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DOE OSTI2025

Quantum chaos, integrability, and late times in the Krylov basis

Quantum chaotic systems are conjectured to display a spectrum whose fine-grained features (gaps and correlations) are well described by random matrix theory (RMT). We propose and develop a complementary version of this conjecture: quantum chaotic systems display a Lanczos spectrum whose local means and covariances are well described by RMT. To support this proposal, we first demonstrate its validity in examples of chaotic and integrable systems. We then show that for Haar-random initial states in RMTs the mean and covariance of the Lanczos spectrum suffice to produce the full long-time behavior of general survival probabilities including the spectral form factor, as well as the spread complexity. In addition, for initial states with continuous overlap with energy eigenstates, we analytically find the long-time averages of the probabilities of Krylov basis elements in terms of the mean Lanczos spectrum. This analysis suggests a notion of eigenstate complexity, the statistics of which differentiate integrable systems and classes of quantum chaos. Lastly, we clarify the relation between spread complexity and the universality classes of RMT by exploring various values of the Dyson index and Poisson distributed spectra.

Combinatorics↗

DOE OSTI2025

An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced due to various issues such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. In this work, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). The choice of data quality problems investigated is informed by the examples in the repository curated in this work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2023

Beyond Expected Values Evolving Metrics for Resource Adequacy Assessment

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Phylogenetic structure of specialization: A new approach that integrates partner availability and phylogenetic diversity to quantify biotic specialization in ecological networks

Abstract Biotic specialization holds information about the assembly, evolution, and stability of biological communities. Partner availabilities can play an important role in enabling species interactions, where uneven partner availabilities can bias estimates of biotic specialization when using phylogenetic diversity indices. It is therefore important to account for partner availability when characterizing biotic specialization using phylogenies. We developed an index, phylogenetic structure of specialization (PSS), that avoids bias from uneven partner availabilities by uncoupling the null models for interaction frequency and phylogenetic distance. We incorporate the deviation between observed and random interaction frequencies as weights into the calculation of partner phylogenetic α‐diversity. To calculate the PSS index, we then compare observed partner phylogenetic α‐diversity to a null distribution generated by randomizing phylogenetic distances among the same number of partners. PSS quantifies the phylogenetic structure (i.e., clustered, overdispersed, or random) of the partners of a focal species. We show with simulations that the PSS index is not correlated with network properties, which allows comparisons across multiple systems. We also implemented PSS on empirical networks of host–parasite, avian seed‐dispersal, lichenized fungi–cyanobacteria, and hummingbird pollination interactions. Across these systems, a large proportion of taxa interact with phylogenetically random partners according to PSS, sometimes to a larger extent than detected with an existing method that does not account for partner availability. We also found that many taxa interact with phylogenetically clustered partners, while taxa with overdispersed partners were rare. We argue that species with phylogenetically overdispersed partners have often been misinterpreted as generalists when they should be considered specialists. Our results highlight the important role of randomness in shaping interaction networks, even in highly intimate symbioses, and provide a much‐needed quantitative framework to assess the role that evolutionary history and symbiotic specialization play in shaping patterns of biodiversity. PSS is available as an R package at https://github.com/cjpardodelahoz/pss .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

SpotSDC: Revealing the Silent Data Corruption Propagation in High-Performance Computing Systems

We report the trend of rapid technology scaling is expected to make the hardware of high-performance computing (HPC) systems more susceptible to computational errors due to random bit flips. Some bit flips may cause a program to crash or have a minimal effect on the output, but others may lead to silent data corruption (SDC), i.e., undetected yet significant output errors. Classical fault injection analysis methods employ uniform sampling of random bit flips during program execution to derive a statistical resiliency profile. However, summarizing such fault injection result with sufficient detail is difficult, and understanding the behavior of the fault-corrupted program is still a challenge. In this article, we introduce SpotSDC, a visualization system to facilitate the analysis of a program's resilience to SDC. SpotSDC provides multiple perspectives at various levels of detail of the impact on the output relative to where in the source code the flipped bit occurs, which bit is flipped, and when during the execution it happens. SpotSDC also enables users to study the code protection and provide new insights to understand the behavior of a fault-injected program. Based on lessons learned, we demonstrate how what we found can improve the fault injection campaign method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Full Counting Statistics of Charge in Chaotic Many-Body Quantum Systems

We investigate the full counting statistics of charge transport in U(1)-symmetric random unitary circuits. We consider an initial mixed state prepared with a chemical potential imbalance between the left and right halves of the system and study the fluctuations of the charge transferred across the central bond in typical circuits. Using an effective replica statistical mechanics model and a mapping onto an emergent classical stochastic process valid at large on-site Hilbert space dimension, we show that charge transfer fluctuations approach those of the symmetric exclusion process at long times, with subleading t –1/2 quantum corrections. Here, we discuss our results in the context of fluctuating hydrodynamics and macroscopic fluctuation theory of classical nonequilibrium systems and check our predictions against direct matrix-product state calculations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Timescales of Quantum Equilibration, Dissipation and Fluctuation in Nuclear Collisions

Understanding the dynamics of equilibration processes in quantum systems as well as their interplay with dissipation and fluctuation is a major challenge in quantum many-body theory. The timescales of such processes are investigated in collisions of atomic nuclei using fully microscopic approaches. Findings from time-dependent Hartree-Fock and time-dependent random-phase approximation calculations are compared for 13 systems over a broad range of energies. The timescale for full mass equilibration ( ~ 2 × 10 - 20 s ) is found to be much larger than timescales for neutron-to-proton equilibration, kinetic energy, and angular momentum dissipations which are on the order of 10 - 21 s . Fluctuations of mass numbers in the fragments and correlations between their neutron and proton numbers build up within only a few 10 - 21 s . This suggests that dissipation is basically not impacted by mass equilibration, but is mostly driven by the exchange of nucleons between the fragments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗

DOE OSTI2021

Chiral Spin Textures in Amorphous Iron–Germanium Thick Films

Topological solitary fields, such as magnetic and polar skyrmions, are envisioned to revolutionize microelectronics. These configurations have been stabilized in solid-state materials with a global inversion symmetry breaking, which translates in magnetic materials into a vector spin exchange known as the Dzyaloshinskii-Moriya interaction (DMI), as well as spin chirality selection and isotropic solitons. This work reports experimental evidence of 3D chiral spin textures, such as helical spins and skyrmions with different chirality and topological charge, stabilized in amorphous Fe-Ge thick films. Overall, these results demonstrate that structurally and chemically disordered materials with a random DMI can resemble inversion symmetry broken systems with similar magnetic properties, moments, and states. Disordered systems are distinguished from systems with global inversion symmetry breaking by their degenerate spin chirality that allows for forming isotropic and anisotropic topological spin textures at remanence, while offering greater flexibility in materials synthesis, voltage, and strain manipulation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Magnetic Materials: Chiral Spin Textures in Amorphous Iron–Germanium Thick Films (Adv. Mater. 8/2021)

Topological solitary fields, such as magnetic and polar skyrmions, are envisioned to revolutionize microelectronics. These configurations have been stabilized in solid-state materials with a global inversion symmetry breaking, which translates in magnetic materials into a vector spin exchange known as the Dzyaloshinskii–Moriya interaction (DMI), as well as spin chirality selection and isotropic solitons. This work reports experimental evidence of 3D chiral spin textures, such as helical spins and skyrmions with different chirality and topological charge, stabilized in amorphous Fe–Ge thick films. These results demonstrate that structurally and chemically disordered materials with a random DMI can resemble inversion symmetry broken systems with similar magnetic properties, moments, and states. In conclusion, disordered systems are distinguished from systems with global inversion symmetry breaking by their degenerate spin chirality that allows for forming isotropic and anisotropic topological spin textures at remanence, while offering greater flexibility in materials synthesis, voltage, and strain manipulation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗

DOE OSTI2023

Contact-Less Integrity Verification of Microelectronics Using Near-Field EM Analysis

Modern microelectronics life-cycle and supply chain ecosystem bring multiple untrusted entities, which can compromise their integrity. A major integrity issue of microelectronics stems from piracy of intellectual properties (IP) and counterfeiting, which causes significant revenue loss to the semiconductor manufacturers. Further, these components often lead to compromised functionality, reliability, security, and safety of an electronic system. This paper presents secure information transmission and probing methods for verifying the integrity of digital integrated circuit (ICs) based on their electromagnetic (EM) near-field emissions and thereby protecting systems against counterfeit components. The proposed method has been tested on both high-level instructions executed by microprocessors or Systems-on-Chip (serving as examples of software), and also logic circuits within FPGA fabrics and ASICs (serving as examples of hardware). The authentication information required by each digital system is generated using a pseudo-random number generator circuit and securely transmitted via near-field magnetic emissions. The authorized party can probe these emissions using a near-field probe, process the acquired signals to improve the signal-to-noise ratio (SNR), and then recover the secure information through matched filtering. Experimental results from commercial SoCs are used to demonstrate the proposed technique. Methods for reducing EM interference during integrity verification of both FPGAs and ASICs are also described.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

Abstract. Uncertainties in an output of interest that depends on the solution of a complex system (e.g., of partial differential equations with random inputs) are often, if not nearly ubiquitously, determined in practice using Monte Carlo (MC) estimation. While simple to implement, MC estimation fails to provide reliable information about statistical quantities (such as the expected value of the output of interest) in application settings such as climate modeling, for which obtaining a single realization of the output of interest is a costly endeavor. Specifically, the dilemma encountered is that many samples of the output of interest have to be collected in order to obtain an MC estimator that has sufficient accuracy – so many, in fact, that the available computational budget is not large enough to effect the number of samples needed. To circumvent this dilemma, we consider using multifidelity Monte Carlo (MFMC) estimation which leverages the use of less costly and less accurate surrogate models (such as coarser grids, reduced-order models, simplified physics, and/or interpolants) to achieve, for the same computational budget, higher accuracy compared to that obtained by an MC estimator – or, looking at it another way, an MFMC estimator obtains the same accuracy as the MC estimator at lower computational cost. The key to the efficacy of MFMC estimation is the fact that most of the required computational budget is loaded onto the less costly surrogate models so that very few samples are taken of the more expensive model of interest. We first provide a more detailed discussion about the need to consider an alternative to MC estimation for uncertainty quantification. Subsequently, we present a review, in an abstract setting, of the MFMC approach along with its application to three climate-related benchmark problems as a proof-of-concept exercise.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

SANS characterization of time dependent, slow molecular exchange in an SDS micellar system

Here, we have investigated the molecular exchange of sodium dodecyl sulfate (SDS) micelles in aqueous solution by time-resolved small angle neutron scattering (TR-SANS) measurements as a function of the surfactant and salt concentration. Starting with deuterated (d-SDS) and protonated (h-SDS) SDS micelles, surfactant exchange across the micelles leads to a randomized distribution of d-SDS and h-SDS within each micelle. By employing the contrast matching technique, we have studied this randomization process which is a direct measure of the molecular exchange of this system. Our results show that the randomization of the pure h-SDS and d-SDS micelles occurs in two steps: first, an almost instantaneous drop in the scattering intensity is observed where ~80% of the micelles are randomized (contrast matched). After this, micelle randomization progresses slowly spanning over ~100 hours. Importantly, we show that the kinetics in the second step are dominated by the formation of domains rich in either h-SDS, d-SDS and randomized (50 : 50 h-SDS : d-SDS). The slow exchange step is modeled via a phenomenological approach by drawing analogy to the Langmuir adsorption theory. Finally, the effects of the surfactant and salt concentrations on the instantaneous, and the time dependent randomization of SDS micelles are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Spacer2PAM: A computational framework to guide experimental determination of functional CRISPR-Cas system PAM sequences

Abstract RNA-guided nucleases from CRISPR-Cas systems expand opportunities for precise, targeted genome modification. Endogenous CRISPR-Cas systems in many prokaryotes are attractive to circumvent expression, functionality, and unintended activity hurdles posed by heterologous CRISPR-Cas effectors. However, each CRISPR-Cas system recognizes a unique set of protospacer adjacent motifs (PAMs), which requires identification by extensive screening of randomized DNA libraries. This challenge hinders development of endogenous CRISPR-Cas systems, especially those based on multi-protein effectors and in organisms that are slow-growing or have transformation idiosyncrasies. To address this challenge, we present Spacer2PAM, an easy-to-use, easy-to-interpret R package built to predict and guide experimental determination of functional PAM sequences for any CRISPR-Cas system given its corresponding CRISPR array as input. Spacer2PAM can be used in a ‘Quick’ method to generate a single PAM prediction or in a ‘Comprehensive’ method to inform targeted PAM libraries small enough to screen in difficult to transform organisms. We demonstrate Spacer2PAM by predicting PAM sequences for industrially relevant organisms and experimentally identifying seven PAM sequences that mediate interference from the Spacer2PAM-informed PAM library for the type I-B CRISPR-Cas system from Clostridium autoethanogenum. We anticipate that Spacer2PAM will facilitate the use of endogenous CRISPR-Cas systems for industrial biotechnology and synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. Further, in this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗