and reservoir computing

The emergence of doped hafnium oxide (HfO 2 )-based ferroelectric films has enabled highly scalable and silicon-compatible ferroelectric devices, opening new frontiers in neuromorphic and reservoir computing. Among these, ferroelectric field-effect transistors (FeFETs) are particularly promising due to their analog memory characteristics and unique polarization dynamics. These properties make FeFETs ideal candidates for artificial synapses in neuromorphic architectures, supporting deep neural networks and spiking neural networks based on leaky-integrate-and-fire (LIF) mechanisms. Beyond neuromorphic computing, FeFETs also play a crucial role in physical reservoir computing, leveraging their intrinsic nonlinear and history-dependent behavior for efficient real-time learning. This approach offers significant advantages for time-series processing and edge artificial intelligence (AI) applications, addressing the growing need for energy-efficient computing. As a result, this article explores the principles, key demonstrations, and future potential of FeFET-based neuromorphic and reservoir computing, highlighting their impact on next-generation AI hardware.

36 MATERIALS SCIENCE↗

NASA NTRS1989

Local parallel models for integration of stereo matching constraints and intrinsic image combination

Parallel relaxation computations such as those of connectionist networks offer a useful model for constraint integration and intrinsic image combination in developing a general-purpose stereo matching algorithm. This paper describes such a stereo algorithm that incorporates hierarchical, surface-structure, and edge-appearance constraints that are redefined and integrated at the level of individual candidate matches. The algorithm produces a high percentage of correct decisions on a wide variety of stereo pairs. Its few errors arise when the correlation measures defined by the constraints are either weakened or ambiguous, as in the case of periodic patterns in the images. Two additional mechanisms are discussed for overcoming the remaining errors.

Stewart, Charles V.↗

DOE OSTI2023

XploreNAS : Explore Adversarially Robust and Hardware-efficient Neural Architectures for Non-ideal Xbars

Compute In-Memory platforms such as memristive crossbars are gaining focus as they facilitate acceleration of Deep Neural Networks (DNNs) with high area and compute efficiencies. However, the intrinsic non-idealities associated with the analog nature of computing in crossbars limits the performance of the deployed DNNs. Furthermore, DNNs are shown to be vulnerable to adversarial attacks leading to severe security threats in their large-scale deployment. Thus, finding adversarially robust DNN architectures for non-ideal crossbars is critical to the safe and secure deployment of DNNs on the edge. This work proposes a two-phase algorithm-hardware co-optimization approach called XploreNAS that searches for hardware efficient and adversarially robust neural architectures for non-ideal crossbar platforms. We use the one-shot Neural Architecture Search approach to train a large Supernet with crossbar-awareness and sample adversarially robust Subnets therefrom, maintaining competitive hardware efficiency. Our experiments on crossbars with benchmark datasets (SVHN, CIFAR10, CIFAR100) show up to ~8–16% improvement in the adversarial robustness of the searched Subnets against a baseline ResNet-18 model subjected to crossbar-aware adversarial training. We benchmark our robust Subnets for Energy-Delay-Area-Products (EDAPs) using the Neurosim tool and find that with additional hardware efficiency–driven optimizations, the Subnets attain ~1.5–1.6× lower EDAPs than ResNet-18 baseline.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

27 AlNMR chemical shift of Al(OH)$^{-}_{4}$ calculated from first principles: Assessment of error cancellation in chemically distinct reference and target systems

Predicting accurate nuclear magnetic resonance chemical shieldings relies upon cancellation of different types of errors between the theoretically calculated shielding constant of the analyte of interest and the reference. Often, the intrinsic error in computed shieldings due to basis sets, approximations in the Hamiltonian, description of the wave function, and dynamic effects is nearly identical between the analyte and reference, yet if the electronic structure or sensitivity to local environment differs dramatically, this cannot be taken for granted. Detailed prior work has examined the octahedral trivalent cation Al(H 2 O)$^{3+}_{6}$, accounting for ab initio intrinsic errors. However, the use of this species as a reference for the chemically distinct tetrahedral anion Al(OH)$^{-}_{4}$ requires an understanding of how these errors cancel, in order to define the limits of accurately predicting 27 Al chemical shielding in Al(OH)$^{-}_{4}$. In this work, we estimate the absolute shielding of the 27 Al nucleus in Al(OH)$^{-}_{4}$ at the coupled cluster level (515.1± 5.3 ppm). Shielding sensitivity to the choice of method approximation and atomic basis sets used has been evaluated. Solvent and thermal effects are assessed through ensemble averaging techniques using ab-initio molecular dynamics. The contribution of each type of intrinsic error is assessed for the Al(H 2 O)$^{3+}_{6}$ and Al(OH)$^{-}_{4}$ ions, revealing significant differences that fundamentally hamper the ability to accurately calculate the 27 Al chemical shift of Al(OH)$^{-}_{4}$ from first principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations

Noisy, intermediate-scale quantum computers come with intrinsic limitations in terms of the number of qubits (circuit "width") and decoherence time (circuit "depth") they can have. Here, for the first time, we demonstrate a recently introduced method that breaks a circuit into smaller subcircuits or fragments, and thus makes it possible to run circuits that are either too wide or too deep for a given quantum processor. We investigate the behavior of the method on one of IBM's 20-qubit superconducting quantum processors with various numbers of qubits and fragments. We build noise models that capture decoherence, readout error, and gate imperfections for this particular processor. We then carry out noisy simulations of the method in order to account for the observed experimental results. We find an agreement within 20% between the experimental and the simulated success probabilities, and we observe that recombining noisy fragments yields overall results that can outperform the results without fragmentation.

Ayral, Thomas↗

NASA NTRS1985

A comparison of electronic heterodyne moire deflectometry and electronic heterodyne holographic interferometry for flow measurements

Electronic heterodyne moire deflectometry and electronic heterodyne holographic interferometry are compared as methods for the accurate measurement of refractive index and density change distributions of phase objects. Experimental results are presented to show that the two methods have comparable accuracy for measuring the first derivative of the interferometric fringe shift. The phase object for the measurements is a large crystal of KD*P, whose refractive index distribution can be changed accurately and repeatably for the comparison. Although the refractive index change causes only about one interferometric fringe shift over the entire crystal, the derivative shows considerable detail for the comparison. As electronic phase measurement methods, both methods are very accurate and are intrinsically compatible with computer controlled readout and data processing. Heterodyne moire is relatively inexpensive and has high variable sensitivity. Heterodyne holographic interferometry is better developed, and can be used with poor quality optical access to the experiment.

Decker, A. J.↗

NASA NTRS1985

A comparison of electronic heterodyne moire deflectometry and electronic heterodyne holographic interferometry for flow measurements

Electronic heterodyne moire deflectometry and electronic heterodyne holographic interferometry are compared as methods for the accurate measurement of refractive index and density change distributions of phase objects. Experimental results are presented to show that the two methods have comparable accuracy for measuring the first derivative of the interferometric fringe shift. The phase object for the measurements is a large crystal of KD P, whose refractive index distribution can be changed accurately and repeatably for the comparison. Although the refractive index change causes only about one interferometric fringe shift over the entire crystal, the derivative shows considerable detail for the comparison. As electronic phase measurement methods, both methods are very accurate and are intrinsically compatible with computer controlled readout and data processing. Heterodyne moire is relatively inexpensive and has high variable sensitivity. Heterodyne holographic interferometry is better developed, and can be used with poor quality optical access to the experiment.

Decker, A. J.↗

DOE OSTI2021

Computational Complexity of Neuromorphic Algorithms

Neuromorphic computing has several characteristics that make it an extremely compelling computing paradigm for post Moore computation. Some of these characteristics include intrinsic parallelism, inherent scalability, collocated processing and memory, and event-driven computation. While these characteristics impart energy efficiency to neuromorphic systems, they do come with their own set of challenges. One of the biggest challenges in neuromorphic computing is to establish the theoretical underpinnings of the computational complexity of neuromorphic algorithms. In this paper, we take the first steps towards defining the space and time complexity of neuromorphic algorithms. Specifically, we describe a model of neuromorphic computation and state the assumptions that govern the computational complexity of neuromorphic algorithms. Next, we present a theoretical framework to define the computational complexity of a neuromorphic algorithm. We explicitly define what space and time complexities mean in the context of neuromorphic algorithms based on our model of neuromorphic computation. Finally, we leverage our approach and define the computational complexities of six neuromorphic algorithms: constant function, successor function, predecessor function, projection function, neuromorphic sorting algorithm and neighborhood subgraph extraction algorithm.

Date, Prasanna↗

NASA NTRS1992

Extra high speed modified Lundell alternator parameters and open/short-circuit characteristics from global 3D-FE magnetic field solutions

The combined magnetic vector potential - magnetic scalar potential method of computation of 3D magnetic fields by finite elements, introduced in a companion paper, is used for global 3D field analysis and machine performance computations under open-circuit and short-circuit conditions for an example 14.3 kVA modified Lundell alternator, whose magnetic field is of intrinsic 3D nature. The computed voltages and currents under these machine test conditions were verified and found to be in very good agreement with corresponding test data. Results of use of this modelling and computation method in the study of a design alteration example, in which the stator stack length of the example alternator is stretched in order to increase voltage and volt-ampere rating, are given here. These results demonstrate the inadequacy of conventional 2D-based design concepts and the imperative of use of this type of 3D magnetic field modelling in the design and investigation of such machines.

Wang, R.↗

DOE OSTI2024

Accurate numerical simulations of open quantum systems using spectral tensor trains

Decoherence between qubits is a major bottleneck in quantum computations. Decoherence results from intrinsic quantum and thermal fluctuations as well as noise in the external fields that perform the measurement and preparation processes. With prescribed colored noise spectra for intrinsic and extrinsic noise, we present a numerical method, Quantum Accelerated Stochastic Propagator Evaluation (Q-ASPEN), to solve the time-dependent noise-averaged reduced density matrix in the presence of intrinsic and extrinsic noise. Q-ASPEN is arbitrarily accurate and can be applied to provide estimates for the resources needed to error-correct quantum computations. We employ spectral tensor trains, which combine the advantages of tensor networks and pseudospectral methods, as a variational ansatz to the quantum relaxation problem and optimize the ansatz using methods typically used to train neural networks. Here, the spectral tensor trains in Q-ASPEN make accurate calculations with tens of quantum levels feasible. We present benchmarks for Q-ASPEN on the spin-boson model in the presence of intrinsic noise and on a quantum chain of up to 32 sites in the presence of extrinsic noise. In our benchmark, the memory cost of Q-ASPEN scales as a low-order polynomial in the size of the system once the number of system states surpasses the number of basis functions used in the spectral expansion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Computed entropy spectra for grain boundary segregation in polycrystals

Abstract Grain boundary solute segregation influences most bulk material properties, and understanding solute thermodynamics at grain boundaries is critical for engineering them. However, the vast grain boundary space in polycrystals is challenging to evaluate due to its size, especially for the intrinsically hard-to-compute segregation excess entropy. Here data science methods are used to generate a database of site-wise grain boundary segregation entropy spectra for 155 dilute binary alloys within the harmonic approximation. The spectral framework allows scale bridging between the calculated atomistic site-wise energy-entropy spectra and macroscopic segregation entropy estimates. The results affirm that macroscopic averaging is not sufficient: a spectral treatment of grain boundary segregation is needed to accurately model bulk temperature dependence of grain boundary solute segregation. The calculated spectral entropy database and thermodynamic framework can be applied for both understanding segregation experiments and alloy design exercises, paving the way to a finite-temperature grain boundary genome.

Chemistry↗

DOE OSTI2022

Route to Chemical Accuracy for Computational Uranium Thermochemistry

Benchmark spinor-based relativistic coupled-cluster calculations for the ionization energies of the uranium atom, the uranium monoxide molecule (UO), and the uranium dioxide molecule (UO 2 ) and for the bond dissociation energies of UO and UO 2 are reported. The accuracy of these calculations in the treatments of relativistic, electron correlation, and basis-set effects is analyzed. Furthermore, the intrinsic convergence of the computed results and the favorable comparison with the experimental values demonstrate the unique applicability of the spinor representation of quantum-chemical methods to open-shell uranium-containing atomic and molecular species with uranium oxidation states ranging from U(0) to U(V).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2004

Design Space Issues for Intrinsic Evolvable Hardware

This paper discuss the problem of increased programming time for intrinsic evolvable hardware (EHW) as the complexity of the circuit grows. We develop equations for the size of the population, n, and the number of generations required for the population to converge, ngen, based on L, the length of the programming string. We show that the processing time of the computer becomes negligible for intrinsic EHW since the selection/crossover/mutation steps are only done once per generation, suggesting there is room for use of more complex evolutionary algorithms m intrinsic EHW. F i y , we review the state of the practice and discuss the notion of a system design approach for intrinsic EHW.

Hereford, James↗

DOE OSTI2026

Multiscale modeling of packed-bed microwave reactors and estimation of intrinsic materials' permittivity

Modeling of packed-bed microwave reactors relies on an accurate representation of particle size, shape, and distribution within the bed, as well as the particles' dielectric properties. The measured permittivity of microwave susceptors (powders or structured materials) depends on the geometric features of the particles and the porosity of the bed, as well as the specific form factor of a structured material. These are effective properties and cannot be used to analyze other reactor configurations unless the geometric effects are removed. Therefore, we introduce a methodology for extracting the intrinsic particle permittivity from experimentally measured effective permittivity by combining cavity-based measurements with multiscale simulations and machine learning. Further, we develop the first multiscale model of packed-bed microwave reactors that incorporate particle effects (geometric features, random packing, and particle contact). This approach bridges macroscopic observables with mesoscopic physics, enabling analysis of local hotspots, arcing, and contact effects that control reactor performance. Using polymer-based spherical activated carbon (PBSAC) and silicon carbide (SiC) as examples, we demonstrate that the inferred particle permittivity is consistent with independent experimental heating profiles we collect from microwave reactors without adjustable parameters. Finally, this methodology establishes a foundation for predictive, multiscale design of microwave packed-bed reactors that explicitly accounts for particle-scale effects, enabling the estimation of intrinsic permittivity for the first time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Glutamine-rich regions of the disordered CREB transactivation domain mediate dynamic intra- and intermolecular interactions

The cyclic AMP response element (CRE) binding protein (CREB) is a transcription factor that contains a 280-residue N-terminal transactivation domain and a basic leucine zipper that mediates interaction with DNA. The transactivation domain comprises three subdomains, the glutamine-rich domains Q1 and Q2 and the kinase inducible activation domain (KID). NMR chemical shifts show that the isolated subdomains are intrinsically disordered but have a propensity to populate local elements of secondary structure. The Q1 and Q2 domains exhibit a propensity for formation of short β-hairpin motifs that function as binding sites for glutamine-rich sequences. These motifs mediate intramolecular interactions between the CREB Q1 and Q2 domains as well as intermolecular interactions with the glutamine-rich Q1 domain of the TATA-box binding protein associated factor 4 (TAF4) subunit of transcription factor IID (TFIID). Using small-angle X-ray scattering, NMR, and single-molecule Förster resonance energy transfer, we show that the Q1, Q2, and KID regions remain dynamically disordered in a full-length CREB transactivation domain (CREB TAD ) construct. The CREB TAD polypeptide chain is largely extended although some compaction is evident in the KID and Q2 domains. Paramagnetic relaxation enhancement reveals transient long-range contacts both within and between the Q1 and Q2 domains while the intervening KID domain is largely devoid of intramolecular interactions. Phosphorylation results in expansion of the KID domain, presumably making it more accessible for binding the CBP/p300 transcriptional coactivators. In conclusion, our study reveals the complex nature of the interactions within the intrinsically disordered transactivation domain of CREB and provides molecular-level insights into dynamic and transient interactions mediated by the glutamine-rich domains.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Asymmetric oligomerization state and sequence patterning can tune multiphase condensate miscibility

Endogenous biomolecular condensates, composed of a multitude of proteins and RNAs, can organize into multiphasic structures with compositionally distinct phases. This multiphasic organization is generally understood to be critical for facilitating their proper biological function. However, the biophysical principles driving multiphase formation are not completely understood. Here we use in vivo condensate reconstitution experiments and coarse-grained molecular simulations to investigate how oligomerization and sequence interactions modulate multiphase organization in biomolecular condensates. We demonstrate that increasing the oligomerization state of an intrinsically disordered protein results in enhanced immiscibility and multiphase formation. Interestingly, we find that oligomerization tunes the miscibility of intrinsically disordered proteins in an asymmetric manner, with the effect being more pronounced when the intrinsically disordered protein, exhibiting stronger homotypic interactions, is oligomerized. Our findings suggest that oligomerization is a flexible biophysical mechanism that cells can exploit to tune the internal organization of biomolecular condensates and their associated biological functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Thermodynamic Modeling of Intrinsic Defects in MnBi₂Te₄

This repository contains the computational data supporting the manuscript titled “The critical role of intrinsic defects and many-body interactions on the stability of MnBi₂Te₄.” It includes: 1. DFT data generated using VASP, used for training and benchmarking electronic structure models. 2. Quantum Monte Carlo (QMC) data produced with QMCPACK, used to apply many-body corrections and validate the electronic and magnetic properties of MnBi₂Te₄. 3. Relevant scripts used to run, analyze, and process the calculations, enabling reproducibility and transparency of the workflows.

36 MATERIALS SCIENCE↗