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DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗

DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗

DOE OSTI2024

Probing bosonic overdensities with optomechanical sensing

Previous work has shown that optomechanical force sensing can be used for efficient detection of ultralight (sub-eV) dark matter candidates. We propose to extend the reach of this method to the search for ultralight dark matter in gravitationally bound configurations in the Milky Way. We consider three scenarios, each strongly motivated by previous studies: boson stars traveling in the galaxy with virial velocity; a bosonic halo centered around the Sun (a “solar halo”); and a bosonic halo centered around the Earth. For each case, we consider bound states composed of either scalar particles with a Yukawa coupling, or vector particles coupled to baryon minus lepton number charge. Accounting for all experimental constraints on coupling strength, we estimate the sensitivity reach of an optomechanical sensor search. We conclude that, although boson star encounters with Earth would be too infrequent to be detected in the relevant parameter space, current optomechanical force sensing technologies provide promising search capabilities for solar or Earth-bound halos. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Vesicular Stomatitis Virus Transmission Dynamics Within Its Endemic Range in Chiapas, Mexico

Vesicular stomatitis virus (VSV), comprising vesicular stomatitis New Jersey virus (VSNJV) and vesicular stomatitis Indiana virus (VSIV), emerges from its focus of endemic transmission in Southern Mexico to cause sporadic livestock epizootics in the Western United States. A dearth of information on the role of potential arthropod vectors in the endemic region hampers efforts to identify factors that enable endemicity and predict outbreaks. In a two-year, longitudinal study at five cattle ranches in Chiapas, Mexico, insect taxa implicated as VSV vectors (blackflies, sandflies, biting midges, and mosquitoes) were collected and screened for VSV RNA, livestock vesicular stomatitis (VS) cases were monitored, and serum samples were screened for neutralizing antibodies. VS cases were reported during the rainy (n = 20) and post-rainy (n = 2) seasons. Seroprevalence against VSNJV in adult cattle was very high (75–100% per ranch) compared with VSIV (0.6%, all ranches). All four potential vector taxa were sampled, and VSNJV RNA was detected in each of them (11% VSNJV-positive of 874 total pools), while VSIV RNA was only detected in four pools of mosquitoes. Our findings indicate that VSNJV is the dominant serotype across our sampling sites with a variety of potential insect vectors involved in its transmission throughout the year. Although no livestock cases were reported in Chiapas during the dry season, VSNJV was detected in insects during this period, suggesting that mechanisms other than transmission from livestock support VSV endemicity.

Virology↗

DOE OSTI2026

Hall effect in LaCrGe 3 at pressures up to 24 kbar and a 𝐻−𝑇 phase diagram of the pressure-induced magnetic phase

LaCrGe 3 is a metallic, itinerant ferromagnet with 𝑇 𝐶 ≈ 86K and an easy magnetic direction along its hexagonal 𝑐 axis. Here we present studies of the 𝐻 ∥ 𝑐 Hall effect in LaCrGe 3 at ambient pressure and under pressures up to 24 kbar. At ambient pressure the anomalous Hall conductivity in the ferromagnetic state reaches a large value of ∼520 Ω − 1 cm −1 . The overall data are consistent with the behavior expected for a ferromagnet with magnetic field applied along its easy axis. Measurements under pressure yield an 𝐻 = 0, 𝑃−𝑇 phase diagram consistent with earlier publications with 𝑇 𝐶 being suppressed with pressure and nonferromagnetic phases appearing for 𝑃 >15 kbar. The data suggest that the observed 𝑇 𝐶 suppression is due to the pressure-dependent exchange interactions rather than suppression of the size of the magnetic moment. Field-dependent Hall resistivity in the higher-pressure, nonferromagnetic state is consistent with the net moment being zero for 𝐻=0. In a finite field there is a sharp anomaly comparable to the one associated with a spin-flip metamagnetic transition. The 𝐻−𝑇 phase diagrams corresponding to a pressure-induced magnetic phase are constructed for several pressures. In conclusion, the Hall data suggest the possibility of the pressure-induced nonferromagnetic phase being a combination of multiple small regions of antiferromagnetic phases with different wave vectors.

Bud'ko, Sergey L. [Ames Laboratory, and Iowa State↗

DOE OSTI2026

SEARCHING FOR MESONIC DARK MATTER WITH THE HEAVY PHOTON SEARCH EXPERIMENT

Several highly-sensitive astrophysical experiments over the past couple of decades have demonstrated that the current abundance of visible Standard Model matter cannot explain galactic rotation curves, the expansion history of the Universe, or the apparent warping of light in empty space. Instead, one finds strong agreement with this body of experimental results upon positing the existence of an invisible particulate field, dark matter. Namely, a cold, weakly interacting dark matter component can explain all these phenomena. A number of accelerator-based experiments have been developed to search for the weak couplings/interactions of these particles, many of them concentrating on particle models with masses of tens to thousands of GeV. A relatively new, well-motivated model is a dark sector coupled to the Standard Model via a dark photon. The current abundance of dark matter can be obtained if one assumes that dark matter is coupled to light by a MeV to GeV particle with a U(1) symmetry. The parameter space of these models remains largely unexplored because they are difficult to probe experimentally. In this thesis, I analyze data from the Heavy Photon Search (HPS) detector, whose two detector halves closely surround the electron beam, providing acceptance to far-forward boosted interactions. This forward acceptance to highly boosted particles yields unprecedented sensitivity to MeV-scale invariant masses. I exhaustively optimize the offline reconstruction of the HPS detector. Each reconstruction object, from Silicon Vertex Tracker hits to tracks, is studied to maximize acceptance of dark matter events. I then use the 2021 run data to search for one model of dark-photon-mediated matter, the Strongly Interacting Massive Particle (SIMP). SIMP models provide self-interacting dark matter candidates that can form bound states resembling dark mesons. HPS can detect SIMPs through the decay of a dark vector boson (either a dark ¿ or ¿) into e+e- pairs. I obtain exclusion contours for SIMPs using both an optimized cuts-based selection and a machine-learning-based selection, advancing our knowledge of the nature of dark matter.

O'Dwyer, Rory [Stanford Univ., CA (United States).↗

DOE OSTI2025

Observation of a Giant Goos-Hänchen Shift for Matter Waves

The Goos-Hänchen (GH) shift describes a phenomenon in which a specularly reflected beam is translated along the reflecting surface such that the incident and reflected rays no longer intersect at the surface. Using a neutron spin-echo technique and a specially designed magnetic multilayer mirror, we have measured the relative phase between the reflected up and down neutron spin states in total reflection. The relative GH shift calculated from this phase shows a strong resonant enhancement at a particular incident neutron wave vector, which is due to a waveguiding effect in one of the magnetic layers. Calculations based on the observed phase difference between the neutron states indicate a propagation distance along the waveguide layer of 0.65 mm for the spin-down state, which we identify with the magnitude of the giant GH shift. The existence of a physical GH shift is confirmed by the observation of neutron absorption in the waveguide layer. Here, we propose ways in which our experimental method may be exploited for neutron quantum-enhanced sensing of thin magnetic layers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Exclusive photoproduction of excited ρ mesons decaying to four pions in ultraperipheral Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The intense photon ﬂuxes from relativistic nuclei provide an opportunity to study photonuclear interactions in ultraperipheral collisions. In particular, it allows for the investigations of excited, light-ﬂavour vector mesons. The measurement of coherently photoproduced 𝜋⁺𝜋 − 𝜋⁺𝜋 − ﬁnal states in ultraperipheral Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV is presented for the ﬁrst time. The cross section, d𝜎/d𝑦, times the branching ratio (𝜌 → 𝜋⁺𝜋⁺𝜋 − 𝜋 − ) is found to be 47.8 ± 2.3 (stat.) ± 7.7 (syst.) mb in the rapidity interval |𝑦| < 0.5. The invariant mass distribution is not well described with a single Breit-Wigner resonance without an interference term. Including in- terference with a non-resonant contribution results in the mass and width values being too far from those reported in PDG, while the production of two interfering resonances, 𝜌(1450) and 𝜌(1700), also provides a good description of the data. The values of the masses (𝑚) and widths (Γ) of the resonances extracted from the ﬁt assuming two interfering resonances are 𝑚₁ = 1385 ± 14 (stat.) ± 3 (syst.) MeV/𝑐², Γ₁ = 431 ± 36 (stat.) ± 82 (syst.) MeV/𝑐², 𝑚₂ = 1663 ± 13 (stat.) ± 22 (syst.) MeV/𝑐² and Γ₂ = 357 ± 31 (stat.) ± 49 (syst.) MeV/𝑐², respectively. The measured cross sections times the branching ratios are compared to recent theoretical predictions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Quantifying and visualizing the microscopic degrees of freedom of grain boundaries in the Wigner–Seitz cell of the displacement-shift-complete lattice

Here we introduce a grain boundary (GB) translation vector, t ws , to describe and quantify the domain of the microscopic degrees of freedom of GBs. It has long been recognized that for fixed macroscopic degrees of freedom of a GB there exists a large multiplicity of states characterized by different relative grain translations. More recently another degree of freedom, [n], the number of GB atoms, has emerged and is now recognized as an equally important component of GB structural multiplicity. In this work, we show that all GB microstates can be uniquely characterized by their value of t ws , which is located within the Wigner–Seitz (WS) cell of the Displacement-Shift-Complete lattice (DSCL) of the GB. The GB translation vector captures information about both the translation state and the number of GB atoms. We show that the density of GB microstates inside the WS cell of the DSCL is not uniform and can form clusters that correspond to different GB phases. Further, the vectors connecting the centers of the clusters correspond to the Burgers vectors of GB phase junctions, which can be predicted without building the junctions. Using t ws , we quantify GB excess shear and argue that it is defined up to a DSCL vector, which has implications for thermodynamic equilibrium conditions. Additionally, this work generalizes the definition of the number of GB atoms [n] to asymmetric boundaries.

36 MATERIALS SCIENCE↗

DOE OSTI2026

The X17 with Chiral Couplings

In recent years, the ATOMKI collaboration has performed a series of measurements of excited nuclei, observing a resonant excess of electron-positron pairs at large opening angles compared to the Standard Model prediction. The excess has been hypothesized to be due to the production of a new spin-1 or spin-0 particle, X17, with a mass of about 17 MeV. Recently, the PADME experiment has reported an excess in the $e^+e^-$ cross section at center-of-mass energies near 17 MeV, perhaps further hinting at the existence of a new state. Studies of the spin-1 case have hitherto focused on either vector {\em or} axial-vector couplings to quarks and leptons, whereas UV theories more naturally produce {\em both} vector and axial-vector (\textit{i.e.} chiral) couplings, analogous to the Standard Model weak interactions. We consider the ATOMKI anomalies in the context of an $X$ with chiral couplings to quarks and explore the parameter space that can explain the ATOMKI anomalies, contrasting them with experimental constraints. We find that it is possible to accommodate the reported ATOMKI signals. However, the $99\%$ CL region is in tension with null results from searches for atomic parity violation and direct searches for new low mass physics coupled to electrons. This tension is found to be driven by the magnitude of the reported excess in the transition of $^{12}{\rm C}(17.23)$, which drives the best-fit region towards excluded couplings.

Fieg, Max H. [UC, Irvine; UC, Irvine (main); Fermi↗

DOE OSTI2025

Higgs production via vector-boson fusion at the LHC

In this article, we summarise the recent experimental measurements and theoretical work on Higgs boson production via vector-boson fusion at the LHC. Along with this, we provide state-of-the-art predictions at fixed order as well as with parton-shower corrections within the Standard Model at 13.6 TeV. The results are presented in the form of multi-differential distributions as well as in the Simplified Template Cross Section bins. All materials and outputs of this study are available on public repositories. Finally, following findings in the literature, recommendations are made to estimate theoretical uncertainties related to parton-shower corrections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Geoanalytical Evaluation of Saline Storage (GEESS) Geodatabase v2.0

The Geoanalytical Economic Evaluation of Saline Storage (GEESS) geodatabase was developed to support the United States Department of Energy (DOE) and National Energy Technology Laboratory (NETL) in their geologic carbon storage efforts by characterizing saline geologic formations present in the FECM/NETL CO2 Saline Storage Cost Model (CO2_S_COM) [1]. Using publicly available literature and data, the GEESS geodatabase characterizes 57 geologic formations across the lower-48 U.S. states in what are called Fully Integrated Geodatabases (FIGs). The FIG is a vector polygon feature containing thousands or tens of thousands of individual polygons, which each contain discrete geologic parameter values. A list of the critical geologic parameters that are characterized in the GEESS geodatabase are described in the “Processing Steps and Workflow” part of the ReadMe file, as well as the Data Catalog accompanying the GEESS geodatabase. The FIG is the basis of the GEESS geodatabase and is the direct representation of the collected geologic data. In addition to the FIG, the GEESS system contains grid files. Due to the complexity of the FIGs, grids are used to sample the geologic data so they can be exercised within CO2_S_COM. The grid files contain the geologic data sampled from the FIG, as well as estimates of “Plume Uncertainty Diameter” and “First-year Break-even Price of CO2” derived from CO2_S_COM based on the GEESS grid data.

carbon↗

DOE OSTI2025

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗

DOE OSTI2024

Shorter function summaries for finite state machine-based high consequence systems using logic synthesis and tautologies (Final Report LDRD 24-1302)

Computer programs are often viewed as collections of functions – each function has parameters (inputs) and computes a return value, and each has potential side effects that modify program state (outputs). In this research, a Sandia symbolic execution tool designed to support “human-in-the-loop” analysis was modified to automatically create “function summaries,” and a new tool, “diaboolical,” was created to support enhancing readability of the summary using a novel approach to bit-vector simplification that leverages logic synthesis and tautologies. For this effort, students at Auburn University created several finite state machines (FSMs) to serve as exemplars for high-consequence systems. Function summaries for each of the machines were obtained, and then portions of the summaries were simplified using both diaboolical and the simplification procedure of a popular SMT solver. A comparison of the results shows that diaboolical can often produce smaller function summaries, with expression length improvements over the unsimplified function summaries ranging from 0% to 90% for diaboolical and 0% to 65% for the SMT solver, though diaboolical had a significantly greater cost in time. Diaboolical was evaluated against a collection of “arbitrary” C-code as well as FSM exemplars, and for both datasets it achieved an approximately 10% improvement in expression length compared to simplifications that could be obtained using existing techniques. Function summaries can assist assurance efforts that evaluate existing systems and their executable code. A smaller function summary is likely easier for humans to understand and could thus increase the ability and efficacy of assurance practices centered around the analysis of executable artifacts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Machine learning approaches for integrating multi-omics data to expand microbiome annotation (Final Technical Report)

We fulfilled all original three aims of the proposal. Following the earlier release (during the first phase of the project at Montana) of software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes, we have nearly completed a second gap-filling tool that improves accuracy and explainability. We completed software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we completed a neural embedding model for identifying similarities between protein sequences based on amino-wise latent vectors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings

We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. Furthermore, the efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH 2 NH 2 + .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Spin-Density Matrix Elements in K∗(892) Photoproduction with Recoiling Λ at GlueX

We report on the measurement of spin-density matrix elements for the K∗(892) → K+π0 photoproduction process with a recoiling Λ hyperon. The measurement used high-statistics GlueX data from photoproduction off a proton target at photon energies between 8.2 and 8.8 GeV, in a kinematic regime dominated by t-channel exchange processes. GlueX is a fixed-target experiment with a linearly polarized photon beam and a nearly 4π hermetic detector, allowing extraction of the full spin observ- ables from both the K+π0 and Λ → π−p systems. The GlueX data show clear evidence of the K∗(892) resonance in the K+π0 mass spectrum, along with additional structures at higher masses that may correspond to K∗0 or K∗2 resonances. The precise measurement of the spin-density matrix elements of the K∗(892) could serve as a standard candle for con- tinuing studies of higher-mass excited K∗ states. In addition, t-channel K∗ photoproduction differs from previous measurements of non-strange vector meson photoproduction, as it is free from Pomeron exchange and is virtually unexplored in this energy regime. Furthermore, the unprece- dented data collected by GlueX allow for investigations of correlations between the Λ polarization and the spin-density matrix elements of K∗ production, paving the way for future partial-wave analyses with full spin information involving the recoil hyperon.

Li, Hao [College of William and Mary, Williamsburg↗

DOE OSTI2025

Multi-top signals of vectorlike quarks at the LHC

We point out that events with 6 or more top quarks may be observed at the LHC if certain particles exist at the TeV scale. In a model where a vectorlike quark of charge 2/3 decays into a top quark and a pseudoscalar particle, which subsequently decays into a top-antitop pair, the LHC production cross section for events with 6 top quarks may be above 10 fb. If the pseudoscalar is part of a complex scalar field, then longer cascade decays, involving the scalar partner, may lead to events with 8 or even 10 top quarks. We show that for a region of parameter space the dominant LHC signal in this model is 8 top quarks (i.e., four $t\bar{t}$ pairs). The ensuing signals would be spectacular, including many leptons and b jets. A discovery in that case would allow several cross section measurements that may determine the masses of all three new particles.

Specific BSM Phenomenology↗