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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Single photon emitters in van der Waals solids for quantum photonics: materials, theory and molecular-scale characterization probes

Strong light–matter interactions in two-dimensional layered materials (2D materials) have attracted the interest of researchers from interdisciplinary fields for more than a decade now. A unique phenomenon in some 2D materials is their large exciton binding energies (BEs), increasing the likelihood of exciton survival at room temperature. It is this large BE that mediates the intense light–matter interactions of many of the 2D materials, particularly in their monolayer limit, where the interplay of excitonic phenomena poses a wealth of opportunities for high-performance optoelectronics and quantum photonics. Within quantum photonics, quantum information science (QIS) is growing rapidly, where photons are a promising platform for information processing due to their low-noise properties, excellent modal control, and long-distance propagation. A central element for QIS applications is a single photon emitter (SPE) source, where an ideal on-demand SPE emits exactly one photon at a time into a given spatiotemporal mode. Recently, 2D materials have shown practical appeal for QIS which is directly driven from their unique layered crystalline structure. This structural attribute of 2D materials facilitates their integration with optical elements more easily than the SPEs in conventional three-dimensional solid state materials, such as diamond and SiC. In this review article, we will discuss recent advances made with 2D materials towards their use as quantum emitters, where the SPE emission properties maybe modulated deterministically. Here, the use of unique scanning tunneling microscopy tools for the in-situ generation and characterization of defects is presented, along with theoretical first-principles frameworks and machine learning approaches to model the structure-property relationship of exciton–defect interactions within the lattice towards SPEs. Given the rapid progress made in this area, the SPEs in 2D materials are emerging as promising sources of nonclassical light emitters, well-poised to advance quantum photonics in the future.

2D layered materials↗
DOE OSTI2025

Investigation of the Effect of Framework Flexibility on CO 2 Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

Metal–organic frameworks (MOFs) offer promise as selective CO 2 sorbents, but successful MOF sorbent materials need high CO 2 binding affinity and selectivity for CO 2 over water. This work focuses on the use of machine-learned force fields (MLFFs) to model CO 2 adsorption in flexible MOFs, with a focus on SIFSIX-3-Cu, an anion-pillared MOF known for its high CO 2 affinity. A preliminary high-throughput screening of over 900 anion-pillared MOFs was performed using rigid UFF+DDEC6 force fields to predict zero-loading heats of adsorption for CO 2 and H 2 O. SIFSIX-3-Cu was selected for further computational study due to its predicted CO 2 heat of adsorption and experimental relevance. A DeePMD-based MLFF was trained to reproduce DFT (PBE+D3) energies and forces, with an iterative sampling scheme combining molecular dynamics, geometry optimization, random geometric insertion, and NVT Monte Carlo-based configuration generation to capture both attractive and repulsive regions of the potential energy surface. Flexibility of the MOF was explicitly included, contrasting with previous models that approximated the MOF as rigid. Hybrid Monte Carlo/molecular dynamics (MC/MD) simulations with the MLFF produced CO 2 adsorption isotherms in good agreement with experimental data at direct air capture (DAC) pressures (e.g., 40 Pa), in contrast to previous overestimations of CO 2 sorption by models with rigid structures. Bond and angle histogram analysis showed that MOF flexibility increased the variance of fluorine–fluorine diagonal distances at adsorption sites, resulting in a lower predicted sorption for flexible, asymmetric SIFSIX-3-Cu pore geometries compared to the rigid, symmetric DFT-optimized SIFSIX-3-Cu pore geometry. A detailed description of flexibility afforded by the MLFF resulted in an accurately predicted CO 2 uptake (0.88 mmol/g) at low pressure (40 Pa) compared to the experimentally measured value (1.24 mmol/g). In conclusion, these results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low-pressure applications.

adsorption↗
DOE OSTI2026

Quantum fluctuations in dense plasma simulations

Molecular dynamics (MD) simulations are a powerful tool for modeling warm and hot dense matter. Density functional theory (DFT) MD simulations are often preferred in dense plasmas in order to accurately model quantum electronic structure. However, DFT-MD simulations neglect interaction effects due to fluctuations in excited states. In this work, we present an MD approach that uses excited state method pseudoatoms to run dense plasma simulations with many different core-electron configurations at classical MD speeds. We also allow for transitions between different configurations in our simulations and find that these fluctuations are especially important for highly excited states. Our results suggest that finite configuration lifetimes that are comparable to the inverse ion plasma frequency need to be accounted for in order to accurately model ion distributions in dense plasma simulations. We also demonstrate that excited state fluctuations have a direct impact on ion plasma microfields, generate different plasma microfields for different excitation levels, and thereby induce absorption–emission line shape asymmetries even in steady-state plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model‐data integration workflow to address uncertainty in groundwater response to beaver‐induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning‐based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down‐valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down‐valley underflow, from 2 during dry pond periods to 20 during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down‐valley underflow, minimizes the effect of beaver‐induced inundation on water quality. We emphasize the need for field‐scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing↗
DOE OSTI2024

ExaCA grain structure predictions for laser powder bed fusion processing

This dataset contains simulated cross-sections of laser powder bed fusion grain structures produced using the microstructure model ExaCA, in turn using time-temperature history data produced using the heat transport model AdditiveFOAM. These predictions show the grain structure using various permutations of hatch spacing and nucleation density. Also contained in the dataset are the input files necessary to reproduce the results. AdditiveFOAM (https://github.com/ORNL/AdditiveFOAM) and ExaCA (https://github.com/LLNL/ExaCA), both open source software, are required to reproduce the results in this dataset using the given input files. This DOI was updated 12/09/2025.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of layer bending on montmorillonite hydration and structure from molecular simulation

Conceptual models of smectite hydration include planar (flat) clay layers that undergo stepwise expansion as successive monolayers of water molecules fill the interlayer regions. However, X-ray diffraction (XRD) studies indicate the presence of interstratified hydration states, suggesting non-uniform interlayer hydration in smectites. Additionally, recent theoretical studies have shown that clay layers can adopt bent configurations over nanometer-scale lateral dimensions with minimal effect on mechanical properties. Therefore, in this study we used molecular simulations to evaluate structural properties and water adsorption isotherms for montmorillonite models composed of bent clay layers in mixed hydration states. Results are compared with models consisting of planar clay layers with interstratified hydration states (e.g. 1W–2W). The small degree of bending in these models (up to 1.5 Å of vertical displacement over a 1.3 nm lateral dimension) had little or no effect on bond lengths and angle distributions within the clay layers. Except for models that included dry states, porosities and simulated water adsorption isotherms were nearly identical for bent or flat clay layers with the same averaged layer spacing. Similar agreement was seen with Na- and Ca-exchanged clays. In conclusion, while the small bent models did not retain their configurations during unconstrained molecular dynamics simulation with flexible clay layers, we show that bent structures are stable at much larger length scales by simulating a 41.6×7.1 nm 2 system that included dehydrated and hydrated regions in the same interlayer.

58 GEOSCIENCES↗
DOE OSTI2024

The hierarchical structure of organic mixed ionic–electronic conductors and its evolution in water

Polymeric organic mixed ionic–electronic conductors underpin several technologies in which their electrochemical properties are desirable. These properties, however, depend on the microstructure that develops in their aqueous operational environment. We investigated the structure of a model organic mixed ionic–electronic conductor across multiple length scales using cryogenic four-dimensional scanning transmission electron microscopy in both its dry and hydrated states. Four-dimensional scanning transmission electron microscopy allows us to identify the prevalent defects in the polymer crystalline regions and to analyse the liquid crystalline nature of the polymer. The orientation maps of the dry and hydrated polymers show that swelling-induced disorder is mostly localized in discrete regions, thereby largely preserving the liquid crystalline order. Therefore, the liquid crystalline mesostructure makes electronic transport robust to electrolyte ingress. Furthermore, this study demonstrates that cryogenic four-dimensional scanning transmission electron microscopy provides multiscale structural insights into complex, hierarchical structures such as polymeric organic mixed ionic–electronic conductors, even in their hydrated operating state.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interactive multiscale modeling to bridge atomic properties and electrochemical performance in Li-CO 2 battery design

Li-CO 2 batteries are promising energy storage systems due to their high theoretical energy density and CO 2 fixation capability, relying on reversible Li 2 CO 3 /C formation during discharge/charge cycles. Here, we present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO 2 battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb 0.67 Bi 1.33 Te 3 catalyst. DFT and AIMD determined the electrical conductivities of Sb 0.67 Bi 1.33 Te 3 and Li 2 CO 3 using the Kubo–Greenwood formalism and studied the CO 2 reduction mechanism on the cathode catalyst. MD simulations calculated the CO 2 diffusion coefficient, Li + transference number, ionic conductivity, and Li + solvation structure. The FEA model, parameterized with atomistic simulation data, reproduced the available experimental voltage–capacity profile at 1 mA/cm 2 and revealed spatio-temporal variations in Li 2 CO 3 /C deposition, porosity, and CO 2 concentration dependence on discharge rates in the cathode. Accordingly, Li 2 CO 3 can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm 2 and 1 mA/cm 2 , respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm 2 to 6200 mAh/g at 1 mA/cm 2 , due to pore clogging from excessive discharge product deposition that limits CO 2 transport to the cathode interior. Therefore, the performance of Li-CO 2 batteries can be improved by enhancing CO 2 transport, regulating Li 2 CO 3 deposition, and optimizing cathode architecture.

Battery performance↗
DOE OSTI2025

A multifrequency study of sub-parsec jets with the Event Horizon Telescope

Context. The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed, including the main science targets, Sgr A* and M 87, and various calibrators. Sixteen sources were AGN.Aims. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-Königl (BK) jet model. In particular, we aimed to study the signatures of jet acceleration and magnetic energy conversion.Methods. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components (1749+096, 1055+018, BL Lac, J0132–1654, J0006–0623, CTA 102, and 3C 454.3) and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2–86 GHz range. Combining these data into a multifrequency EHT+ data set, we studied the dependences of the VLBI core component flux density, size, and brightness temperature on the frequency measured in the AGN host frame (and hence on the distance from the central black hole), characterizing them with power law fits. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole.Results. Our observations spanning event horizon to parsec scales indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Only some of the discrepancies may be alleviated by tweaking the model parameters or the jet collimation profile. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations. For our sample, we estimate a general radial dependence of the Doppler factor δ ∝ r≤0.5. This interpretation is consistent with a magnetically accelerated sub-parsec jet. We also estimate a steep decrease of the magnetic field strength with radius B ∝ r−3, hinting at jet acceleration or efficient magnetic energy dissipation.Key words: techniques: interferometric / galaxies: active / galaxies: jets / galaxies: nuclei / quasars: general / quasars: supermassive black holes⋆⋆ Deceased.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

X-ray photoelectron spectroscopy of surfactants on sub-micron aqueous aerosols

Organic molecules at the aqueous interfaces of droplets and aerosols can influence multiphase chemistry and cloud condensation nuclei activity. While surface tension measurements on flat surfaces are commonly used to quantify organic partitioning and structure, extending these measurements to more realistic aerosols that might exist in metastable, supercooled and supersaturated states remains a challenge. Here, we use aerosol velocity map imaging X-ray photoelectron spectroscopy (A-VMI-XPS), a surface-sensitive and in situ technique, to study the partitioning and structure of a model surfactant (n-octyl β-D-thioglucopyranoside, OTG) in submicron aqueous aerosols with a mean radius of 116 nm. After accounting for finite size effects and evaporative cooling, we find the bulk concentrations of OTG in the aerosol are depleted 600-fold relative to macroscopic solutions. At the low temperatures of the experiment (ca. ∼188 K), we obtain a Langmuir equilibrium constant of 700 m3 mol-1, which corresponds to 4.9 m3 mol-1 at room temperature; a value that is consistent with prior literature reports. Analysis of photoelectron angular distributions, peak areas, and secondary electron escape barriers indicates a transition from a disordered surfactant layer at low [OTG] to a more structured layer at high [OTG]. These results link macroscopic surface tension measurements to nanoparticle surfactant behavior and demonstrate the utility of A-VMI-XPS in probing surfactant coating structure on free aerosols, with implications for understanding the impact of organic coatings on cloud formation and atmospheric chemistry.

Deal, Alexandra M↗
DOE OSTI2024

Mechanisms and models of the turbulent boundary layers at transcritical conditions

Computational models for high-pressure transcritical turbulence in wall-modeled large-eddy simulation typically rely on wall-function models to overcome the need for resolving the boundary layer structures. However, the mechanisms and models of turbulent boundary layer at transcritical conditions remain poorly understood since the near-wall flow and heat transfer are significantly affected by the enhanced fluctuations and steep gradients in thermodynamic properties. Here, to address this issue, we study the mechanisms of transcritical turbulent boundary layers and wall-attached models at transcritical conditions. It is shown that the real-fluid variable-property effects are associated with the coupling between the near-wall cycle and the wall-normal coherent motions in the outer layer, resulting in the amplification of turbulent energy in the outer layer and noticeable energy transfer between the log-layer and the outer layer; hence, turbulence in the log-layer is modulated by the outer layer. Based on this underlying physical principle, we propose the characteristic velocity and length scales for the attached eddy at transcritical conditions, and extend the attached eddy model to transcritical turbulent boundary layers by introducing a mixed scaling that incorporates both inner and outer scalings. We show that the new characteristic scales and extended attached-eddy model perform well in characterizing the structures in transcritical turbulent boundary layers.

Li, Fangbo↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ramen

Many computationally unintensive models for material process-structure-property connections exist in the literature (e.g. journal articles and textbooks). However, to our knowledge there is no existing compilation of these models in one package. Many industrially and research relevant workflows involve combinations of many of these models and if the models have inconsistent interfaces, building these workflows can be challenging. Ramen provides a collection of computationally unintensive models for calculating material process-structure-property relationships from the literature, as well as related tools (e.g. for plotting results). These models use a consistent interface structure and all leverage material data files and data structures from the Mist library (also developed at ORNL). Ramen provides a structured package for combining many standard models in one place, with a standardized interface. This approach makes combining models in long/complex chains easy and straightforward.

DeWitt, Stephen [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Partial wrapping of single-stranded DNA by replication protein A and modulation through phosphorylation

Abstract Single-stranded DNA (ssDNA) intermediates which emerge during DNA metabolic processes are shielded by replication protein A (RPA). RPA binds to ssDNA and acts as a gatekeeper to direct the ssDNA towards downstream DNA metabolic pathways with exceptional specificity. Understanding the mechanistic basis for such RPA-dependent functional specificity requires knowledge of the structural conformation of ssDNA when RPA-bound. Previous studies suggested a stretching of ssDNA by RPA. However, structural investigations uncovered a partial wrapping of ssDNA around RPA. Therefore, to reconcile the models, in this study, we measured the end-to-end distances of free ssDNA and RPA–ssDNA complexes using single-molecule FRET and double electron–electron resonance (DEER) spectroscopy and found only a small systematic increase in the end-to-end distance of ssDNA upon RPA binding. This change does not align with a linear stretching model but rather supports partial wrapping of ssDNA around the contour of DNA binding domains of RPA. Furthermore, we reveal how phosphorylation at the key Ser-384 site in the RPA70 subunit provides access to the wrapped ssDNA by remodeling the DNA-binding domains. These findings establish a precise structural model for RPA-bound ssDNA, providing valuable insights into how RPA facilitates the remodeling of ssDNA for subsequent downstream processes.

Biochemistry & Molecular Biology↗
DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗
DOE OSTI2024

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin–Iron Complexes: Novel Pipeline and Complex Structures

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56 Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37 Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeL x Cl y (L: organic ligand, x = 1–4, y = 0–3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Updating Nuclear Energy Cost Estimates for Net Zero World Initiative

Energy modeling of decarbonized scenarios in integrated energy systems requires nuclear energy parameters that are critical for forecasting, modeling and cost structure analysis. Using updated real-world data has always been a challenge to estimate current nuclear reactors costs and deployment scenarios. Given this, an updated set of parameters for overnight capital costs and operation and maintenance costs are estimated for the Net Zero World initiative using recent reports that provided a vast set of open sources data inputs. This paper follows the methodology developed in the Net Zero World report and applies the new ranges estimated in the Gateway for Accelerated Innovation in Nuclear report that address many of the current challenges in obtaining accurate cost data for advanced nuclear concepts. The final goal is to provide new estimates of the overnight capital costs and operational costs for different countries. The present paper improves the earlier capital cost estimations, building on recent literature that aims to obtain accurate data for modeling and simulation to enhance energy system evaluations and support decision-making in areas like de-carbonization and capacity expansion. Finally, the paper compares the new cost estimates with the old cost results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Effects of Composition and Oxidation States on the Structures of Chromium-Containing Sodium Silicate Glasses: Molecular Dynamics Simulations using Machine Learning Interatomic Potentials

Chromium represents a significant challenge for the vitrification of high-level nuclear waste into silicate and borosilicate glasses due to its low solubility and variable oxidation states, which can limit the waste loading due to promotion of crystallization or phase separation during processing. In this study, we modeled chromium containing silicate glasses using molecular dynamics simulations with three machine learning interatomic potentials (MLIPs), MACE, CHGNet, and PFP were employed, to gain insights on glass composition and oxidation states on the structures of these glasses. One of the goals is to evaluate their ability of these MLIPs to accurately represent the general structure of silicate glasses and chromium local environments as a function of chromium oxidation states. Density Functional Theory (DFT) based calculations and experimental data such as neutron structure factors were used to validate the structural models. It was found that the foundation models of all three MLIPs are able to reproduce general structural features of the sodium silicate glass structure consistent with experimental and DFT data, but only CHGNet and PFP can accurately capture the oxidation states and local environment of chromium: tetrahedral for Cr6+ and octahedral for Cr3+. Furthermore, we studied the effect of varying Cr3+/ Cr6+ (Cr3+/Crtotal) ratio and total chromium content using PFP. Our results show that Cr6+ enhances network polymerization by reducing non-bridging oxygens through Na? charge compensation required due to the formation of chromate (CrO42-) species, while Cr³? acts as a network modifier that disrupts connectivity. System size effects on the structural characteristics and chromium environments were also tested using the PFP potential. This work highlights the importance of careful validation on the precision, transferability, and potential of MLIPs for modeling glasses containing transition metal elements that can exist in multiple oxidation states. It is also encouraging to see the foundational models are all three MLFFs are able to reproduce the basic sodium silicate glass structures, while suggesting additional training or refining is needed to improve the description of more complex systems containing transition metals.

Puga, Christina L.↗