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DOE OSTI2024

Bacterial community dynamics as a result of growth-yield trade-off and multispecies metabolic interactions toward understanding the gut biofilm niche

Abstract Bacterial communities are ubiquitous, found in natural ecosystems, such as soil, and within living organisms, like the human microbiome. The dynamics of these communities in diverse environments depend on factors such as spatial features of the microbial niche, biochemical kinetics, and interactions among bacteria. Moreover, in many systems, bacterial communities are influenced by multiple physical mechanisms, such as mass transport and detachment forces. One example is gut mucosal communities, where dense, closely packed communities develop under the concurrent influence of nutrient transport from the lumen and fluid-mediated detachment of bacteria. In this study, we model a mucosal niche through a coupled agent-based and finite-volume modeling approach. This methodology enables us to model bacterial interactions affected by nutrient release from various sources while adjusting individual bacterial kinetics. We explored how the dispersion and abundance of bacteria are influenced by biochemical kinetics in different types of metabolic interactions, with a particular focus on the trade-off between growth rate and yield. Our findings demonstrate that in competitive scenarios, higher growth rates result in a larger share of the niche space. In contrast, growth yield plays a critical role in neutralism, commensalism, and mutualism interactions. When bacteria are introduced sequentially, they cause distinct spatiotemporal effects, such as deeper niche colonization in commensalism and mutualism scenarios driven by species intermixing effects, which are enhanced by high growth yields. Moreover, sub-ecosystem interactions dictate the dynamics of three-species communities, sometimes yielding unexpected outcomes. Competitive, fast-growing bacteria demonstrate robust colonization abilities, yet they face challenges in displacing established mutualistic systems. Bacteria that develop a cooperative relationship with existing species typically obtain niche residence, regardless of their growth rates, although higher growth yields significantly enhance their abundance. Our results underscore the importance of bacterial niche dynamics in shaping community properties and succession, highlighting a new approach to manipulating microbial systems.

Microbiology↗

DOE OSTI2024

Large eddy simulation of a pressurized reactive fluid–particle system: single-particle dynamics analysis

Pressurized oxy-combustion (POC) is a promising candidate to reduce carbon emissions in power generation. Designing an effective burner plays a vital role in developing new combustion technologies. Because of the high pressure, the volume fraction of particles in a pressurized oxy-combustor could be higher than that in the conventional combustor, therefore, the particle dynamics in the pressurized vessel need to be evaluated accordingly. The present computational work is to predict the influence of the particle size and particle injection location on the particle trajectory. The Stokes number based on turbulence fluctuation, St, is adopted to evaluate the impact of the gas phase on the particulate phase. A large eddy simulation (LES) with the environment pressure of 15 bar and the thermal input of the fuel particle of 100 kWth has been performed by means of the commercial CFD package. It is shown that that in this pressurized environment St << 1 for the 25 µm particles, St ~ 1 for the 100 µm particles, while for the particles exceeding 200 µm St >> 1. The trajectories of the sampling particles from the LES results support this conclusion: the particles less than 100 µm tend to be uniformly distributed in the space while the particles exceeding 200 µm tend to concentrate in the combustor.

Li, Lei↗

DOE OSTI2023

Weak phylogenetic and habitat effects on root trait variation of 218 Neotropical tree species

Tropical forests harbor a large diversity of closely related tree species that can thrive across habitats. This biodiversity has been found to correspond to large functional diversity in aboveground traits, and likely also relates to belowground trait variation. Globally, root trait (co-)variation is driven by different belowground resource strategies of species, environmental variation, and phylogeny; however, these patterns mostly reflect observations from temperate biomes and remain unconfirmed in tropical trees. We examine phylogenetic and environmental effects on root trait (co-)variation of trees across habitats in an Amazonian rainforest. Roots of 218 tree species from ten dominant families were sampled across three major habitats near Manaus, Brazil. We quantified five morphological and architectural root traits to (i) investigate how they reflected different resource strategies across species, (ii) compare them between families and superorders to test phylogenetic effects, and (iii) compare them between habitats to determine environmental effects on root trait expressions and variability. Root traits discriminated species along a tradeoff between root diameter and root branching and, secondly, due to variation in root tissue density. Our results further show weak phylogenetic effects on tropical tree root variation, for example, families from the same superorder showed large divergence in their root traits, while those from different superorders often overlapped in their root morphology and architecture. Root traits differed significantly between habitats but habitat type had only little effect on overall root trait variation. Our work suggests that the dimensions and drivers that underlie (co-)variation in tropical root traits may differ from global patterns defined by mostly temperate datasets. Due to (a)biotic environmental differences, different root trait dimensions may underlie the belowground functional diversity in (Neo)tropical forests, and we found little evidence for the strong phylogenetic conservatism observed in root traits in temperate biomes. We highlight important avenues for future research on tropical roots in order to determine the degree of, and shifts in functional diversity belowground as communities and environments change in tropical forests.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Influence of Application Method on Disinfectant Byproduct Formation during Indoor Bleach Cleaning: A Case Study on Phenol Chlorination

Modern disinfection methods increasingly utilize droplet dispersal as a means of delivering disinfectant within an indoor space. Such an application produces droplets over wide size ranges, some of which may remain airborne for minutes to hours while serving as small reaction environments. We report here the formation of chlorophenolic disinfection byproducts (DBPs) during the injection of bleach microdroplets into an environmental chamber. These reactions within airborne microdroplets are driven by phenol dissolution and availability, and the observed DBPs span multiple generations of chlorination chemistry. DBPs representing successive chlorine addition to the phenol ring are initially observed (mono-, di-, and trichlorophenol), followed by DBPs that lack aromaticity (2,6-dichlorobenzoquinone and 2,4,4,6-tetrachloro-2,5-cyclohexadienone, among others). Chlorophenolic DBPs have not been reported during prior work examining indoor bleach cleaning and we attribute their observation in this work to the dispersal of disinfectant microdroplets rather than traditional mopping or wiping with a bulk aqueous solution on an indoor surface. Airborne microdroplets represent unique reaction and volatilization environments and unique exposure pathways to DBPs, via direct compound inhalation as well as the inhalation of DBP-containing microdroplets. The observed DBPs (particularly 2,6-dichlorobenzoquinone) have previously been linked to adverse health effects in humans through either direct toxicity or as precursors to other DBPs with adverse health effects. In conclusion, these measurements suggest that more work is needed to understand potential DBP formation and human exposure within a variety of indoor environments where disinfection techniques that generate airborne microdroplets are used.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Classifying Topology in Photonic Heterostructures with Gapless Environments

Photonic topological insulators exhibit bulk-boundary correspondence, which requires that boundary-localized states appear at the interface formed between topologically distinct insulating materials. However, many topological photonic devices share a boundary with free space, which raises a subtle but critical problem as free space is gapless for photons above the light line. Here, we use a local theory of topological materials to resolve bulk-boundary correspondence in heterostructures containing gapless materials and in radiative environments. In particular, we construct the heterostructure’s spectral localizer, a composite operator based on the system’s real-space description that provides a local marker for the system’s topology and a corresponding local measure of its topological protection; both quantities are independent of the material’s bulk band gap (or lack thereof). Moreover, we show that approximating radiative outcoupling as material absorption overestimates a heterostructure’s topological protection. Importantly, as the spectral localizer is applicable to systems in any physical dimension and in any discrete symmetry class (i.e., any Altland-Zirnbauer class), our results show how to calculate topological invariants, quantify topological protection, and locate topological boundary-localized resonances in topological materials that interface with gapless media in general.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Fungal elemental profiling unleashed through rapid laser-induced breakdown spectroscopy (LIBS)

ABSTRACT Elemental profiling of fungal species as a phenotyping tool is an understudied topic and is typically performed to examine plant tissue or non-biological materials. Traditional analytical techniques such as inductively coupled plasma–optical emission spectroscopy (ICP-OES) and inductively coupled plasma–mass spectrometry (ICP-MS) have been used to identify elemental profiles of fungi; however, these techniques can be cumbersome due to the difficulty of preparing samples. Additionally, the instruments used for these techniques can be expensive to procure and operate. Laser-induced breakdown spectroscopy (LIBS) is an alternative elemental analytical technique—one that is sensitive across the periodic table, easy to use on various sample types, and is cost-effective in both procurement and operation. LIBS has not been used on axenic filamentous fungal isolates grown in substrate media. In this work, as a proof of concept, we used LIBS on two genetically distinct fungal species grown on a nutrient-rich and nutrient-poor substrate media to determine whether robust elemental profiles can be detected and whether differences between the fungal isolates can be identified. Our results demonstrate a distinct correlation between fungal species and their elemental profile, regardless of the substrate media, as the same strains shared a similar uptake of carbon, zinc, phosphorus, manganese, and magnesium, which could play a vital role in their survival and propagation. Independently, each fungal species exhibited a unique elemental profile. This work demonstrates a unique and valuable approach to rapidly phenotype fungi through optical spectroscopy, and this approach can be critical in understanding these fungi's behavior and interactions with the environment. IMPORTANCE Historically, ionomics, the elemental profiling of an organism or materials, has been used to understand the elemental composition in waste materials to identify and recycle heavy metals or rare earth elements, identify the soil composition in space exploration on the moon or Mars, or understand human disorders or disease. To our knowledge, ionomic profiling of microbes, particularly fungi, has not been investigated to answer applied and fundamental biological questions. The reason is that current ionomic analytical techniques can be laborious in sample preparation, fail to measure all potential elements accurately, are cost-prohibitive, or provide inconsistent results across replications. In our previous efforts, we explored whether laser-induced breakdown spectroscopy (LIBS) could be used in determining the elemental profiles of poplar tissue, which was successful. In this proof-of-concept endeavor, we undertook a transdisciplinary effort between applied and fundamental mycology and elemental analytical techniques to address the biological question of how LIBS can used for fungi grown axenically in a nutrient-rich and nutrient-poor environment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Stringent σ8 constraints from small-scale galaxy clustering using a hybrid MCMC + emulator framework

ABSTRACT We present a novel simulation-based hybrid emulator approach that maximally derives cosmological and Halo Occupation Distribution (HOD) information from non-linear galaxy clustering, with sufficient precision for DESI Year 1 (Y1) analysis. Our hybrid approach first samples the HOD space on a fixed cosmological simulation grid to constrain the high-likelihood region of cosmology + HOD parameter space, and then constructs the emulator within this constrained region. This approach significantly reduces the parameter volume emulated over, thus achieving much smaller emulator errors with fixed number of training points. We demonstrate that this combined with state-of-the-art simulations result in tight emulator errors comparable to expected DESI Y1 LRG sample variance. We leverage the new abacussummit simulations and apply our hybrid approach to CMASS non-linear galaxy clustering data. We infer constraints on σ8 = 0.762 ± 0.024 and fσ8(zeff = 0.52) = 0.444 ± 0.016, the tightest among contemporary galaxy clustering studies. We also demonstrate that our fσ8 constraint is robust against secondary biases and other HOD model choices, a critical first step towards showcasing the robust cosmology information accessible in non-linear scales. We speculate that the additional statistical power of DESI Y1 should tighten the growth rate constraints by at least another 50–60 ${{\ \rm per\ cent}}$, significantly elucidating any potential tension with Planck. We also address the ‘lensing is low’ tension, which we find to be in the same direction as a potential tension in fσ8. We show that the combined effect of a lower fσ8 and environment-based bias accounts for approximately $50{{\ \rm per\ cent}}$ of the discrepancy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Modeling of the effectiveness of novel edge seal designs for fast, low‐Cap‐Ex manufacturing

Abstract In the manufacturing of photovoltaic (PV) modules, the lamination process can take up to 20 min to complete. In this work, new lamination processes are being developed, and have been prototyped, which hope to be able to cut this time down to as little as 30 s. This could provide significant savings in the cost of lamination equipment, floor space, and energy. PV modules are expected to have a lifespan exceeding 20 to 30 years. For moisture‐sensitive PV technologies, the edge seal between the two layers of glass can be the weakest point of its reliability. There is an inherent challenge when evaluating edge seal materials due to their low permeation rates. As part of Colorado State University's Photovoltaic Research and Development 2, work at the National Renewable Energy Laboratory has developed models to evaluate edge seal configurations in glass‐glass PV modules. Here, this new manufacturing process is evaluated for long‐term moisture durability. Different edge seal design options within glass–glass PV modules are explored. Most of these designs are targeting a superstrate on glass configuration, e.g CdTe, but some designs could be used on conventional crystalline Si cells. Using COMSOL finite element simulation software, we investigated the edge seal and interlayer design configurations containing silicone perimeter edge adhesive, desiccated polyisobutylene‐based edge seal, air, and polyolefin while integrating climate conditions equivalent to a hot and humid environment such as Miami, Florida. We found optimized configurations that will allow the module to prevent moisture ingress over 50 years minimizing the amount of time and material used while utilizing polymers that are easily dispensed.

14 SOLAR ENERGY↗

DOE OSTI2022

graphenv: a Python library for reinforcement learning on graph search spaces

Many important and challenging problems in combinatorial optimization (CO) can be expressed as graph search problems, in which graph vertices represent full or partial solutions and edges represent decisions that connect them. Graph structure not only introduces strong relational inductive biases for learning (Battaglia et al., 2018) - in this context, by providing a way to explicitly model the value of transitioning (along edges) between one search state (vertex) and the next - but lends itself to problems both with and without clearly defined algebraic structure. For example, classic CO problems on graphs such as the Traveling Salesman Problem (TSP) can be expressed as either pure graph search or integer programs. Other problems, however, such as molecular optimization, do no have concise algebraic formulations and yet are readily implemented as a graph search (V. et al., 2022; Zhou et al., 2019). Such "model-free" problems constitute a large fraction of modern reinforcement learning (RL) research owing to the fact that it is often much easier to write a forward simulation that expresses all of the state transitions and rewards, than to write down the precise mathematical expression of the full optimization problem. In the case of molecular optimization, for example, one can use domain knowledge alongside existing software libraries to model the effect of adding a single bond or atom to an existing but incomplete molecule, and let the RL algorithm build a model of how good a given decision is by "experiencing" the simulated environment many times through. In contrast, a model-based mathematical formulation that fully expresses all the chemical and physical constraints is intractable. In recent years, RL has emerged as an effective paradigm for optimizing searches over graphs and led to state-of-the-art heuristics for games like Go and chess, as well as for classical CO problems such as the TSP. This combination of graph search and RL, while powerful, requires non-trivial software to execute, especially when combining advanced state representations such as Graph Neural Networks (GNN) with scalable RL algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Characterization of the Finite Element Computational Fluid Dynamics Capabilities in the Multiphysics Object Oriented Simulation Environment

We report the multiphysics object-oriented simulation environment (moose) is a code package that couples a variety of physics modules, allowing for highly accessible multiphysics simulations. The physics modules include a finite element Navier–Stokes (N–S) module that is designed to solve laminar fluid dynamics problems. The usage of this module in multiple recent studies coupled with the growing interest in moose for usage in nonlight water reactor safety studies by the Nuclear Regulatory Commission (NRC) prompted the authors to investigate the computational fluid dynamics capabilities of moose. A two-dimensional laminar flow past a circular cylinder scenario is simulated in the moose framework to investigate the effectiveness of the N–S module. Simulations assumed an unsteady laminar flow with a Reynolds number of 200. To verify the results from moose, similar simulations were conducted using the well-utilized simulation of turbulent flow in arbitrary regions—computational continuum mechanics C++ (star-ccm + ) finite volume code. Results from both codes are also compared to some results from literature. Velocity and pressure profiles of both transient simulations were compared. The numerical and input errors in moose are also visualized with contour plots to qualitatively understand the evolution of the errors across time and space. The comparisons between moose and star-ccm + showed nearly perfect agreement between the codes for velocity and pressure, especially after the development of the vortex street in later time-steps. The force coefficients showed excellent agreement after the development of the vortex street, but demonstrated notable discrepancies prior to the vortex street development, which is likely due to how each code simulated the approach to the vortex street in earlier time-steps.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Whole plant traits of coastal dune vegetation and implications for interactions with dune dynamics

Abstract Coastal dunes are important protective features against sea level rise and coastal storms. Interactions between dune plant aboveground structures and sediment trapping that allow for dune building and maintenance are well established. More recently, studies documenting belowground biomass for promoting erosion resistance in dominant dune species have been conducted, yet a knowledge gap remains regarding species‐specific characterization of whole plants, specifically with respect to roots, rhizomes, and belowground stems. Our objective was to quantify above‐ and belowground traits of four dominant dune grasses to document the potential for species‐specific effects on dune growth, maintenance, and erosion resistance. We examined above‐ and belowground traits among four prominent dune grasses of the Atlantic and Gulf Coasts of North America: Ammophila breviligulata , Panicum amarum , Spartina patens , and Uniola paniculata . Whole plant samples of each species were collected from the foredune at the US Army Engineer Research and Development Center's Field Research Facility in Duck, North Carolina, USA, and quantified for several above‐ and belowground traits (e.g., stem height, rhizome number and length, root surface area by diameter class, root tensile strength, and mycorrhizal percent infection). Belowground factors known to impact important dune processes, such as rhizome length, mycorrhizal percent infection, and root traits, differed substantially among species. When visualized in multivariate space, all species significantly differed in suites of above‐ and belowground traits. When considering belowground only, Ammophila and Spartina were similar, despite differences in biomass allocation. Species separated along axes related to mycorrhizal association, biomass allocation, and root construction. The four co‐occurring dune grass species were dissimilar in suites of plant traits. Belowground trait differences were driven by those describing root construction, biomass allocation, and mycorrhizal infection. Dissimilarity in above‐ and belowground suites of traits may demonstrate different approaches for surviving the dune environment. Incorporating belowground traits into modeling will enhance predictions of dune response to climate change through interactions between vegetation and dune dynamics that facilitate coastal resistance and resilience.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Dual carbonate clumped isotopes (Δ 47 -Δ 48 ) constrains kinetic effects and timescales in peridotite-associated springs at the Cedars, Northern California

The Cedars is an area in Northern California with a chain of highly alkaline springs resulting from CO 2 -charged meteorological water interacting with a peridotite body. Serpentinization resulting from this interaction at depth leads to the sequestration of various carbonate minerals into veins accompanied by a release of Ca 2+ and OH – enriched water to the surface, creating an environment which promotes rapid precipitation of CaCO 3 at surface springs. This environment enables us to apply the recently developed Δ 47 -Δ 48 dual clumped isotope analysis to probe kinetic isotope effects (KIEs) and timescales of CO 2 transformation in a region with the potential for geological CO 2 sequestration. Here, we analyzed CaCO 3 recovered from various localities and identified significant kinetic fractionations associated with CO 2 absorption in a majority of samples, characterized by enrichment in Δ 47 values and depletion in Δ 48 values relative to equilibrium. Surface floes exhibited the largest KIEs (ΔΔ 47 : 0.163‰, ΔΔ 48 : -0.761‰). Surface floe samples begin to precipitate out of solution within the first hour of CO 2 absorption, and the dissolved inorganic carbon (DIC) pool requires a residence time of >100 h to achieve isotopic equilibria. The Δ 48 /Δ 47 slope of samples from the Cedars (-3.223 ± 0.519) is within the range of published theoretical values designed to constrain CO 2 hydrolysis-related kinetic fractionation (-1.724 to -8.330). The Δ 47 /δ 18 O slope (-0.009 ± 0.001) and Δ 47 /δ 13 C slope (-0.009 ± 0.001) are roughly consistent with literature values reported from a peridotite in Oman of -0.006 ± 0.002 and -0.005 ± 0.002, respectively. The consistency of slopes in the multi-isotope space suggests the Δ 47 -Δ 48 dual carbonate clumped isotope framework can be applied to study CO 2 -absorption processes in applied systems, including sites of interest for geological sequestration.

58 GEOSCIENCES↗

DOE OSTI2024

Impact of mid- Z gas fill on dynamics and performance of shock-driven implosions at the OMEGA laser

Shock-driven implosions with 100% deuterium (D 2 ) gas fill compared to implosions with 50:50 nitrogen-deuterium (N 2 ⁢D 2 ) gas fill have been performed at the OMEGA laser facility to test the impact of the added mid-Ζ fill gas on implosion performance. Ion temperature (Τ ion ) as inferred from the width of measured DD-neutron spectra is seen to be 34%±6% higher for the N 2⁢ D 2 implosions than for the D 2 -only case, while the DD-neutron yield from the D 2 -only implosion is 7.2±0.5 times higher than from the N 2⁢ D 2 gas fill. The T ion enhancement for N 2 ⁢D 2 is observed in spite of the higher Z, which might be expected to lead to higher radiative loss, and higher shock strength for the D 2 -only versus N 2 ⁢D 2 implosions due to lower mass, and is understood in terms of increased shock heating of N compared to D, heat transfer from N to D prior to burn, and limited amount of ion-electron-equilibration-mediated additional radiative loss due to the added higher-Z material. Further, this picture is supported by interspecies equilibration timescales for these implosions, constrained by experimental observables. The one-dimensional (1D) kinetic Vlasov-Fokker-Planck code ifp and the radiation hydrodynamic simulation codes hyades (1D) and xrage [1D, two-dimensional (2D)] are brought to bear to understand the observed yield ratio. Comparing measurements and simulations, the yield loss in the N 2 ⁢D 2 implosions relative to the pure D 2 -fill implosion is determined to result from the reduced amount of D 2 in the fill (fourfold effect on yield) combined with a lower fraction of the D 2 fuel being hot enough to burn in the N 2 ⁢D 2 case. The experimental yield and T ion ratio observations are relatively well matched by the kinetic simulations, which suggest interspecies diffusion is responsible for the lower fraction of hot D 2 in the N 2 ⁢D 2 relative to the D 2 -only case. The simulated absolute yields are higher than measured; a comparison of 1D versus 2D XRAGE simulations suggest that this can be explained by dimensional effects. The hydrodynamic simulations suggest that radiative losses primarily impact the implosion edges, with ion-electron equilibration times being too long in the implosion cores. The observations of increased T ion and limited additional yield loss (on top of the fourfold expected from the difference in D content) for the N 2 ⁢D 2 versus D 2 -only fill suggest it is feasible to develop the platform for studying CNO-cycle-relevant nuclear reactions in a plasma environment.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Leveraging intermediate resonances to probe CP violation at colliders

We explore the phenomenological impact of interference in tree-level contributions to three-body final states in $2\rightarrow 3$ scattering processes. This work introduces a novel search strategy leveraging asymmetries to enable sensitivity to CP-violating effects in less well-explored regions of phase space. Analytically, we demonstrate the effectiveness of this observable in probing interference between Standard Model charged-current decays and effective left-handed vector interactions, illustrated in a toy model featuring a scalar leptoquark, $S_1 \sim (3, 1, -\,1/3)$. Numerically, we apply this framework to studying the process $pp\rightarrow b \tau \nu $; unlike traditional high-$p_T$ searches or “bump hunts”, this approach utilizes an intermediate energy regime – where new physics is neither light enough to be produced on shell or heavy enough to justify an effective field theory treatment. A proof-of-principle analysis at parton level demonstrates a percent-level asymmetry, with sensitivity also to BSM weak-CP phase. While the specific phase sensitivity is diminished at particle level due to showering and detector effects, a machine learning classifier can recover sensitively to the presence of SM-BSM interference, significantly outperforming standard analysis methods. Notably discrimination between BSM signal and SM background could be achieved at the 2$\sigma $ level for the current LHC dataset and 8$\sigma $ at the High-Luminosity LHC. Moreover, this asymmetry observable as defined can also be more broadly applied to other searches for CP-violation in $2\rightarrow 3$ processes in present and future collider environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Agentic AI vs ML-Based Autotuning: A Comparative Study for Loop Reordering Optimization

High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.

Rosas, Miguel Romero↗

DOE OSTI2020

Development of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an Integrated Energy System (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. A new RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization (HERON) has been developed for optimizing the production variables of the IES given the demand profile. Currently, only the limits that affect the production variables and their corresponding time rates of change are considered (explicit constraints). However, other variables are additionally subject to constraints, but the associated limits are not accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response effects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. The Reference Governor (RG) algorithm is traditionally used for enforcing state and control constraints by modifying the set-point trajectories supplied to the feedback regulators. In our application, the RG is coupled within an iterative loop with the HERON-power dispatcher to generate optimal trajectories that ensure the operational constraints are met. A data-driven procedure to derive a representation of the dynamics of the controlled system was developed. First, the variables that represented the state of the system are selected (PCA-based approach), and then state-space representation matrices are derived from the collected measurements (DMDc algorithm). A preliminary version of the developed workflow based on Linear Time Invariant matrices was assessed by adopting a two-unit test case. More sophisticated versions of this workflow foreseeing the on-line derivation of system matrices will be deployed in FY 2021. Finally, a “plug-and-play” library of controllers and state observers was developed in Dymola. Some aspects of the current configuration of the IES unit components, e.g., the encapsulation of the control schemes into dedicated blocks, are consistent with the “plug-and-play” philosophy. Other features, e.g., the system buses collecting the input and the output variables, are not. For this reason, once listed and described the limits of the current configuration, necessary modifications to the plant model interface are presented. As a test-case, the interfaces of the SES model in the RAVEN-based HYBRID framework were reworked accordingly, and two different control schemes were applied to the same plant model.

24 POWER TRANSMISSION AND DISTRIBUTION↗