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At least 559 records · Page 31

PigmentHunter: A point-and-click application for automated chlorophyll-protein simulations

Chlorophyll proteins (CPs) are the workhorses of biological photosynthesis, working together to absorb solar energy, transfer it to chemically active reaction centers, and control the charge-separation process that drives its storage as chemical energy. Yet predicting CP optical and electronic properties remains a serious challenge, driven by the computational difficulty of treating large, electronically coupled molecular pigments embedded in a dynamically structured protein environment. To address this challenge, we introduce here an analysis tool called PigmentHunter, which automates the process of preparing CP structures for molecular dynamics (MD), running short MD simulations on the nanoHUB.org science gateway, and then using electrostatic and steric analysis routines to predict optical absorption, fluorescence, and circular dichroism spectra within a Frenkel exciton model. Inter-pigment couplings are evaluated using point-dipole or transition-charge coupling models, while site energies can be estimated using both electrostatic and ring-deformation approaches. The package is built in a Jupyter Notebook environment, with a point-and-click interface that can be used either to manually prepare individual structures or to batch-process many structures at once. Here, we illustrate PigmentHunter’s capabilities with example simulations on spectral line shapes in the light harvesting 2 complex, site energies in the Fenna–Matthews–Olson protein, and ring deformation in photosystems I and II.

14 SOLAR ENERGY↗

How To Determine and Verify Operations and Maintenance Savings in Energy Savings Performance Contracts

Operations and maintenance (O&M) savings frequently occur in energy savings performance contracts (ESPCs). During FY 2022, 37% of reported annual cost savings for projects awarded under the U.S. Department of Energy (DOE) ESPC indefinite delivery indefinite quantity (IDIQ) contracts and in the performance period were due to O&M or other energy- and/or water-related cost savings, with the balance (63%) from utility cost savings (i.e., energy or water cost savings). Sometimes the energy- and water-related cost savings are acknowledged and included in payments within ESPCs; other times, for various reasons, they are not. As presented in this guide, FEMP recommends including energy- and water-related cost savings that are O&M (including related repair and replacement) savings in the financial aspects of an ESPC, to the extent such savings can be documented. Inclusion of these savings will help augment project scopes and/or lower interest costs (by shortening financing terms). However, there is a burden of proof as to what constitutes acceptability in O&M savings that needs to be carefully considered and documented in individual projects. Beyond promoting a key tenet used in U.S. federal performance contracting—that savings must be from actual budgets and therefore based on the level of O&M that is actually occurring, not what should have been performed—FEMP also recommends good practice in establishing and documenting O&M baselines, formulating the rationale for baseline adjustments during the performance period, and conducting ongoing verification activities. This document concludes with five examples of how O&M savings may be handled, in situations ranging from the partial displacement of O&M contracts to consolidation and “virtualization” of servers in data centers. A key theme that permeates this guide is the importance of thoroughly documenting all conditions and assumptions used in the development of and accounting for O&M costs and savings throughout the ESPC life cycle, from baseline-setting to measurement and verification (M&V) of the savings during each year of the performance period. Doing so not only prevents internal claims of non-performance (especially in the case of staff turnover during the contract term), but also simplifies ordering agency and energy service company (ESCO) response in the event of scrutiny from oversight organizations, such as government audits. While this guide focuses on federal ESPCs, it may also be applicable when O&M savings are included in utility energy service contracts (UESCs) and non-federal ESPCs.

Voss, Phil↗

Carbon cycling across ecosystem succession in a north temperate forest: Controls and management implications

Despite decades of progress, much remains unknown about successional trajectories of carbon (C) cycling in north temperate forests. Drivers and mechanisms of these changes, including the role of different types of disturbances, are particularly elusive. To address this gap, we synthesized decades of data from experimental chronosequences and long-term monitoring at a well-studied, regionally representative field site in northern Michigan, USA. Our study provides a comprehensive assessment of changes in above- and belowground ecosystem components over two centuries of succession, links temporal dynamics in C pools and fluxes with underlying drivers, and offers several conceptual insights to the field of forest ecology. Our first advance shows how temporal dynamics in some ecosystem components are consistent across severe disturbances that reset succession and partial disturbances that slightly modify it: both of these disturbance types increase soil N availability, alter fungal community composition, and alter growth and competitive interactions between short-lived pioneer and longer-lived tree taxa. Further, these changes in turn affect soil C stocks, respiratory emissions, and other belowground processes. Second, we show that some other ecosystem components have effects on C cycling that are not consistent over the course of succession. For example, canopy structure does not influence C uptake early in succession but becomes important as stands develop, and the importance of individual structural properties changes over the course of two centuries of stand development. Third, we show that in recent decades, climate change is masking or overriding the influence of community composition on C uptake, while respiratory emissions are sensitive to both climatic and compositional change. In synthesis, we emphasize that time is not a driver of C cycling; it is a dimension within which ecosystem drivers such as canopy structure, tree and microbial community composition change. Changes in those drivers, not in forest age, are what control forest C trajectories, and those changes can happen quickly or slowly, through natural processes or deliberate intervention. Stemming from this view and a whole-ecosystem perspective on forest succession, we offer management applications from this work and assess its broader relevance to understanding long-term change in other north temperate forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

A challenge in population ecology studies is identifying how to best group individuals into populations, especially when individual origin is unknown. Machine learning has improved upon traditional methods of identifying population structure and is more efficient at handling large, complex datasets. We demonstrate the applicability of a machine learning method to identify hierarchical population structure in an emerging pathogen, Coccidioides spp., the causative agent of Valley fever. We compared the network clusters to structure identified by traditional tools as a validation of the network performance. We used publicly available whole-genome data for 48 C. immitis and 102 C. posadasii, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

59 BASIC BIOLOGICAL SCIENCES↗

Schrödinger cat states of a nuclear spin qudit in silicon

High-dimensional quantum systems are a valuable resource for quantum information processing. They can be used to encode error-correctable logical qubits, which has been demonstrated using continuous-variable states in microwave cavities or the motional modes of trapped ions. For example, high-dimensional systems can be used to realize ‘Schrödinger cat’ states, which are superpositions of widely displaced coherent states that can be used to illustrate quantum effects at large scales. Recent proposals have suggested encoding qubits in high-spin atomic nuclei, which are finite-dimensional systems that can host hardware-efficient versions of continuous-variable codes. Here, in this study, we demonstrate the creation and manipulation of Schrödinger cat states using the spin-7/2 nucleus of an antimony atom embedded in a silicon nanoelectronic device. We use a multi-frequency control scheme to produce spin rotations that preserve the symmetry of the qudit, and we constitute logical Pauli operations for qubits encoded in the Schrödinger cat states. Our work demonstrates the ability to prepare and control non-classical resource states, which is a prerequisite for applications in quantum information processing and quantum error correction, using our scalable, manufacturable semiconductor platform.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

TRUST Sensors in Environments: Thermocouples (SE-TC), Release FY25

The Delivery Environments Testbeds to Reduce Uncertainty in Simulations and Tests (TRUST) project is a broad project intended to analyze simplified problems experimentally and with modeling and simulation. The purpose of analyzing these simplified problems is to extend solution methods to more complex problems, as well as understand deficiencies and gaps in knowledge of methods currently used in more complex analyses. The TRUST project encompasses several smaller testbeds intended to isolate individual phenomena. The testbed under consideration in this report is the Sensors in Environments: Thermocouples testbed. In previous years, the purpose of this testbed was to quantify uncertainty of thermocouple sensors. To accomplish this, an aluminum plate was placed in a thermal chamber and subject to various types of thermal loading. Thermocouples were placed in various locations on the aluminum plate in various configurations (e.g., embedded in the plate, placed under Kapton tape), and an effort was made to quantify uncertainty in these measurements. Finite element simulations were performed to investigate how sensitive these measurements were to parameters such as the boundary conditions on the plate and material properties. However, a fundamental source of uncertainty in this analysis was the convective heat transfer from the plate. Convective heat transfer is a complex physical phenomenon comprised of a number of interacting sub-processes and is difficult to predict accurately a priori. As such, the main purpose of this testbed in FY25 was to better understand, both experimentally and numerically, the convective heat transfer from the plate. This is a highly applicable problem to several more complex problems, as convective heat transfer occurs in nearly all problems where a body is moving through air. Numerically, this required a two-step approach. First, the air flow in the thermal chamber was in vestigated using computational fluid dynamics. The commercial solver Fluent was used to perform these simulations. From these simulations, a heat transfer coefficient over the surface of the plate was calculated. This heat transfer was then used as boundary conditions for finite element heat transfer simulations within the plate, which were performed using Abaqus. Significant effort was devoted to automating the handoff between these two solvers. Experimentally, previous thermocouple results in the plate were used to validate the time-dependent thermal profiles produced from Abaqus. Further experimental efforts were performed both to help validate the Fluent simulations and to inform its boundary conditions. For example, hot-wire anemometers were used to measure the velocity in the chamber, which would be particularly useful in understanding the chamber inlet velocity. Thermocouple measurements were also taken in the chamber, instead of only on the plate, to serve as validation evidence for the Fluent simulations. Numerical results showed that the Fluent to Abaqus workflow matched previous plate thermocouple measurements well. This type of handoff is useful for more complex experiments, or those that are not able to be examined in as great of detail as this testbed, as it was performed without any experimental input. Experimental results, however, were more mixed. The anemometers proved unreliable, with inconsistent measurements across all anemometers, even at locations that were nearly identical. On the other hand, the thermocouples provided a relatively rich view of the temperature field in the chamber.

42 ENGINEERING↗

Electron Bifurcating Hydrogenases

The importance of electron-bifurcating enzymes is manifest by their ability to maximize energy efficiency. Specifically, they couple a downhill oxidation-reduction (redox) reaction with an uphill redox reaction. Since the rapid increase in the discovery of bifurcating enzymes starting in 2008, there has been interest in incorporating their mechanistic principles into artificial/semiartificial systems to drive chemically challenging reactions. This has yet to be achieved, partly because the details of electron bifurcation, i.e. mechanisms, are largely elusive. Nevertheless, much progress has been made in understanding reactivities, structures, and some mechanistic aspects of these enzymes. Notable examples are electron-bifurcating hydrogenases, which are the focus of this chapter. The chapter is organized as follows. Section 11.1 provides an overview of hydrogenases and electron bifurcation. In Section 11.2, some physiological roles of electron-bifurcating hydrogenases are highlighted. Additionally, electron-bifurcating subunit compositions and biochemical reactivities are comprehensively tabulated, and some key points/considerations about these are noted. In Section 11.3, we discuss the known structures of these enzymes, which provide insight into their complex arrangements of redox cofactors, such as iron-sulfur clusters. Also provided are tabulations and discussions of some biophysical properties of the cofactors. In Section 11.4, we discuss the mechanistic proposals of these enzymes, which are primarily based on structural information. Areas of research that are much needed are outlined in Section 11.5. We conclude on the note that what is learned from electron-bifurcating hydrogenases has applicability to other bifurcating enzymes, nonbifurcating analogs, and mechanistic enzymology at large.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Room-Temperature Methane Oxidation to Formaldehyde Mediated by CoMoO + Gas-Phase Cations

Formaldehyde (HCHO) is a fundamental chemical feedstock with widespread industrial applications. The direct oxidation of methane by oxygen to formaldehyde (CH 4 + 1/2O 2 → H 2 + HCHO) under mild conditions represents an attractive but challenging transformation, as it requires both activation of the inert C–H bonds of CH 4 and suppression of overoxidation to products such as carbon dioxide. In this work, mass spectrometry experiments combined with theoretical calculations reveal that CoMoO + cations can efficiently mediate this transformation at room temperature. The unique electronic structure of CoMoO + facilitates the formation of a crucial CoMoOCH 2 + intermediate during the reaction with CH 4 and prevents methanol formation. In the subsequent oxidation reaction, the Mo atom in CoMoO + serves as the active site for O 2 adsorption, and both Mo and Co atoms act as electron donors to activate O 2 , leading to the formation of the C–O bond in formaldehyde. This work reports the first gas-phase example of achieving conversion of CH 4 to HCHO and its radical derivatives by O 2 at room temperature using heteronuclear non-noble metal cations. Remarkably, the CoMoOCH 2 + cation maintains high reactivity after adsorbing one or two CH 4 molecules. Finally, these findings provide new mechanistic insights into selective methane activation and conversion.

aldehydes↗

Performance of the spin-component-scaled methods for energy bands

The performance of various spin-component-scaled parameterisations is examined for the second-order many-body Green's-function [MBGF(2)] calculations of valence energy bands, taking three of the experimentally well-characterised polymers as examples: polyethylene, polytetrafluoroethylene, and polyacetylene. The parameterisations considered are Grimme's original SCS parameter set, Jung et al.'s original SOS set (retaining the opposite-spin component only), Śmiga et al.'s SCS(IP) set (calibrated specifically for ionization energies), and Śmiga et al.'s SOS(IP) set (calibrated for ionization energies with the opposite-spin component only; implicit in the os-D2 model of Opoku et al.). The SCS(IP) and SOS(IP) parameterisations are found to shift both outer and inner valence bands by up to a few electronvolts away from the experimental data. The original SCS and SOS parameter sets do not improve upon, but largely maintain the accuracy of the unscaled MBGF(2) methods. Given that the SOS-MBGF(2) method can be implemented in a quartic-scaling algorithm (for all roots), it is most promising for solid-state applications. Furthermore this observation is consistent with the success of the quartic-scaling GW methods without the vertex correction based on a density-functional theory reference.

Green’s-function theory↗

Improving adhesive bonding of short carbon fiber thermoplastic composites to aluminum alloys with a hybrid laser-plasma surface modification strategy

This study investigates hybrid laser–plasma surface modification strategies for metal–CFRTP (carbon-fiber-reinforced thermoplastic polymer) dissimilar joints to improve their bonding performance, in contrast to existing literature that mostly focuses on either plasma or laser treatment alone. By conducting double cantilever beam (DCB) tests on adhesively-bonded AA5052 and CFRPA66 (carbon-fiber-reinforced polyamide 66) joints, as an example of metal–CFRTP joints, it was found that laser engraving on the metal surface combined with plasma treatment on the CFRTP surface significantly improved the specific fracture energy of the joint by 187% and 31% compared to as-received and plasma-treated-only joints, respectively. However, the hybrid treatment of laser engraving and plasma on the investigated CFRTP surface did not improve the bonding performance of the joints. The underlying mechanisms related to hybrid laser-plasma surface modification strategies were further investigated by examining the surface and cross-sectional morphologies after DCB testing using microscopy. Computational modeling was performed to elucidate the interaction between grooves on the metal substrate and the CFRTP–adhesive interfacial bonding in metal–CFRTP joints. This study provides new insights into developing surface modification methods for achieving strong metal–CFRTP adhesive joints, aimed at lightweighting structural components in automotive, aerospace, and other applications.

Adhesive bonding↗

Projective Representations, Bogomolov Multiplier, and Their Applications in Physics

We present a pedagogical review of projective representations of finite groups and their physical applications in quantum many-body systems. Some of our physical results are new. We begin with a self-contained introduction to projective representations, highlighting the role of group cohomology, representation theory, and classification of irreducible projective representations. We then focus on a special subset of cohomology classes, known as the Bogomolov multiplier, which consists of cocycles that are symmetric on commuting pairs but remain nontrivial in group cohomology. Such cocycles have important physical implications: they characterize (1+1)D SPT phases that cannot be detected by string order parameters and give rise, upon gauging, to distinct gapped phases with completely broken non-invertible Rep(G) symmetry. We construct explicit lattice models for these phases and demonstrate how they are distinguished by the fusion rules of local order parameters. We show that a pair of completely broken Rep(G) SSB phases host nontrivial interface modes at their domain walls. As an example, we construct a lattice model where the ground state degeneracy on a ring increases from 32 without interfaces to 56 with interfaces.

Bogomolov multiplier↗

Efficient Training of Deep Neural Operator Networks via Randomized Sampling

Neural operators (NOs) employ deep neural networks to learn the mappings between infinitedimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, we introduce a random sampling technique to be adopted during the training of DeepONet, aimed at improving the generalization ability of the model, while significantly reducing the computational time. The proposed approach targets the trunk network of the DeepONet model that outputs the basis functions corresponding to the spatiotemporal locations of the bounded domain on which the physical system is defined. While constructing the loss function, DeepONet training traditionally considers a uniform grid of spatiotemporal points at which all the output functions are evaluated for each iteration. This approach leads to a larger batch size, resulting in poor generalization and increased memory demands, due to the limitations of the stochastic gradient descent (SGD) optimizer. The proposed random sampling over the inputs of the trunk net mitigates these challenges, improving generalization and reducing the memory requirements during training, resulting in significant computational gains. We validate our hypothesis through three benchmark examples, demonstrating substantial reductions in training time while achieving comparable or lower overall test errors relative to the traditional training approach. Our results indicate that incorporating randomization in the trunk network inputs during training enhances the efficiency and robustness of DeepONet, offering a promising avenue for improving the framework’s performance in modeling complex physical systems.

Karumuri, Sharmila [Department of Civil & Systems ↗

Compressed Air Scoping Tool Teaches Energy Efficiency Best Practices

Standards and practices around compressed air systems are evolving as previous golden rules become outdated and unhelpful. One of the most referenced guidelines gives us the perfect example: The previous rule of thumb for compressed air storage – 1-3 gal/cfm1,2 – is now accepted to be 3-5 gal/cfm. This change in recommended storage is only one of many in the industry, resulting in industrial users lost in a sea of contradictory best practices. To help combat confusion and poor information sharing, a collaborative effort between Oak Ridge National Laboratory (ORNL) and the Department of Energy’s (DOE’s) Better Plants program created the Compressed Air (CA) Scoping Tool with the most up-to-date best practices in the industry (as of 2023). It can be used as a nonbiased, one-stop shop for best practice recommendations. The Excelbased tool is designed to be an initial step in understanding the operation of a compressed air system, a baselining tool enabling users to comprehend various aspects of a facility’s system from the production of compressed air to its application by end users.

42 ENGINEERING↗

Status Report on Design of In-situ Thermomechanical Testing at LANSCE

Nuclear fuel encounters severe thermomechanical environments in which its mechanical response is determined by its microstructure, temperature and stress level histories. Simulating the response of such microstructures is crucial for predicting both performance and transient fuel mechanical responses and experimental verification of such predictions is therefore of great interest. While most of the deformation in a nuclear fuel rod occurs in the cladding, deformation of the fuel itself is still of interest with deformation mechanisms at operating temperature and above including creep, swelling, cracking as well as pellet-clad interaction. Characterization of these properties and understanding of the underlying deformation phenomena at operating or excursion temperatures is therefore of great importance for development and ultimately licensing of improved and novel nuclear fuel forms. Diffraction techniques offer unique insight on the atomistic (e.g. crystal structure) and microstructure (e.g. phase transformations, texture, defects) length scales and have a long history of providing unique data to inform relevant deformation models that enable the required predictive capabilities. For example, dislocations lead to diffraction peak broadening that can be characterized to estimate the dislocation density and study the role of dislocations on the deformation while measuring lattice strains allows to studie load sharing in two phase materials. In this report the requirements for a sample environment for high temperature deformation of nuclear fuels are defined. The HIPPO neutron time-of-flight diffractometer at LANSCE will host this sample environment and is also described. This instrument covers diffraction angles from 140° to 40° and is also equipped with an event-mode neutron imaging detector system, enabling energy-resolved neutron imaging in parallel with the diffraction that could measure sample temperature from Doppler broadening of neutron absorption resonances or measure pore densities from changes in the attenuation. Designs of devices to characterize thermomechanical properties of nuclear fuel without diffraction are also considered to guide the design. While this report is focused on applications for nuclear fuels, the device can also characterize cladding, moderator or structural materials and therefore contribute to other fields of research and development for advanced reactors. The temperatures planned to be reached are above 2000℃, thus enabling characterization of LWR reactor fuels under accident scenarios but also reaching temperatures of fuels developed for nuclear thermal propulsion and providing opportunities to characterize those. In conjunction with the energy-resolved neutron imaging detector, this setup would allow to measure neutron cross-sections at high temperatures, filling a gap towards development of reactors operating at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Scalable Nanoimprint Manufacturing of Functional Multilayer Metasurface Devices

Optical metasurfaces, consisting of subwavelength-scale meta-atom arrays, hold great promise of overcoming the fundamental limitations of conventional optics. Due to their structural complexity, metasurfaces usually require high-resolution yet slow and expensive fabrication processes. Here, using a metasurface polarimetric imaging device as an example, the photonic structures and the Nanoimprint lithography (NIL) processes are designed, creating two separate NIL molds over a patterning area of > 20 mm2 with designed Moiré alignment markers by electron-beam writing, and further subsequently integrate silicon and aluminum metasurface structures on a chip. Uniquely, the silicon and aluminum metasurfaces are fabricated by using the nanolithography and 3D pattern-transfer capabilities of NIL, respectively, achieving nanometer-scale linewidth uniformity, sub-200 nm translational overlay accuracy, and <0.017 rotational alignment error while significantly reducing fabrication complexity and surface roughness. Here, the micro-sized multilayer metasurfaces have high circular polarization extinction ratios as large as ≈20 and ≈80 in blue and red wavelengths. Further, the metasurface chip-integrated CMOS imager demonstrates high accuracy in broad-band, full Stokes parameter analysis in the visible wavelength ranges and single-shot polarimetric imaging. This novel, NIL-based, multilayered nanomanufacturing approach is applicable to the scalable production of large-area functional structures for ultra-compact optic, electronic, and quantum devices.

36 MATERIALS SCIENCE↗

Few measurement shots challenge generalization in learning to classify entanglement

The ability to extract general laws from a few known examples depends on the complexity of the problem and on the amount of training data. In the quantum setting, the learner's generalization performance is further challenged by the destructive nature of quantum measurements that, together with the no-cloning theorem, limits the amount of information that can be extracted from each training sample. In this paper we focus on hybrid quantum learning techniques where classical machine-learning methods are paired with quantum algorithms and show that, in some settings, the uncertainty coming from a few measurement shots can be the dominant source of errors. We identify an instance of this possibly general issue by focusing on the classification of maximally entangled vs. separable states, showing that this toy problem becomes challenging for learners unaware of entanglement theory. Finally, we introduce an estimator based on classical shadows that performs better in the big data, few copy regime. Our results show that the naive application of classical machine-learning methods to the quantum setting is problematic, and that a better theoretical foundation of quantum learning is required.

97 MATHEMATICS AND COMPUTING↗

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗