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At least 847 records · Page 47

Nuclear Data Uncertainty Quantification: Past, Present and Future

An historical overview is provided of the mathematical foundations of uncertainty quantification and the roles played in the more recent past by nuclear data uncertainties in nuclear data evaluations and nuclear applications. Significant advances that have established the mathematical framework for contemporary nuclear data evaluation methods, as well as the use of uncertainty information in nuclear data evaluation and nuclear applications, are described. This is followed by a brief examination of the current status concerning nuclear data evaluation methodology, covariance data generation, and the application of evaluated nuclear data uncertainties in contemporary nuclear technology. A few possible areas for future investigation of this subject are also suggested.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Generalist multimodal AI: A review of architectures, challenges and opportunities

Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.

Artificial intelligence (AI)↗

A model to assess Zircaloy’s mechanical property changes following a transient beyond critical heat flux

Maintaining the integrity of nuclear fuel rods is essential for ensuring public health and safety in nuclear power generation. During reactor operation, this integrity is confirmed by demonstrating compliance with established regulatory acceptance criteria. For moderate-frequency events, such as limiting transients and anticipated operational occurrences (AOOs), the current fuel integrity criterion is based on preventing boiling transition. This criterion assumes that prevention of boiling transition will prevent excessive cladding heating and, thus, fuel failure during normal operations. While conservative, this approach places significant constraints on core design, fuel cycle economics, and a plant’s ability to perform major power uprates, leading to suboptimal fuel utilization and inefficient carbon-free energy production. A more efficient approach could be achieved by revising the failure criterion to a material-specific limit rather than strictly preventing the boiling transition, since boiling transition per se is not a cause of fuel cladding failure. Here, as a result, a new licensing framework based on material properties, termed time-at-temperature (t@T), is needed. This approach would allow for brief periods of post–critical heat flux operation during an AOO without compromising safety. Implementing the t@T licensing strategy requires a robust technical foundation in material properties, which must be established through comprehensive data collection on both unirradiated and irradiated fuel and cladding materials. This foundation would enable the development of a safety basis that ensures safe operation while providing greater flexibility and efficiency for reactor operation. This paper documents a thorough review of the available data to establish a baseline knowledge that can inform the development of cladding mechanical models, as well as identify experimental data gaps that need to be addressed in future research. Machine learning and data informatics were utilized to extract the importance of parameters on the t@T parameter. Industry tools were used to perform baseline analyses to define the relevant transient conditions for data analysis. The subsequent review successfully identified applicable experimental data, as well as sufficient data to evaluate changes in cladding mechanical properties following an AOO transient. Rather than developing new models, this work coupled existing irradiation annealing and recrystallization models to calculate changes in hardness, yield stress, and ultimate tensile stress following an AOO event. The findings from this review were summarized to highlight the experimental data needs required to fill remaining gaps and support the development of future t@T licensing methodologies.

Cladding performance↗

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗

Modal dynamics and flutter analysis of floating offshore vertical axis wind turbines

Floating platforms provide vertical axis wind turbines (VAWTs) significant advantages over offshore horizontal axis wind turbines, which has led to increased research interest in offshore VAWT technology. Here, this paper examines dynamic stability of floating VAWTs and presents structural dynamic and aero-elastic flutter analysis of utility-scale 5 MW floating VAWTs by coupling a rotor finite element model with a linearized model for the floating system. The coupled floating VAWT model is utilized to study the effects of tower height, number of blades, blade tapering scheme, and impacts of the floating platform on modal properties and flutter. The study provides important new results and guidance to floating VAWT designers: (1) tower height increase leads to lower tower frequencies more prone to resonance, (2) by moving from land-based foundations to a floating TLP foundation, tower-mode frequency is increased significantly (by 49%–68%) which mitigates resonance and increases the flutter RPM (154% increase), (3) increasing the number of blades reduces the tower mode frequencies for both land-based and floating configuration, and (4) blade chord tapering can have a significant impact on increasing the tower mode flutter RPM. Further, analysis of low-frequency rigid body modes shows a safe, higher flutter RPM relative to the operating RPM.

17 WIND ENERGY↗

Carbon acquisition ecological strategies to connect soil microbial biodiversity and carbon cycling

Soil carbon feedbacks to global change are uncertain, and the biological processes that govern soil organic matter decomposition are not resolved in current ecosystem models. Though it is recognized that microbial biodiversity influences decomposition rates, incorporating this relationship into ecosystem models is challenging because microbial communities are prohibitively diverse. It is likely necessary to distill microbial biodiversity by focusing on functional groups or ecological strategies. The ecological strategies that currently dominate the microbial ecology literature derive from macroecological theory, have clear weaknesses, and have had limited success when applied to predict soil carbon dynamics. Here, we present a new framework for soil microorganisms: Carbon Acquisition Ecological Strategies (CAES), and we outline a path toward incorporating microbial biodiversity into ecosystem models using this framework to enhance predictions of soil carbon feedbacks to global change. Because a microorganism's diet is central to its ecological niche and likely to covary with other ecologically significant traits, we posit that carbon acquisition may serve as a tractable foundation for developing ecological strategies. Further, we describe four candidate ecological strategies for soil microorganisms: 1° decomposers that assimilate complex plant polymers, 2° decomposers that assimilate microbial necromass, passive consumers that assimilate dissolved organic carbon, and predatory microbes that assimilate live microbial biomass. These strategies are directly linked to soil carbon pools currently represented in ecosystem models and may provide a foundation for greater integration of microbial community dynamics into ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗

ElectroCat: DOE's approach to PGM-free catalyst and electrode R&D

The successful development of high-performance, durable platinum group metal-free (PGM-free) electrocatalysts and electrodes for polymer electrolyte membrane fuel cells (PEMFCs) will ultimately improve the cost-competiveness of fuel cells in a wide range of applications. This is considered to be a critical development especially for automotive fuel cell applications in order to bring the system cost of an automotive fuel cell system down to the $30/kW cost target set by the U.S. Department of Energy (DOE). The platinum group metal (PGM) electrocatalysts are a major contributor to the system cost. Addressing the technical challenges to PGM-free electrocatalyst and electrode development, therefore, represents one of DOE's most pressing research and development (R&D) priorities. ElectroCat was formed by the DOE as part of the Energy Materials Network (EMN) in early 2016, and shares with other EMN consortia the goal of decreasing the time to market for advanced materials related to clean energy technologies, in the context of increasing U.S. fuel cell electric vehicle (FCEV) manufacturing competitiveness. To accomplish this, the consortium performs core research and development and provides universities and companies streamlined access to the unique, world-class set of tools and expertise relevant to early-stage applied PGM-free catalyst R&D of the member national laboratories. Moreover, ElectroCat fosters a systematic methodology by which prospective catalysts and electrodes are prepared and analyzed rapidly and comprehensively using high-throughput, combinatorial methods. Catalyst discovery is augmented by theory as well as foundational electrocatalysis and materials knowledge at the participating national laboratories. Furthermore, ElectroCat has developed a data sharing framework, requisite of all EMN consortia, for disseminating its findings to the public via a searchable database, to further expedite incorporation of PGM-free electrocatalysts into next-generation fuel cells by advancing the general understanding of the PGM-free electrocatalyst field.

Thompson, Simon T.↗

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms’ metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. Furthermore, this review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

59 BASIC BIOLOGICAL SCIENCES↗

A Level Set Evaporation Model for Heterogeneous Atom Probe Tip

With the development of local electrode atom probe tomography (LEAP), the materials systems feasible for atomic structure determination has expanded from predominantly metals to include heterogeneous materials with complex structure and compositions. This makes the 3D atomic structure determination possible for most energy materials in battery, fuel cell, semiconductor, and nuclear applications. Atom probe tomography (APT) has been utilized to study multiphase materials with segregation and interface problems. The unique and quantitative information retrieved from APT can provide a foundation for the study of kinetic pathways, nanoscale composition mapping, multilayer interfacial segregation, dislocations and grain boundaries. However, a new challenge surfaces when the materials systems expand into heterogeneous materials: the traditional structure reconstruction introduces false magnification and large artifacts due to the underlying assumption of uniform and isotropic evaporation.

Atom probe, heterogeneous structure, evporation mo↗

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗

Spectroscopic Characterization of $μ$-$η$ 1 :$η$ 1 -Peroxo Ligands Formed by Reaction of Dioxygen with Electron-Rich Iridium Clusters

Although oxygen is a common ligand in supported metal catalysts, its coordination has been challenging to elucidate. Herein we characterize a diiridium complex that has been previously shown by X-ray diffraction crystallography to incorporate a μ-η 1 :η 1 -peroxo ligand. We observe markedly enhanced intensity at 788 cm -1 in the Raman spectrum of this complex, which is a consequence of bonding of the peroxo ligand but does not shift upon 18 O labeling. Electronic structure calculations at the density functional theory level suggest that this increase in Raman intensity results from bands associated with rocking of CH 2 substituents directly attached to P(Ph) 2 groups coupling with the O-O band. These results provide part of the foundation for understanding oxygen ligands on a silica-supported tetrairidium carbonyl cluster stabilized with bulky electron-donating phosphine ligands [ p-tert -butyl-calix[4]arene(OPr) 3 (OCH 2 PPh 2 ) (Ph = phenyl; Pr = propyl)]. Reaction of the cluster with O2 also led to the growing in of a Raman band at 788 cm -1 , similar to that in the diiridium complex and also assigned to the bonding of a bridging peroxo ligand. Infrared spectra recorded as the supported cluster reacted in sequential exposures to (i) H 2 , (ii) O 2 , (iii) H 2 , and (iv) CO indicate that two bridging peroxo ligands were bonded irreversibly per tetrairidium cluster, replacing bridging carbonyl ligands without altering either the cluster frame or the phosphine ligands. X-ray absorption near edge and infrared spectra include isosbestic points signifying a stoichiometrically simple reaction of the cluster with O 2 , and mass spectra of the effluent gas show that CO 2 formed by oxidation of one terminal CO ligand per cluster as H 2 (and not H 2 O) formed, evidence that hydride ligands had been present on the cluster following treatment (i). The understanding of how O 2 reacts with the metal polyhedron provides a foundation for understanding of how oxidation catalysis may proceed on the surfaces of noble metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces

Here, this paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems and then deployed for potential energy prediction for C 5 H 5 using B2PLYP-D3/6-311++G(d,p) for high-fidelity simulation data and Hartree–Fock 6-31G for low-fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Impact of Coordination Environment on the Thermodynamic Stability of Uranium Oxides

Amorphous uranium oxides are acknowledged to arise via industrial processes relevant to the nuclear fuel cycle yet evade rigorous structural characterization. An encouraging approach is to develop statistical relationships between uranium–oxygen coordination environments and thermodynamic stability from which general statements about the likelihood of observing particular U–O arrangements can be made. The number of known crystalline uranium oxides is insufficient to build statistical relationships. We have developed a database of theoretical compounds using genetic algorithms with the density functional theory as a foundation to analyze coordination geometries in the uranium–oxygen phase space. We draw fundamental insights into the nature of uranium–oxygen interactions by correlating total energy with the coordination environment. The most stable configuration of U cations with O anions is in an environment with coordination numbers 5–8 in a cubic configuration. Higher and lower coordination numbers are observed only in metastable phases. General trends are observable in the relationships between the coordination number, density, and uranium fraction in each structure. The new insight into uranium coordination enabled by these analyses is foundational for further studies into the characteristic properties of individual uranium oxide materials and for elucidation of potential oxidation pathways for uranium metal.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Upconverting Nanoparticles as Optical Sensors of Nano- to Micro-Newton Forces

Mechanical forces affect a myriad of processes, from bone growth to material fracture to touch-responsive robotics. While nano- to micro-Newton forces are prevalent at the microscopic scale, few methods have the nanoscopic size and signal stability to measure them in vivo or in situ. Here, we develop an optical force-sensing platform based on sub-25 nm NaYF 4 nanoparticles (NPs) doped with Yb 3+ , Er 3+ , and Mn 2+ . The lanthanides Yb 3+ and Er 3+ enable both photoluminescence and upconversion, while the energetically coupled d-metal Mn 2+ adds force tunability through its crystal field sensitivity. IN using a diamond anvil cell to exert up to 3.5 GPa pressure or ~10 μN force per particle, we track stress-induced spectral responses. The red (660 nm) to green (520, 540 nm) emission ratio varies linearly with pressure, yielding an observed color change from orange to red for α-NaYF 4 and from yellow–green to green for d-metal optimized β-NaYF 4 when illuminated in the near infrared. We record consistent readouts over multiple pressure cycles and hours of illumination. With the nanoscopic size, a dynamic range of 100 nN to 10 μN, and photostability, these nanoparticles lay the foundation for visualizing dynamic mechanical processes, such as stress propagation in materials and force signaling in organisms.

36 MATERIALS SCIENCE↗

Structural Basis for Selectivity in Flavin-Dependent Monooxygenase-Catalyzed Oxidative Dearomatization

Biocatalytic reactions embody many features of ideal chemical transformations, including the potential for impeccable selectivity, high catalytic efficiency, mild reaction conditions, and the use of environmentally benign reagents. These advantages have created a demand for biocatalysts that expand the portfolio of complexity-generating reactions available to synthetic chemists. However, the trade-off that often exists between the substrate scope of a biocatalyst and its selectivity limits the application of enzymes in synthesis. We recently demonstrated that a flavin-dependent monooxygenase, TropB, maintains high levels of site- and stereoselectivity across a range of structurally diverse substrates. In this study, we disclose the structural basis for substrate binding in TropB, which performs a synthetically challenging asymmetric oxidative dearomatization reaction with exquisite site- and stereoselectivity across a range of phenol substrates, providing a foundation for future protein engineering and reaction development efforts. Our hypothesis for substrate binding is informed by a crystal structure of TropB and molecular dynamics simulations with the corresponding computational TropB model and is supported by experimental data. In contrast to canonical class A FAD-dependent monooxygenases in which substrates bind in a protonated form, our data indicate that the phenolate form of the substrate binds in the active site. Furthermore, the substrate position is controlled through two-point binding of the phenolate oxygen to Arg206 and Tyr239, which are shown to have distinct and essential roles in catalysis. Arg206 is involved in the reduction of the flavin cofactor, suggesting a role in flavin dynamics. Further, QM/MM simulations reveal the interactions that govern the facial selectivity that leads to a highly enantioselective transformation. Thus, the structural origins of the high levels of site- and stereoselectivity observed in reactions of TropB across a range of substrates are elucidated, providing a foundation for future protein engineering and reaction development efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Synthetic Control of Exciton Dynamics in Bioinspired Cofacial Porphyrin Dimers

Understanding how the complex interplay among excitonic interactions, vibronic couplings, and reorganization energy determines coherence-enabled transport mechanisms is a grand challenge with both foundational implications and potential payoffs for energy science. We use a combined experimental and theoretical approach to show how a modest change in structure may be used to modify the exciton delocalization, tune electronic and vibrational coherences, and alter the mechanism of exciton transfer in covalently linked cofacial Zn-porphyrin dimers (meso-beta linked AB m-β and meso-meso linked AA m-m ). While both AB m-β and AA m-m feature zinc porphyrins linked by a 1,2-phenylene bridge, differences in the interporphyrin connectivity set the lateral shift between macrocycles, reducing electronic coupling in AB m-β and resulting in a localized exciton. Pump-probe experiments show that the exciton dynamics is faster by almost an order of magnitude in the strongly coupled AA m-m dimer, and two-dimensional electronic spectroscopy (2DES) identifies a vibronic coherence that is absent in AB m-β . Theoretical studies indicate how the interchromophore interactions in these structures, and their system-bath couplings, influence excitonic delocalization and vibronic coherence-enabled rapid exciton transport dynamics. Real-time path integral calculations reproduce the exciton transfer kinetics observed experimentally and find that the linking-modulated exciton delocalization strongly enhances the contribution of vibronic coherences to the exciton transfer mechanism, and that this coherence accelerates the exciton transfer dynamics. These benchmark molecular design, 2DES, and theoretical studies provide a foundation for directed explorations of nonclassical effects on exciton dynamics in multiporphyrin assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Proton-Coupled Electron Transfer: Moving Together and Charging Forward

Proton-coupled electron transfer (PCET) is ubiquitous throughout chemistry and biology. This Perspective discusses recent advances and current challenges in the field of PCET, with an emphasis on the role of theory and computation. The fundamental theoretical concepts are summarized, and expressions for rate constants and kinetic isotope effects are provided. Computational methods for calculating reduction potentials and pKa’s for molecular electrocatalysts, as well as methods for simulating the nonadiabatic dynamics of photoinduced processes, are also described. Representative applications to PCET in solution, proteins, electrochemistry, and photoinduced processes are presented, highlighting the interplay between theoretical and experimental studies. The current challenges and suggested future directions are outlined for each type of application, concluding with an overall view to the future. The work described herein was supported by National Science Foundation Grant CHE-13-61293 (theory development), National Institutes of Health Grant GM056207 (soybean lipoxygenase), Center for Chemical Innovation of the National Science Foundation Solar Fuels Grant CHE-1305124 (cobalt catalysts), Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences (nickel catalysts), and Air Force Office of Scientific Research Award No. FA9550-14-1-0295 (photoinduced PCET).

Hammes-Schiffer, Sharon↗