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DOE OSTI2025

Reduced-basis method for few-body bound-state emulation

Recent advances in both theoretical and computational methods have enabled large-scale, precision calculations of the properties of atomic nuclei. With the growing complexity of modern nuclear theory, however, also comes the need for novel methods to perform systematic studies and quantify the uncertainties of models when confronted with experimental data. Here, this study presents an application of such an approach, the reduced basis method, to substantially lower computational costs by constructing a significantly smaller Hamiltonian subspace informed by previous solutions. Our method shows comparable efficiency and accuracy to other dimensionality reduction techniques on an artificial three-body bound system while providing a richer representation of physical information in its projection and training subspace. This methodological advancement can be applied in other contexts and has the potential to greatly improve our ability to systematically explore theoretical models and thus enhance our understanding of the fundamental properties of nuclear systems.

cluster models↗

DOE OSTI2024

Heuristic solutions to the single depot electric vehicle scheduling problem with next day operability constraints

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. Further, a case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Using the Metropolis algorithm to explore the loss surface of a recurrent neural network

In the limit of small trial moves the Metropolis Monte Carlo algorithm is equivalent to gradient descent on the energy function in the presence of Gaussian white noise. This observation was originally used to demonstrate a correspondence between Metropolis Monte Carlo moves of model molecules and overdamped Langevin dynamics, but it also applies in the context of training a neural network: making small random changes to the weights of a neural network, accepted with the Metropolis probability, with the loss function playing the role of energy, has the same effect as training by explicit gradient descent in the presence of Gaussian white noise. We explore this correspondence in the context of a simple recurrent neural network. We also explore regimes in which this correspondence breaks down, where the gradient of the loss function becomes very large or small. In these regimes the Metropolis algorithm can still effect training, and so can be used as a probe of the loss function of a neural network in regimes in which gradient descent struggles. We also show that training can be accelerated by making purposely-designed Monte Carlo trial moves of neural-network weights.

Casert, Corneel↗

DOE OSTI2025

Mapping the cosmic gamma-ray horizon: the 1CGH catalogue of Fermi -LAT detections above 10 GeV

ABSTRACT We present the First Cosmic Gamma-ray Horizon (1CGH) catalogue, featuring $\gamma$-ray detections above 10 GeV based on 16 yr of observations with the Fermi Large Area Telescope (Fermi-LAT) satellite. After carefully selecting a sample of blazars and blazar candidates from catalogues in the literature, we performed a binned likelihood analysis and identified 2791 $\gamma$-ray emitters above 10 GeV, at >3$\sigma$ level, including 62 that are new $\gamma$-ray detections. For each source, we estimated the mean energy of the highest energy bin and analysed them in the context of the cosmic gamma-ray horizon. By adopting a reference model for the extragalactic background light (EBL), we identified a subsample of 525 sources where moderate to severe $\gamma$-ray absorption could be detected across the redshift range of 0–3. This work provides the most up-to-date compilation of detections above 10 GeV, along with their redshift information. We condense extensive results from the literature, including reports on observational campaigns dedicated to blazars and $\gamma$-ray sources, thereby delivering an unprecedented review of the redshift information for sources detected above 10 GeV. Additionally, we highlight key 1CGH sources where redshift information remains incomplete, offering guidance for future optical observation campaigns. The 1CGH catalogue aims to track the most significant sources for studying the $\gamma$-ray transparency of the Universe. Furthermore, it provides a targeted subsample where the EBL optical depth, $\tau _{(E,z)}$, can be robustly measured using Fermi-LAT data.

Arsioli, Bruno (ORCID:0000000181666602)↗

DOE OSTI2025

Search for charged-lepton flavor violation in the production and decay of top quarks using trilepton final states in proton-proton collisions at $\sqrt{s}$ =13 TeV

A search is performed for charged-lepton flavor violating processes in top quark (𝑡) production and decay. The data were collected by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 138 fb −1 . The selected events are required to contain one opposite-sign electron-muon pair, a third charged lepton (electron or muon), and at least one jet of which no more than one is associated with a bottom quark. Boosted decision trees are used to distinguish signal from background, exploiting differences in the kinematics of the final states particles. The data are consistent with the standard model expectation. Upper limits at 95% confidence level are placed in the context of effective field theory on the Wilson coefficients, which range between 0.024–0.424 TeV −2 depending on the flavor of the associated light quark and the Lorentz structure of the interaction. These limits are converted to upper limits on branching fractions involving up (charm) quarks, 𝑡 → 𝑒⁢𝜇⁢𝑢 (𝑡 → 𝑒⁢𝜇⁢𝑐), of 0.032⁢(0.498) × 10 −6 , 0.022⁢(0.369) × 10 −6 , and 0.012⁢(0.216) × 10 −6 for tensorlike, vectorlike, and scalarlike interactions, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Perform Design Support with MCNP for New Measurements

This report incorporates our work carried out during our 5-month internship at LANL under an internship agreement with EAMEA (École des Applications Militaires de l’Énergie Atomique). After outlining the context in which we worked, we present our work as aid to modeling and predicting the neutronic behavior of nuclear systems, with a view to carrying out criticality experiments qualifying the MCNP code as part of innovative projects. Fourth generation reactors will enable to tackle a lot of issues such as environmental crisis, affordable energy access, and nuclear waste management. They seem to be one of the keys for a sustainable future. Most of the projects that emerge nowadays include the use of HALEU (high assay low enriched uranium) or MOX recycled fuels. Our projects are part of this dynamic and addresses concrete scientific research issues in the nuclear field. Studies of HALEU package are essential to anticipate the need, therefore the Optimus L (OPTImal Modular Universal Shipping cask technology) designed by NAC (Nuclear Assurance Corporation) international but filled with 20 % enriched uranium dioxide (UO 2 ) will be studied to support safe transportation. However, it seems there is no benchmark with a high correlation with the combination of this fuel and this package to validate MCNP simulations. As such, the study will focus on the development of new criticality safety benchmarks for this case. On the other hand, there is a great need for critical benchmarks in the intermediate energy range with MOX fuel. An IER (Integral Experiment Request) has then be requested to answer it through a partnership between French institution IRSN and U.S. Department of Energy's Nuclear Criticality Safety Program (NCSP). This experience planned for 2025 requires to gather calculated data through a MCNP model to be realized safely. Finally, a presentation of our one-week experience at the DAF as part of our discovery of criticality experiments will be introduced in Appendix 1: Week at the DAF (Device Assembly Facility)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Numerical Treatment of Shock-induced Nuclear Burning in Double Detonation Type Ia Supernovae

We present a benchmark problem to assess the treatment of shock-induced nuclear burning in the context of double detonation Type Ia supernovae. In a stratified white dwarf model, we implement a shock-detection criterion that suppresses burning in zones characterized by compression and significant pressure gradients, controlled by a tunable parameter, f shock . One-dimensional simulations, using the open-source Castro suite, were conducted across three treatments—burning fully enabled, and burning suppressed with f shock = 2/3 and f shock = 1—across three spatial resolutions (5.0, 2.5, and 0.3125 km). At the finest resolution, the burning-enabled and f shock = 1 models converge, while the f shock = 2/3 front continues to show slight offset behavior. Since most simulations are carried out at much lower resolutions, our tests support the idea that burning in shocks should always be disabled in practice. We also observe that the behavior of lower-resolution simulations remains extremely sensitive to the choice of f shock .

GPU computing↗

DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗

DOE OSTI2026

A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images

Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as “black boxes”, lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method across multiple deep learning–based WSI analysis tasks spanning different tissue types. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the model’s predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.

Lou, Peiliang↗

DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗

DOE OSTI2025

Mapping Hsp104 interactions using cross‐linking mass spectrometry

Molecular machines from the AAA+ (ATPases Associated with diverse cellular Activity) superfamily of protein disaggregases play important roles in protein folding, disaggregation and DNA processing. Recent cryo-EM structures of AAA+ molecular machines have uncovered nuanced changes in their conformation that underlie their specialized functions. Structural knowledge of these molecular machines in complex with substrates begins to explain their mechanism of activity. Here, we explore how cross-linking mass spectrometry (XL-MS) can be used to interpret changes in conformation induced by ATP in Hsp104 and how a substrate may interact with Hsp104. We applied a panel of cross-linking reagents to produce cross-linking maps of Hsp104 and interpret our data on previously determined X-ray and cryo-EM structures of Hsp104 from a thermophilic yeast, Calcarisporiella thermophila. We developed an analysis pipeline to differentiate between intra-subunit and inter-subunit contacts within the hexameric homo-oligomer. We identify cross-links that break the asymmetry that is present in Hsp104 in an ATP-hydrolysis competent conformation but is absent in an ATP-hydrolysis-defective mutant. Finally, we identify contacts between Hsp104 and a selected protein (proprotein convertase subtilisin/kexin type 9 PCSK9) to reveal contacts on the central channel of Hsp104 across the length of this protein indicating that we might have trapped interactions consistent with its translocation. Our simple and robust XL-MS-based experiments and methods help interpret how these molecular machines change conformation and bind to other proteins even in the context of homo-oligomeric assemblies enabling coupling state-of-the-art modeling approaches with XL-MS.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Resilient water infrastructure partnerships in institutionally complex systems face challenging supply and financial risk tradeoffs

Abstract As regions around the world invest billions in new infrastructure to overcome increasing water scarcity, better guidance is needed to facilitate cooperative planning and investment in institutionally complex and interconnected water supply systems. This work combines detailed water resource system ensemble modeling with multiobjective intelligent search to explore infrastructure investment partnership design in the context of ongoing canal rehabilitation and groundwater banking in California. Here we demonstrate that severe tradeoffs can emerge between conflicting goals related to water supply deliveries, partnership size, and the underlying financial risks associated with cooperative infrastructure investments. We show how hydroclimatic variability and institutional complexity can create significant uncertainty in realized water supply benefits and heterogeneity in partners’ financial risks that threaten infrastructure investment partnership viability. We demonstrate how multiobjective intelligent search can design partnerships with substantially higher water supply benefits and a fraction of the financial risk compared to status quo planning processes. This work has important implications globally for efforts to use cooperative infrastructure investments to enhance the climate resilience and financial stability of water supply systems.

Science & Technology - Other Topics↗

DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗

DOE OSTI2024

XFEL SASE pulses can enhance time-dependent observables

Abstract X-ray free electron lasers (XFELs) have emerged as powerful sources of short and intense x-ray pulses. We propose a simple and robust procedure which takes advantage of the inherent stochasticity of self-amplified stimulated emission (SASE) pulses to enhance the time-resolution and signal strength of the recorded data. Notably, the proposed method is able to enhance the average signal without knowledge of the signal strength of individual shots. Simple metrics for the probe pulses are introduced, such as an effective pulse duration applicable to SASE pulses characterised in the time domain using e.g. an X-band transverse cavity. The approach is evaluated using simulated and real pulse data in the context of ultrafast electron dynamics in a molecule. Utilising H 2 as a model system, we demonstrate the efficacy of the method theoretically, successfully enhancing the predicted nonresonant ultrafast x-ray scattering signal associated with electron dynamics. The method presented is broadly applicable and offers a general strategy for enhancing time-dependent observables at XFELs.

data analysis↗

DOE OSTI2025

Computing virtual dark-field X-ray microscopy images of complex discrete dislocation structures from large-scale molecular dynamics simulations

Dark-field X-ray microscopy (DFXM) is a novel diffraction-based imaging technique that non-destructively maps the local deformation from crystalline defects in bulk materials. While studies have demonstrated that DFXM can spatially map 3D defect geometries, it is still challenging to interpret DFXM images of the high-dislocation-density systems relevant to macroscopic crystal plasticity. This work develops a scalable forward model to calculate virtual DFXM images for complex discrete dislocation structure(s) (DDS) obtained from atomistic simulations. Our new DDS-DFXM model integrates a non-singular formulation for calculating the local strain from the DDS and an efficient geometrical optics algorithm for computing the DFXM image from the strain field. We apply the model to complex DDS obtained from a large-scale molecular dynamics simulation of compressive loading on single-crystal silicon. Simulated DFXM images exhibit prominent contrast for dislocation features between the multiple slip systems, demonstrating the potential of DFXM to resolve features from dislocation multiplication. In conclusion, the integrated DDS-DFXM model provides a toolbox for DFXM experimental design and image interpretation in the context of bulk crystal plasticity for a range of measurements across shock plasticity and the broader materials science community.

X-ray imaging↗

DOE OSTI2025

Reconstructing f ( T ) gravity and exploring the torsion driven warm inflationary cosmology

The current paper reports an investigation of a warm inflationary scenario in the context of f(T) gravity for a spatially flat FLRW universe. In our model, inflation is driven purely by the torsional sector of f(T) gravity, without introducing any additional scalar fields. We focus on the high dissipative regime (R >> 1), reconstruct the Hubble parameter as a function of the e-folding number N, and derive the slow-roll parameters ε 1 (N) and ε 2 (N). The study has encapsulated the dynamics of inflation and its duration under strong dissipation. The dissipative coefficient Γ is modeled with a temperature-dependent power-law form, linking the inflationary dynamics to thermal corrections and the particle content of the early universe. The analysis has affirmed that the torsion-induced energy density ρ T successfully transitions to radiation energy density ρ rad , facilitating a graceful exit from inflation. Finally, we have validated our model by comparing the scalar spectral index and tensor-to-scalar ratio with Planck 2018 results, demonstrating consistency within observational bounds. Additionally, it is verified that the thermal domination condition T * /H > 1 and the torsion dominance condition ρ T /ρ rad > 1 are satisfied.

Ghosh, Moli [Amity University, Kolkata (India); Mr↗

DOE OSTI2024

Live cell imaging of cellular dynamics in poplar wood using computational cannula microscopy

This study presents significant advancements in computational cannula microscopy for live imaging of cellular dynamics in poplar wood tissues. Leveraging machine-learning models such as pix2pix for image reconstruction, we achieved high-resolution imaging with a field of view of 55µm using a 50µm-core diameter probe. Our method allows for real-time image reconstruction at 0.29 s per frame with a mean absolute error of 0.07. We successfully captured cellular-level dynamics in vivo , demonstrating morphological changes at resolutions as small as 3µm. We implemented two types of probabilistic neural network models to quantify confidence levels in the reconstructed images. This approach facilitates context-aware, human-in-the-loop analysis, which is crucial for in vivo imaging where ground-truth data is unavailable. Using this approach we demonstrated deep in vivo computational imaging of living plant tissue with high confidence (disagreement score ⪅0.2). This work addresses the challenges of imaging live plant tissues, offering a practical and minimally invasive tool for plant biologists.

Ingold, Alexander (ORCID:0009000752380016)↗

DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

42 ENGINEERING↗