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DOE OSTI2026

Understanding and Controlling Photoexcited Molecules in Complex Environments (Final Technical Report)

Predicting the outcome of a photochemical reaction is complicated by factors outside the scope of tradition theoretical chemistry. On such small scales, fluctuations of the environment render outcomes stochastic, nonadiabatic dynamics blur the separation between nuclear and electronic motion, and ultrafast relaxation renders typical assumptions of equilibrium inappropriate. This project aimed to elucidate and control nonadiabatic molecular dynamics in condensed phases. We developed accurate, efficient simulation tools to study irreversible molecular processes computational across a range of scales. We applied these methodological advances to study electron transfer under exotic conditions where spin and topology opened new pathways for reactivity. We also brought these ideas to bare on light harvesting systems, where energy and charge transport are coupled, and where long lifetimes can be deleterious to function. These advances helped to establish general principles for chemical efficiency and guide the design of nanoscale energy materials, molecular machines, and related technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Hot Gravitons and Gravitational Waves From Kerr Black Holes in the Early Universe

Any abundance of black holes that was present in the early universe will evolve as matter, making up an increasingly large fraction of the total energy density as space expands. This motivates us to consider scenarios in which the early universe included an era that was dominated by low-mass ($M < 5\times 10^8$ g) black holes which evaporate prior to primordial nucleosynthesis. In significant regions of parameter space, these black holes will become gravitationally bound within binary systems, and undergo mergers before evaporating. Such mergers result in three potentially observable signatures. First, any black holes that have undergone one or more mergers will possess substantial angular momentum, causing their Hawking evaporation to produce significant quantities of high-energy gravitons. These products of Hawking evaporation are predicted to constitute a background of hot ($\sim$eV-keV) gravitons today, with an energy density corresponding to $\Delta N_{\rm eff} \sim 0.01-0.03$. Second, these mergers will produce a stochastic background of high-frequency gravitational waves. And third, the energy density of these gravitational waves can be as large as $\Delta N_{\rm eff} \sim 0.3$, depending on the length of time between the mergers and evaporation. These signals are each potentially within the reach of future measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Emergency Asset Positioning for Resilient Transmission Grid Operation

In this paper we study a problem of hurricane emergency preparedness via placement of emergency generation assets prior to its strike. We present a two-stage stochastic model for choosing locations and quantities of emergency storage to help support the power grid through a hurricane event. The expectation of losses in our two-stage model is estimated using the sample average approximation. We construct damage scenarios for sample average approximation using WIND Toolkit meteorological data and fragility curves of various electric grid components. We demonstrate the efficacy of our two-stage planning model by simulating operations during Hurricane Dolly on the 2000-bus transmission test system. Our model, coupled with our scenario selection strategy, is effective at mitigating loss of load when compared to a model without emergency generation assets placed prior to an extreme event.

emergency preparedness↗

DOE OSTI2023

Emergency Generation Asset Positioning for Resilient Transmission Grid Operation: Preprint

emergency preparedness↗

DOE OSTI2021

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, in this work, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Dynamical structure factors of warm dense matter from time-dependent orbital-free and mixed-stochastic-deterministic density functional theory

Abstract We present the first calculations of the inelastic part of the dynamical structure factor (DSF) for warm dense matter (WDM) using time-dependent orbital-free density functional theory (TD-OF-DFT) and mixed-stochastic-deterministic (mixed) Kohn Sham TD-DFT (KS TD-DFT). WDM is an intermediate phase of matter found in planetary cores and laser-driven experiments, where the accurate calculation of the DSF is critical for interpreting x-ray Thomson scattering measurements. Traditional TD-DFT methods, while highly accurate, are computationally expensive, motivating the exploration of TD-OF-DFT and mixed TD-KS-DFT as more efficient alternatives. We applied these methods to experimentally measured WDM systems, including solid-density aluminum and beryllium, compressed beryllium, and carbon–hydrogen mixtures. Our results show that TD-OF-DFT requires a dynamical kinetic energy potential in order to qualitatively capture the plasmon response. Additionally, it struggles with capturing bound electron contributions. In contrast, mixed TD-KS-DFT offers greater accuracy in distinguishing bound and free electron effects, aligning well with experimental data, though at a higher computational cost. This study highlights the trade-offs between computational efficiency and accuracy, demonstrating that TD-OF-DFT remains a valuable tool for rapid scans of parameter space, while mixed TD-KS-DFT should be preferred for high-fidelity simulations. Our findings provide insight into the future development of DFT methods for WDM and suggest potential improvements for TD-OF-DFT.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗

DOE OSTI2025

Discovering $\mu$Hz gravitational waves and ultra-light dark matter with binary resonances

In the presence of a weak gravitational wave (GW) background, astrophysical binary systems act as high-quality resonators, with efficient transfer of energy and momentum between the orbit and a harmonic GW leading to potentially detectable orbital perturbations. In this work, we develop and apply a novel modeling and analysis framework that describes the imprints of GWs on binary systems in a fully time-resolved manner to study the sensitivity of lunar laser ranging, satellite laser ranging, and pulsar timing to both resonant and nonresonant GW backgrounds. We demonstrate that optimal data collection, modeling, and analysis lead to projected sensitivities which are orders of magnitude better than previously appreciated possible, opening up a new possibility for probing the physics-rich but notoriously challenging to access $\mu\mathrm{Hz}$ frequency GWs. We also discuss improved prospects for the detection of the stochastic fluctuations of ultra-light dark matter, which may analogously perturb the binary orbits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Bill Savings vs. Backup Power: Evaluating operational tradeoffs for home solar+storage systems [Slides]

This study explores tradeoffs between the use of home solar+storage systems for backup power versus day-to-day utility bill savings. The study focuses specifically on the “reserve setting” available with most home battery storage systems, which allow the customer to maintain some minimum level of storage in reserve in case of an unforeseen power interruption. The more capacity that is held in reserve, the greater the customer’s ability to ride-through possible power interruptions, but less capacity is then available to manage utility bills on a day-to-day basis. This study evaluates this operational tradeoff across a diverse set of locations and residential electricity tariff structures, relying on Berkeley Lab’s PRESTO model to stochastically simulate power interruption events, and exploring a range of sensitivities, including variations in customer value of lost load (VoLL), interruption frequency, and other key drivers. The results show that, in most circumstances, the opportunity cost of holding storage capacity in reserve, in terms of foregone bill saving, tends to outweigh any gains in reliability value associated with mitigated power interruptions. This finding is robust across tariff structures and across most of the sensitivities considered, including those related to rate level, customer load level, and storage sizing. There are a limited set of circumstances where raising the reserve setting improves the overall customer value (comprised of bill savings plus reliability value). Specifically, that exception occurs when all of the following conditions apply: (a) the customer resides in a location with exceptionally poor reliability, (b) the customer has exceptionally high VoLL; (c) the customer is on a net billing rate or on a TOU rate that allows grid discharging but not grid charging; and (d), depending on the location, the price arbitrage differential on that rate is relatively small. In all other circumstances analyzed, total customer value declines with reserve level.

14 SOLAR ENERGY↗

DOE OSTI2026

Connecting Current and Future Dual Active Galactic Nucleus Searches to LISA and Pulsar Timing Array Gravitational-wave Detections

Abstract Dual active galactic nuclei (DAGN) mark the observable stage of massive black hole (MBH) pairing during galaxy mergers and are the progenitors of the MBH binaries that generate low-frequency gravitational waves. Using the large-volume ASTRID cosmological simulation, we construct mock DAGN catalogs tailored to the selection functions of current (COSMOS-Web and DESI) and forthcoming (Nancy Grace Roman Space Telescope (Roman) and Lynx X-ray Observatory (Lynx)) surveys, enabling direct comparisons between simulations and observations. With realistic observational selections, ASTRID reproduces the observed dual fractions, projected separations, and host-galaxy properties across redshifts. We predict a substantial population of small-separation (<5 kpc) duals that remain inaccessible to current surveys, demonstrating that the apparent paucity of subkiloparsec systems in COSMOS-Web is primarily a consequence of observational selection rather than an intrinsic absence. Following each simulated dual to coalescence, we show that DAGN are effective tracers of MBH mergers: ∼30%–70% merge within ≲1 Gyr, and 20%–60% of these mergers produce gravitational-wave signals detectable by the Laser Interferometer Space Antenna (LISA). Duals observable with Roman and Lynx are the progenitors of ∼10%–50% of low-redshift LISA sources and contribute ∼30% of the PTA-band stochastic gravitational-wave background. We further identify massive green-valley galaxies hosting moderate-luminosity active galactic nuclei (AGN), together with massive star-forming galaxies containing bright quasars at z > 1, as the environments most likely to host imminent MBH binaries. These results establish a unified cosmological framework connecting DAGN demographics, MBH binary evolution, and gravitational-wave sources, while identifying high-priority targets for coordinated electromagnetic and multimessenger observations in the coming decade.

Chen, Nianyi [Max-Planck-Institut für Astrophysik;↗

DOE OSTI2023

Simulation-based characterization of the variability of earthquake risk to buildings in the near-field

Recent advancements in high performance computing platforms and computational workflow for regional-scale simulations are enabling unprecedented modeling of fault-to-structure earthquake processes. Regional simulations resolving ground motions at frequencies relevant to engineered systems are becoming computationally viable and provide a new capability to improve understanding of the geographical distribution and intensity of risk to buildings and critical infrastructure. As computational capabilities advance, it is essential to move beyond illustrative single rupture realizations for scenario earthquake events towards the development of a full suite of rupture realizations that appropriately characterize the range of risk to building systems. The work described in this article investigates the application of a suite of fault rupture realizations with the objective of assessing near-fault, site-specific seismic demand variability for building structures. A representative high-performance regional-scale computational model is utilized to execute ground motion and building response simulations based on 18 kinematic rupture realizations of an M7 strike-slip scenario earthquake. The fault rupture models for the scenario earthquake are created by systematically perturbing the hypocenter location and stochastically generating rupture parameters (slip, rise time, rake angle) to represent a breadth of ground motion intensities resulting from the spatial and temporal variabilities of an earthquake rupture process. The resulting seismic demand variability for three-story (short period) and forty-story (long period) steel moment-resisting frame buildings is characterized in terms of the median and distribution of peak inter-story drift ratio for a range of near-fault sites. The full suite of 18 fault rupture realizations and approximately 280,000 nonlinear dynamic building simulations indicate that the three-story building undergoes higher median seismic demand and significantly greater variability of demand at a given site than the forty-story building, which has important implications for the level of certainty in predicting building performance during an earthquake. The simulations performed provide deeper insight into the relationship between fault rupture parameterization and building response, which is essential information for developing a representative suite of rupture realizations for specific earthquake scenarios.

58 GEOSCIENCES↗

DOE OSTI2024

Extending the computational reach of a superconducting qutrit processor

Quantum computing with qudits is an emerging approach that exploits a larger, more connected computational space, providing advantages for many applications, including quantum simulation and quantum error correction. Nonetheless, qudits are typically afflicted by more complex errors and suffer greater noise sensitivity which renders their scaling difficult. In this work, we introduce techniques to tailor arbitrary qudit Markovian noise to stochastic Weyl–Heisenberg channels and mitigate noise that commutes with our Clifford and universal two-qudit gate in generic qudit circuits. We experimentally demonstrate these methods on a superconducting transmon qutrit processor, and benchmark their effectiveness for multipartite qutrit entanglement and random circuit sampling, obtaining up to 3× improvement in our results. To the best of our knowledge, this constitutes the first-ever error mitigation experiment performed on qutrits. Our work shows that despite the intrinsic complexity of manipulating higher-dimensional quantum systems, noise tailoring and error mitigation can significantly extend the computational reach of today’s qudit processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Heat Transport Hysteresis Generated Through Frequency Switching of a Time-Dependent Temperature Gradient

A stochastic energetics framework is applied to examine how periodically shifting the frequency of a time-dependent oscillating temperature gradient affects heat transport in a nanoscale molecular model. We specifically examine the effects that frequency switching, i.e., instantaneously changing the oscillation frequency of the temperature gradient, has on the shape of the heat transport hysteresis curves generated by a particle connected to two thermal baths, each with a temperature that is oscillating in time. Analytical expressions are derived for the energy fluxes in/out of the system and the baths, with excellent agreement observed between the analytical expressions and the results from nonequilibrium molecular dynamics simulations. We find that the shape of the heat transport hysteresis curves can be significantly altered by shifting the frequency between fast and slow oscillation regimes. We also observe the emergence of features in the hysteresis curves such as pinched loops and complex multi-loop patterns due to the frequency shifting. The presented results have implications in the design of thermal neuromorphic devices such as thermal memristors and thermal memcapacitors.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks

In this work, we compare hydrocarbon production curves obtained from a graph-based reduced-order model with the high-fidelity Discrete Fracture Network (DFN) predictions for a fracture network created using data from a real shale site. We observe that the bounds for the high fidelity DFN model lie within the bounds for the reduced order model, implying that the reduced-order model provides a conservative estimate. Moreover, we found that except for first-passage times and late arriving mass, the production curves from the reduced-order model predict transport accurately. However, it is to be noted that the results are inspite of trading a three-dimensional geometry for a reduced system in the form of a graph, one that is 500–1000 times faster in terms of computational efficiency (for this particular application). In addition, we also compare the production curves for large drawdown and small drawdown using our graph approach. The reduced-order model is successful in showing that the long term productivity is higher in case of small drawdown although the initial productivity is higher for large drawdown. Thus, this reduced-order model offers great potential in uncertainty quantification for production, as well as in providing operators with information to make real-time decisions for optimal production.

03 NATURAL GAS↗

DOE OSTI2023

Quantum spin helices more stable than the ground state: Onset of helical protection

Topological magnetic structures are promising candidates for resilient information storage. An elementary example is spin helices in one-dimensional easy-plane quantum magnets. To quantify their stability, we numerically implement the stochastic Schrödinger equation and time-dependent perturbation theory for spin chains with fluctuating local magnetic fields. Here, we find two classes of quantum spin helices that can reach and even exceed ground-state stability: spin-current-maximizing helices and, for fine-tuned boundary conditions, the recently discovered “phantom helices.” Beyond that, we show that the helicity itself (left or right rotating) is even more stable. We explain these findings by separated helical sectors and connect them to topological sectors in continuous spin systems. The resulting helical protection mechanism is a promising phenomenon for stabilizing helical quantum structures, e.g., in ultracold atoms and solid-state systems. We also identify a third type of phantom helix in the system.

1-dimensional spin chains↗

DOE OSTI2022

Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models

Highly mutable infectious disease pathogens (hm-IDPs) such as HIV and influenza evolve faster than the human immune system can contain them, allowing them to circumvent traditional vaccination approaches and causing over one million deaths annually. Agent-based models can be used to simulate the complex interactions that occur between immune cells and hm-IDP-like proteins (antigens) during affinity maturation—the process by which antibodies evolve. Compared to existing experimental approaches, agent-based models offer a safe, low-cost, and rapid route to study the immune response to vaccines spanning a wide range of design variables. However, the highly stochastic nature of affinity maturation and vast sequence space of hm-IDPs render brute force searches intractable for exploring all pertinent vaccine design variables and the subset of immunization protocols encompassed therein. To address this challenge, we employed deep reinforcement learning to drive a recently developed agent-based model of affinity maturation to focus sampling on immunization protocols with greater potential to improve the chosen metrics of protection, namely the broadly neutralizing antibody (bnAb) titers or fraction of bnAbs produced. Using this approach, we were able to coarse-grain a wide range of vaccine design variables and explore the relevant design space. Our work offers new testable insights into how vaccines should be formulated to maximize protective immune responses to hm-IDPs and how they can be minimally tailored to account for major sources of heterogeneity in human immune responses and various socioeconomic factors. Our results indicate that the first 3 to 5 immunizations, depending on the metric of protection, should be specially tailored to achieve a robust protective immune response, but that beyond this point further immunizations require only subtle changes in formulation to sustain a durable bnAb response.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

A minimally invasive, efficient method for propagation of full-field uncertainty in solid dynamics

In this work, we present a minimally invasive method for forward propagation of material property uncertainty to full-field quantities of interest in solid dynamics. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known a priori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the evolution equations based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the driving term of the evolution equations. The implementation is model independent, storage and communication efficient, and straightforward. We demonstrate its efficiency, accuracy, scaling with dimension of the parameter space, and convergence in distribution with two problems: a quasi-one-dimensional bar impact, and a two material notched plate impact. For the bar impact problem, we provide an analytical solution to PDF of the solution fields for method validation. With the notched plate problem, we also demonstrate good parallel efficiency and scaling of the method.

42 ENGINEERING↗

DOE OSTI2026

Cosmic axions revealed via amplified modulation of the ellipticity of a laser

We propose a new axion dark matter detection strategy that employs optical readout of laser beam ellipticity modulations caused by axion-induced electric fields in a microwave cavity, using electro-optic (EO) crystals, enhanced by externally injected radio-frequency (rf) power. Building upon the variance-based probing method [Phys. Rev. D 107, 103005 (2023).], we extend this concept to the optical domain: A weak probe laser interacts with an EO crystal coupled to the resonant microwave cavity field at cryogenic temperatures, and the axion-induced electric field is revealed through induced ellipticity. The injected rf signal coherently interferes with that of the axion field, amplifying the optical response and significantly improving sensitivity. While our EO-based method employs a Fabry-Pérot resonator, we do not require Michelson interferometers. Our method, hence, enables compact, high-frequency axion searches, across the 0.5–50 GHz range. Operating at cryogenic temperatures not only suppresses thermal backgrounds, but, critically, allows the probing method to mitigate the quantum noise. This approach offers a scalable path forward for axion detection over the ∼(few−200) μ⁢eV mass range—covering the preferred parameter space for the postinflationary Peccei-Quinn axion dark matter—using compact, tunable systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗