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DOE OSTI2020

A Learning-based Supervisory Control Architecture for Electric Vehicle Charging System Paired with Energy Storages

A Machine Learning-based predictive model is developed for optimal dispatching energy storage system integrated with Electric Vehicle battery charger. The model is effectively trained using transfer learning algorithm and successfully validated via measurement data to achieve high fidelity and accuracy. The study findings provide possibilities for future development of the presented approach to implement computational heavy predictive algorithms with multi-step predictions in emerging modular bidirectional charger topologies to realize vehicle-to-grid (V2G) technology. The online tuning can be explored using the new measurements obtained during the operation, henceforth, enhancing the controller performance for integrating multiple DERs into distribution power networks that shows huge interest for the utilities and regulatory agencies. Multiple operation scenatios have been evaluated and simulation results are discussed to verify intended performance of the proposed control solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.

Cao, Shuxiang↗

DOE OSTI2020

The effect of lighting environment on task performance in buildings – A review

The effects of indoor environmental conditions on human health, satisfaction, and performance have been the focal point of research for decades. This paper reviews and summarizes the impact of lighting environment on task performance, specifically for the built environment audience. Existing studies included a variety of performance tests on cognitive performance and perception, visual acuity and reaction, memory, reasoning, and labor productivity. Illuminance, luminance ratio and correlated color temperature were found to affect performance in different ways, reflecting the impact of experimental techniques, conditions, performance evaluation methods used and data analysis methods. These were reviewed and categorized, with discussion on limitations related to sample size, modeling approach, carryover effects and other factors affecting individual differences in performance, with recommendations for future improvement. Although no universal conclusions can be made, in general, task performance seems to improve with higher illuminances, contrast ratios in the range of 7–11:1 (while always making sure that glare will not occur in the space) and higher correlated color temperature, while spectral tuning in the red or blue wavelengths has also shown positive effects. To obtain more generic evidence, future studies should be more consistent in terms of experimental procedures and overall light conditions, and also consider the effects of vertical illuminance, daylight provision/control, and outside views on task performance. Finally, studying performance with multi-factorial designs in a human-centered optimized manner (such as deploying variable lighting scenarios optimized for various tasks) can lead to deeper understanding of lighting effects on task performance, and ultimately to improved lighting design and operation in buildings overall.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Approaching the Strain-Free Limit in Ultrathin Nanomechanical Resonators

Ultrathin mechanical structures are ideal building platforms to pursue the ultimate limit of nanomechanical resonators for applications in sensing, signal processing, and quantum physics. Unfortunately, as the thickness of the vibrating structures is reduced, the built-in strain of the structural materials plays an increased role in determining the mechanical performance of the devices. As a consequence, it is very challenging to fabricate resonators working in the modulus-dominant regime, where their dynamic behavior is exclusively determined by the device geometry. In this Letter, we report ultrathin doubly clamped nanomechanical resonators with aspect ratios as large as L/t similar to 5000 and working in the modulus-dominant regime. Here, we observed room temperature thermomechanically induced motion of multiple vibration modes with resonant frequencies closely matching the predicted values of Euler-Bernoulli beam theory under an axial strain of 6.3 x 10 -8 . The low strain of the devices enables a record frequency tuning ratio of more than 50 times. These results illustrate a new strategy for the quantitative design of nanomechanical resonators with unprecedented performance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2026

Electrochemical dynamics of imidazolium ionic liquids at graphene electrodes for energy storage applications

Electric double layer capacitors (EDLCs) are prominent energy storage systems that constitute the foundation of more reliable and sustainable energy infrastructures. Modern EDLCs often incorporate ionic liquids (ILs) as a key component in their electrolytes, leveraging the high electrochemical stability of ILs to enhance device performance. The performance and functionality of these capacitors also hinge on the interfacial behavior of ILs at electrode surfaces, which remain insufficiently understood. Here, we performed synchrotron infrared nanospectroscopy (SINS) in combination with density functional theory (DFT) calculations to investigate the electric double layers (EDLs) of three imidazolium-based ILs in a custom-designed graphene liquid cell. This approach revealed new insights into the dynamics of IL EDLs and the underpinning factors originating from the IL structures. Variations in anion size and structure were found to tune the ILs’ ability to form EDLs with compact and closely-correlated ion arrangements, which are critical for enhancing their capacitive performance. These findings highlight the intricate interactions between ions governed by their structures and charge behaviors, which underscores the opportunities for targeted design of IL-based electrolytes to optimize EDLC functionality.

Electric double layers↗

DOE OSTI2023

In-plane uniaxial-strain tuning of superconductivity and charge-density wave in CsV 3 Sb 5

The kagome superconductor CsV 3 Sb 5 with exotic electronic properties has attracted substantial research interest, and the interplay between the superconductivity and the charge-density wave is crucial for understanding its unusual electronic ground state. In this work, we performed resistivity and AC magnetic susceptibility measurements on CsV 3 Sb 5 single crystals uniaxially-strained along [100] and [110] directions. We find that the uniaxial-strain tuning effect ofT c (dT c /dε) andT CDW (dT CDW /dε) are almost identical along these distinct high-symmetry directions. These findings suggest the in-plane uniaxial-strain-tuning ofT c andT CDW in CsV 3 Sb 5 are dominated by associatedc-axis strain, whereas the response to purely in-plane strains is likely small.

Physics↗

DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗

DOE OSTI2021

A Software Framework for Comparing Training Approaches for Spiking Neuromorphic Systems

There are a wide variety of training approaches for spiking neural networks for neuromorphic deployment. However, it is often not clear how these training algorithms perform or compare when applied across multiple neuromorphic hardware platforms and multiple datasets. In this work, we present a software framework for comparing performance across four neuromorphic training algorithms across three neuromorphic simulators and four simple classification tasks. We introduce an approach for training a spiking neural network using a decision tree, and we compare this approach to training algorithms based on evolutionary algorithms, back-propagation, and reservoir computing. We present a hyperparameter optimization approach to tune the hyperparameters of the algorithm, and show that these optimized hyperparameters depend on the processor, algorithm, and classification task. Finally, we compare the performance of the optimized algorithms across multiple metrics, including accuracy, training time, and resulting network size, and we show that there is not one best training algorithm across all datasets and performance metrics.

Schuman, Catherine↗

DOE OSTI2020

Study of the Mu2e sensitivity to the $\mu^-\rightarrow e^+$ conversion process

The Mu2e experiment at Fermilab (Batavia, IL) will search for the coherent neutrinoless conversion of a muon into an electron in the electric field of an aluminum atom. The conversion process results in a monochromatic electron with energy slightly below the muon rest mass ($E_e= 104.97$ MeV). The experiment goal is to improve the previous measurement by four orders of magnitude and reach a single event sensitivity of $3 \times 10^{-17} $ on the conversion rate with respect to the muon capture rate. \\ The experiment will deliver a very intense pulsed negative muon beam to an aluminum target to achieve a total number of $10^{18}$ stopped muons in three years of running. Production and transport of the muons are accomplished with a sophisticated magnetic system comprised of a Production, a Transport and a Detector solenoid, with the last one hosting a Straw-tube Tracker and a Crystal Calorimeter. The entire detector region is surrounded by a Cosmic Ray Veto system. Mu2e is und er construction at the Muon Campus of Fermilab. \\ The INFN italian group is responsible for the design and construction of the crystal calorimeter, which plays a crucial role in the Mu2e measurement, providing particle identification capabilities fundamental for the rejection of the different background sources that can mimic a conversion electron. The calorimeter information allows also to improve the tracking performances it provides a fast standalone trigger for the experiment.\\ After a long R\&D phase, the final drawings see a calorimeter comprised of 1348 pure CsI crystals arranged in two annular disks. Each crystal is readout by two custom UV-extended Silicon Photomultipliers (SiPMs). Performances of the selected components were verified with an electron beam test, carried out in 2017 at the Beam Test Facility (BTF) at the National Laboratories of Frascati, on a large size prototype of 51 crystals and 102 SiPMs, confirming excellent energy ($\sigma_{E}/E = \mathcal{O }(10\%)$) and timing ($\sigma_{T} <500$ ps) resolutions in a! greement with the Mu2e specifications.\\ In the first chapters of this thesis, I describe my involvement in the calorimeter activities, with particular emphasis on the data analysis of the prototype energy response and resolution, that I was personally leading. Moreover, a description of the tuning of the simulation and the careful data-MC comparison is also reported.\\ An auxiliary measurement that can be performed with Mu2e is the search for a Lepton Number Violation (LNV) process where the negatively charged muon converts, without neutrinos, into a positron, $\mu ^{-} +N(A,Z) \rightarrow $ e$^+ +N(A,Z-2)$. At the moment of writing, Neutrinoless Double Beta Decay (0$\nu \beta \beta$) has set the most stringent limit on the LNV processes (T$^{0\nu \beta \beta}_{1/2} > 1.1 \times 10^{26} $ yr at 90$\%$ CL) with the GERDA experiment. In 0$\nu \beta \beta$ a pair of free electrons is created in the transformation from a nucleus $(A, Z)$ into its daughter $(A, Z + 2)$, namely: $(A,Z)\rightarrow (A, Z+2) =2 e^-$. Similar transitions could proceed through the emission of a pair of positrons, double electron capture (EC), or EC and single emission of a positron, with the nucleus changing from $(A, Z)$ to $(A, Z - 2)$. All these variations are equally interesting when discussing of new Physics, since they all manifest a non-conservation of the number of leptons.\\ Moreover, recent studies show that, because of flavor effect, off-diagonal processes, such as the $\mu^- -e^+$ conversion, can have their rate enhanced with respect to processes occurring in the $e-e$ sector.\\ Mu2e will search for $\mu^- + Al(27,13) \rightarrow e^+ +Na(27,11)$, only considering ground state transition, producing a conversion positron with energy $E_{e^+}= 92.32 $ MeV.\\ This thesis is focused on this search. In particular, I developed an algorithm to account for the radiative corrections to be applied to the conversion positrons that resulted in a few percent reduction in s ignal acceptance.\\ One of the most prominent background to ! this sear! ch is the Radiative Muon Capture (RMC) $ \mu+ N(Z,A)\rightarrow \gamma + \nu_{\mu} + N(Z-1,A)$. The produced photon can convert, virtually or in the detector materials (in the following referred as internal or external conversion respectively) in an electron-positron pair, producing positrons with energies approaching the expected conversion energy. Experimental results from previous experiments indicate the RMC endpoint ($\kmax$) to be of $\sim$ 90 MeV. A precise modeling of this background is relevant due to the proximity of the photon spectrum endpoint to the positron conversion energy. The calorimeter group is obviously interested to reconstruct the RMC distribution having also the opportunity to improve the current knowledge of the process. \\ A toy MC has been developed to evaluate the number of $e^+$ from external RMC background in the signal window. Knowing this value and ev...

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Salt Stress Induces Paramylon Accumulation and Fine-Tuning of the Macro-Organization of Thylakoid Membranes in Euglena gracilis Cells

The effects of salt stress condition on the growth, morphology, photosynthetic performance, and paramylon content were examined in the mixotrophic, unicellular, flagellate Euglena gracilis. We found that salt stress negatively influenced cell growth, accompanied by a decrease in chlorophyll (Chl) content. Circular dichroism (CD) spectroscopy revealed the changes in the macro-organization of pigment-protein complexes due to salt treatment, while the small-angle neutron scattering (SANS) investigations suggested a reduction in the thylakoid stacking, an effect confirmed by the transmission electron microscopy (TEM). At the same time, the analysis of the thylakoid membrane complexes using native-polyacrylamide gel electrophoresis (PAGE) revealed no significant change in the composition of supercomplexes of the photosynthetic apparatus. Salt stress did not substantially affect the photosynthetic activity, as reflected by the fact that Chl fluorescence yield, electron transport rate (ETR), and energy transfer between the photosystems did not change considerably in the salt-grown cells. We have observed notable increases in the carotenoid-to-Chl ratio and the accumulation of paramylon in the salt-treated cells. We propose that the accumulation of storage polysaccharides and changes in the pigment composition and thylakoid membrane organization help the adaptation of E. gracilis cells to salt stress and contribute to the maintenance of cellular processes under stress conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Work Function Tuning of a Weak Adhesion Homojunction for Stable Perovskite Solar Cells

Perovskite solar cells (PSCs) have demonstrated a comparable efficiency to Si-based cells. However, the buried interface with weak adhesion remains a critical issue since the ion migration enhanced by the built-in electric field at this interface might lead to instability. We report here that adjusting the energy-level alignment at the weak adhesion homojunction interface can mitigate ion migration and thereby enhance the stability and photovoltaic performance of PSCs. Functional molecules with self-assembled monolayer characteristics were introduced to the surface of the SnO2 layer using silane derivatives, which tuned the work function of the homojunction depending on the functional groups in the molecules and thereby significantly reduced the built-in electric field. The PSC exhibited a power conversion efficiency (PCE) of 25.3%. The maximum power point (MPP) tracking under continuous illumination confirmed that the device retained more than 97% of its initial PCE, even after 1,000 h.

homojunction↗

DOE OSTI2026

Giant Exfoliation Induced Magnetic Coercivity in Fe 3 GaTe 2

Permanent magnets with strong anisotropy and high coercivity underpin modern information and energy technologies, yet rare-earth-free alternatives remain limited. Here, we show that thickness engineering via mechanical exfoliation induces hard magnetic behavior in the van der Waals ferromagnet Fe 3 GaTe 2 . Bulk crystals exhibit Curie temperatures above 350 K but negligible room-temperature coercivity. When thinned below ∼100 nm, the coercive field is dramatically enhanced, reaching nearly 1 T at room temperature for in-plane fields—comparable to conventional hard magnets. Micromagnetic analysis reveals a crossover in magnetization reversal from domain-mediated processes in bulk samples to quasi-coherent rotation in thin flakes, driven by increased effective anisotropy and suppressed domain formation. This thickness-dependent transition enables tuning of magnetic hardness without chemical modification. Combined with high saturation magnetization and robust room-temperature performance, Fe 3 GaTe 2 emerges as a promising rare-earth-free material for spintronic applications. Its layered structure further allows integration into van der Waals heterostructures, where large in-plane coercivity can stabilize magnetic states against perturbations and interlayer coupling, offering potential for high-density nonvolatile memory and domain-wall-based devices.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Information Processing Capacity of Spintronic Oscillator

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗

DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Kim, Seongmin [Department of Chemical and Biologic↗

DOE OSTI2025

Allosteric prediction via convolutional neural networks and protein structural and dynamical features

Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.

Rajeshwar T., Rajitha [Oak Ridge National Laborato↗

DOE OSTI2021

Structure and Dispersion of Free and Grafted Polymer in Nanoparticle Organic Hybrid Materials-Based Solutions by Small-Angle Neutron Scattering

Liquid-like Nanoscale Organic Hybrid Materials or NOHMs consisting of polymer grafted nanoparticles have shown great promise in applications, such as electrochemistry and gas separation, due to their enhanced conductivity, tunability, and negligible vapor pressure. Recently, NOHMs are considered to be used as novel electrolytes in Redox Flow Batteries (RFBs). However, to employ NOHMs in redox flow batteries as electrolytes, it is important to understand the conformation and dispersion of NOHMs in the electrochemical milieu. In this work, we report the use of small-angle neutron scattering to probe the structure and dispersion of Jeffamine M2070 polymer grafted to a SiO 2 nanoparticle in an aqueous solution with and without the presence of a supporting electrolyte. Our results indicate that, in the aqueous environment, there exists a large amount of free polymer in the solution that is not grafted to the functionalized nanoparticles. These protonated free polymers, dispersed in the aqueous solvent, may also strongly interact with the grafted polymer layer and greatly affect the neat structure of NOHMs. Thus, there also exist polymers identified as “interacting” polymers to distinguish them from tethered or truly free polymers in the fluid system. The presence of supporting electrolyte shows a greater effect on the structure of NOHMs-based fluid as it not only alters the structure of the free polymer but also hinders the interaction of the polymer with the functionalized nanoparticles. Moreover, the change in the interaction of the Jeffamine M2070 with the functionalized nanoparticles due to the addition of supporting electrolyte has revealed a drastic change in the viscosities of NOHM solutions. Overall, the dispersion of the free polymer, the interaction of the interacting polymer with grafted polymer, and the change in conformation of free polymer and grafted layers with the addition of supporting electrolyte provide valuable insight into the overall scenario of the electrochemical environment of NOHMs. These results can be applied to fine-tune the structure of liquid-like NOHMs and will aid in a better understanding of their performance as potential electrolytes in RFBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Highly Selective Electrocatalytic Reduction of CO 2 into Methane on Cu–Bi Nanoalloys

Methane (CH 4 ), the main component of natural gas, is one of the most valuable products facilitating energy storage via electricity conversion. However, the poor selectivity and high overpotential for CH 4 formation with metallic Cu catalysts prevent realistic applications. Introducing a second element to tune the electronic state of Cu has been widely used as an effective method to improve catalytic performance, but achieving high selectivity and activity toward CH 4 remains challenging. Here, we successfully synthesized Cu–Bi NPs, which exhibit a CH 4 Faradaic efficiency (FE) as high as 70.6% at -1.2 V versus reversible hydrogen electrode (RHE). The FE of Cu–Bi NPs has increased by approximately 25-fold compared with that of Cu NPs. DFT calculations showed that alloying Cu with Bi significantly decreases the formation energy of *COH formation, the rate-determining step, which explains the improved performance. Further analysis showed that Cu that has been partially oxidized because of electron withdrawal by Bi is the most possible active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗