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DOE OSTI2016

Synchrotron emission from dark matter in galactic subhalos. A look into the Smith cloud

One of the key predictions of the ''WIMP'' paradigm for Dark Matter (DM) is that DM particles can annihilate into charged particles. These annihilations will proceed in e.g. Galactic subhalos such as dwarf Galaxies or, as recently pointed out, high velocity clouds such as the ''Smith Cloud''. In this note, we focus on the radio emission associated with DM annihilations into electrons and positrons occurring in the Smith Cloud. The phenomenology of this emission is discussed in quite some detail. We argue that the uncertainties in the propagation can be captured by the typical diffusion-loss length parameter (Syrovatskii variable) but that the angle-integrated radio fluxes are independent of the propagation. We conclude that if the Smith Cloud is indeed dominated by DM, radio signals from DM annihilation stand out amongst other messengers. Furthermore, low frequencies such as the ones observed by e.g. the Low Frequency Array (LOFAR) and the next-generation Square Kilometre Array (SKA) are optimal for searches for DM in the Smith Cloud. As a practical application, we set conservative constraints on dark matter annihilation cross section using data of continuum radio emission from the Galaxy at 22 MHz and at 1.4 GHz. Stronger constraints could be reached by background subtraction, exploiting the profile and frequency dependence of the putative DM signal. We set stronger but tentative limits using the median noise in brightness temperature from the Green Bank Telescope and the LOFAR sensitivities.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Quantum Noise Spectroscopy of Dynamical Critical Phenomena

The transition between distinct phases of matter is characterized by the nature of fluctuations near the critical point. We demonstrate that noise spectroscopy can not only diagnose the presence of a phase transition, but can also determine fundamental properties of its criticality. In particular, by analyzing a scaling collapse of the decoherence profile, one can directly extract the critical exponents of the transition and identify its universality class. Our approach naturally captures the presence of conservation laws and distinguishes between classical and quantum phase transitions. In the context of quantum magnetism, our proposal complements existing techniques and provides a novel toolset optimized for interrogating two-dimensional magnetic materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

FiXR : a framework to reconstruct fiber cross-sections from X-ray fiber diffraction experiments

Ab initio reconstruction methods have revolutionized the capabilities of small-angle X-ray scattering (SAXS), allowing the data-driven discovery of previously unknown molecular conformations, exploiting optimization heuristics and assumptions behind the composition of globular molecules. While these methods have been successful for the analysis of small particles, their impact on fibrillar assemblies has been more limited. The micrometre-range size of these assemblies and the complex interaction of their periodicities in their scattering profiles indicate that the discovery of fibril structures from SAXS measurements requires novel approaches beyond extending existing tools for molecular discovery. In this work, it is proposed to use SAXS measurements, together with diffraction theory, to infer the electron distribution of the average cross-section of a fiber. This cross-section is modeled as a discrete electron density with continuous support, allowing representations beyond binary distributions. Additional constraints, such as non-negativity or smoothness/connectedness, can also be added to the framework. The proposed approach is tested using simulated SAXS data from amyloid β fibril models and using measured data of Tobacco mosaic virus from SAXS experiments, recovering the geometry and density of the cross-sections in all cases. The approach is further tested by analyzing SAXS data from different amyloid β fibril assemblies, with results that are in agreement with previously proposed models from cryo-EM measurements. The limitations of the proposed method, together with an analysis of the robustness of the method and the combination with different experimental sources, are also discussed.

Biochemistry & Molecular Biology↗

DOE OSTI2016

SU-C-201-04: Noise and Temporal Resolution in a Near Real-Time 3D Dosimeter

Purpose: To characterize the performance of a real-time three-dimensional scintillation dosimeter in terms of signal-to-noise ratio (SNR) and temporal resolution of 3D dose measurements. This study quantifies its efficiency in measuring low dose levels characteristic of EBRT dynamic treatments, and in reproducing field profiles for varying multileaf collimator (MLC) speeds. Methods: The dosimeter prototype uses a plenoptic camera to acquire continuous images of the light field emitted by a 10×10×10 cm{sup 3} plastic scintillator. Using EPID acquisitions, ray tracing-based iterative tomographic algorithms allow millimeter-sized reconstruction of relative 3D dose distributions. Measurements were taken at 6MV, 400 MU/min with the scintillator centered at the isocenter, first receiving doses from 1.4 to 30.6 cGy. Dynamic measurements were then performed by closing half of the MLCs at speeds of 0.67 to 2.5 cm/s, at 0° and 90° collimator angles. A reference static half-field was obtained for measured profile comparison. Results: The SNR steadily increases as a function of dose and reaches a clinically adequate plateau of 80 at 10 cGy. Below this, the decrease in light collected and increase in pixel noise diminishes the SNR; nonetheless, the EPID acquisitions and the voxel correlation employed in the reconstruction algorithms result in suitable SNR values (>75) even at low doses. For dynamic measurements at varying MLC speeds, central relative dose profiles are characterized by gradients at %D{sub 50} of 8.48 to 22.7 %/mm. These values converge towards the 32.8 %/mm-gradient measured for the static reference field profile, but are limited by the dosimeter’s current acquisition rate of 1Hz. Conclusion: This study emphasizes the efficiency of the 3D dose distribution reconstructions, while identifying limits of the current prototype’s temporal resolution in terms of dynamic EBRT parameters. This work paves the way for providing an optimized, second-generational real-time 3D scintillation dosimeter capable of highly efficient and precise dose measurements. The presenting author is financially supported by an Alexander-Graham Bell doctoral scholarship from the Natural Sciences and Engineering Research Council of Canada (NSERC).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

A robust and efficient method for the extraction of plant extracellular surface lipids as applied to the analysis of silks and seedling leaves of maize

Aerial plant organs possess a diverse array of extracellular surface lipids, including both non-polar and amphipathic constituents that collectively provide a primary line of defense against environmental stressors. Extracellular surface lipids on the stigmatic silks of maize are composed primarily of saturated and unsaturated linear hydrocarbons, as well as fatty acids, and aldehydes. To efficiently extract lipids of differing polarities from maize silks, five solvent systems (hexanes; hexanes:diethyl ether (95:5); hexanes:diethyl ether (90:10); chloroform:hexanes (1:1) and chloroform) were tested by immersing fresh silks in solvent for different extraction times. Surface lipid recovery and the relative composition of individual constituents were impacted to varying degrees depending on solvent choice and duration of extraction. Analyses were performed using both silks and leaves to demonstrate the utility of the solvent- and time-optimized protocol in comparison to extraction with the commonly used chloroform solvent. Overall, the preferred solvent system was identified as hexanes:diethyl ether (90:10), based on its effectiveness in extracting surface hydrocarbons and fatty acids as well as its reduced propensity to extract presumed internal fatty acids. Metabolite profiling of wildtype and glossy1 seedlings, which are impaired in surface lipid biosynthesis, demonstrated the ability of the preferred solvent to extract extracellular surface lipids rich in amphipathic compounds (aldehydes and alcohols). In addition to the expected deficiencies in dotriacontanal and dotriacontan-1-ol for gl1 seedlings, an unexpected increase in fatty acid recovery was observed in gl1 seedlings extracted in chloroform, suggesting that chloro-form extracts lipids from internal tissues of gl1 seedlings. This highlights the importance of extraction method when evaluating mutants that have altered cuticular lipid compositions. Lastly, metabolite profiling of silks from maize inbreds B73 and Mo17, exposed to different environments and harvested at different ages, revealed differences in hydrocarbon and fatty acid composition, demonstrating the dynamic nature of surface lipid accumulation on silks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Neural Network-Based Electric Vehicle Range Prediction for Smart Charging Optimization

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. Here, the proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

An Integrated Approach To Offshore Wind Energy Assessment: Great Lakes 3D Wind Experiment

This grant supported fundamental research into the characterization of flow parameters of relevance to the wind energy industry focused on offshore and the coastal zone. A major focus of the project was application of the latest generation of remote sensing instrumentation and also integration of measurements and numerical modeling to optimize characterization of time-evolving atmospheric flow parameters in 3-D. Our research developed a new data-constrained Wind Atlas for the Great Lakes, and developed new insights into flow parameters in heterogeneous environments. Four experiments were conducted during the project: At a large operating onshore wind farm in May 2012; At the National Renewable Energy Laboratory National Wind Technology Center (NREL NWTC) during February 2013; At the shoreline of Lake Erie in May 2013; and At the Wind Energy Institute of Canada on Prince Edward Island in May 2015. The experiment we conducted in the coastal zone of Lake Erie indicated very complex flow fields and the frequent presence of upward momentum fluxes and resulting distortion of the wind speed profile at turbine relevant heights due to swells in the Great Lakes. Additionally, our data (and modeling) indicate the frequent presence of low level jets at 600 m height over the Lake and occasions when the wind speed profile across the rotor plane may be impacted by this phenomenon. Experimental data and modeling of the fourth experiment on Prince Edward Island showed that at 10-14 m escarpment adjacent to long-overseas fetch the zone of wind speed decrease before the terrain feature and the increase at (and slightly downwind of) the escarpment is ~3–5% at turbine hub-heights. Additionally, our measurements were used to improve methods to compute the uncertainty in lidar-derived flow properties and to optimize lidar-scanning strategies. For example, on the basis of the experimental data we collected plus those from one of our research partners we advanced a new methodology to estimate a priori the uncertainty in wind speed retrievals from arc scans based on site characteristics such as wind velocity, turbulence intensity and proposed scan geometry. Insights regarding use of remote sensing technologies deriving from project experiments were used to compile a best practice document http://doi.org/10.7298/X4QV3JGF for measuring wind speeds and turbulence offshore through in-situ and remote sensing technologies. A project-specific web-site was developed and is available at: http://www.geo.cornell.edu/eas/PeoplePlaces/Faculty/spryor/DoE_AIATOWEA/index.html

17 WIND ENERGY↗

DOE OSTI2018

Enhanced red luminescence quantum yield from Gd3+/Eu3+: CaLa2ZnO5 phosphor spheres for photonic applications

Highlights: • Ravishing red emission is observed from Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} phosphor spheres. • Red emission of Eu{sup 3+} was significantly enhanced by co-doping with Gd{sup 3+}. • Effective sensitization of Gd{sup 3+} ions and enhanced quantum yield have been elucidated. • Energy transfer from Gd{sup 3+} to Eu{sup 3+} was demonstrated by several fluorescence dynamics. • CIE coordinates are very close to National Television Standard Committee (NTSC). - Abstract: Novel bright red emission was obtained from co-doped Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} (CLZO) phosphor spheres. We have successfully synthesized co-doped Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres by citrate sol-gel method. Structural and morphological analysis was carried out by XRD and FE-SEM. EDS and XPS studies were employed to analyze the presence of elements and their ionic states respectively. The complex formation studies were carried out by the FTIR spectral study. The photoluminescence spectral profiles confirm the dazzling red emission has been observed at 626 nm ({sup 5}D{sub 0} → {sup 7}F{sub 2}) from the Eu{sup 3+}: CLZO phosphors under the excitation of 467 nm. Upon increasing the Eu{sup 3+} concentration, the photoluminescence performance has also increased remarkably. The optimized concentration of the Eu{sup 3+} was noticed at 12 mol%. Upon co-doping with Gd{sup 3+} ions to Eu{sup 3+}: CLZO phosphors, the red emission pertaining to Eu{sup 3+} was appreciably enhanced through energy transfer from Gd{sup 3+} to Eu{sup 3+} ions. The energy transfer mechanism was substantiated by several fluorescent methods such as lifetime decay dynamics and quantum efficiencies. Commission International de I’Echairage (CIE) 1931 chromaticity coordinates (x–y) calculated for singly doped and co-doped CLZO samples. The CIE coordinates were found to be (0.6644, 0.3355) and (0.6688, 0.3311) respectively, which were good consistent with the standard values of NTSC surprisingly. Correlated color temperature (CCT) value was evaluated for optimised co-doped sample and it was found to be between 2000–5000 K which could be more preferable red component for warm white light applications. The lifetime and quantum efficiency of the optimized co-doped sample have been evaluated as 1.102 ms and 69% respectively. The ravishing red emission of the Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres which suggests that these phosphor materials could be promising candidates for red luminescent optical devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2015

Seniority number description of potential energy surfaces: Symmetric dissociation of water, N{sub 2}, C{sub 2}, and Be{sub 2}

The present study further explores the concept of the seniority number (Ω) by examining different configuration interaction (CI) truncation strategies in generating compact wave functions in a systematic way. While the role of Ω in addressing static (strong) correlation problem has been addressed in numerous previous studies, the usefulness of seniority number in describing weak (dynamic) correlation has not been investigated in a systematic way. Thus, the overall objective in the present work is to investigate the role of Ω in addressing also dynamic electron correlation in addition to the static correlation. Two systematic CI truncation strategies are compared beyond minimal basis sets and full valence active spaces. One approach is based on the seniority number (defined as the total number of singly occupied orbitals in a determinant) and another is based on an excitation-level limitation. In addition, molecular orbitals are energy-optimized using multiconfigurational-self-consistent-field procedure for all these wave functions. The test cases include the symmetric dissociation of water (6-31G), N{sub 2} (6-31G), C{sub 2} (6-31G), and Be{sub 2} (cc-pVTZ). We find that the potential energy profile for H{sub 2}O dissociation can be reasonably well described using only the Ω = 0 sector of the CI wave function. For the Be{sub 2} case, we show that the full CI potential energy curve (cc-pVTZ) is almost exactly reproduced using either Ω-based (including configurations having up to Ω = 2 in the virtual-orbital-space) or excitation-based (up to single-plus-double-substitutions) selection methods, both out of a full-valence-reference function. Finally, in dissociation cases of N{sub 2} and C{sub 2}, we shall also consider novel hybrid wave functions obtained by a union of a set of CI configurations representing the full valence space and a set of CI configurations where seniority-number restriction is imposed for a complete set (full-valence-space and virtual) of correlated molecular orbitals, simultaneously. We discuss the usefulness of the seniority number concept in addressing both static and dynamic electron correlation problems along dissociation paths.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Design and application of ion-implanted polySi passivating contacts for interdigitated back contact c-Si solar cells

Ion-implanted passivating contacts based on poly-crystalline silicon (polySi) are enabled by tunneling oxide, optimized, and used to fabricate interdigitated back contact (IBC) solar cells. Both n-type (phosphorous doped) and p-type (boron doped) passivating contacts are fabricated by ion-implantation of intrinsic polySi layers deposited via low-pressure chemical vapor deposition and subsequently annealed. The impact of doping profile on the passivation quality of the polySi doped contacts is studied for both polarities. It was found that an excellent surface passivation could be obtained by confining as much as possible the implanted-and-activated dopants within the polySi layers. The doping profile in the polySi was controlled by modifying the polySi thickness, the energy and dose of ion-implantation, and the temperature and time of annealing. An implied open-circuit voltage of 721 mV for n-type and 692 mV for p-type passivating contacts was achieved. Besides the high passivating quality, the developed passivating contacts exhibit reasonable high conductivity (R{sub sh n-type} = 95 Ω/□ and R{sub sh p-type} = 120 Ω/□). An efficiency of 19.2% (V{sub oc} = 673 mV, J{sub sc} = 38.0 mA/cm{sup 2}, FF = 75.2%, and pseudo-FF = 83.2%) was achieved on a front-textured IBC solar cell with polySi passivating contacts as both back surface field and emitter. By improving the front-side passivation, a V{sub OC} of 696 mV was also measured.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Neural-Network-Enhanced COTSIM: Advancing Predictive Capabilities for Fast DIII-D Simulations

Sustaining fusion reactions in tokamaks requires heating plasma to thermonuclear temperatures while maintaining confinement and stability. Neutral beam injection (NBI) provides heating, current drive, torque, and fueling, while electron cyclotron (EC) waves are widely used for heating and current drive; together, these actuators shape the plasma current, temperature, and density profiles. The control-oriented tokamak simulator (COTSIM), a predictive, control-oriented code, has been enhanced with neural-network surrogates for transport and sources. Turbulent transport is predicted by MMMnet—a neural-network version of the updated multimode model (MMM 9.0.10)—with significantly reduced computation time relative to MMM; neoclassical transport follows the Chang–Hinton model. NUBEAMnet, a surrogate of the Monte Carlo NUBEAM module, predicts beam-driven heating, current, and torque. EC heating and current drive use a control-oriented, empirically scaled source model; plasma resistivity follows the Spitzer formulation; bootstrap current uses the Sauter model. Equilibrium is computed using both prescribed and fixed-boundary solvers (FBSs), and the pedestal structure is modeled with an empirical pedestal model. For a representative DIII-D discharge, COTSIM predicts electron and ion temperature and safety-factor profiles in close agreement with TRANSP predictive and interpretive simulations while extending predictions through the pedestal region to the plasma edge (versus 80% of the minor radius in TRANSP). Furthermore, the equivalent COTSIM simulation runs in under 3 min compared to about 2 h for TRANSP, enabling rapid scenario planning, optimization of tokamak operation, and between-pulse control design.

Control-oriented tokamak simulator (COTSIM)↗

DOE OSTI2021

Scalable Comparative Visualization of Ensembles of Call Graphs

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey , a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. Finally, we demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma: Preprint

Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.

deep direct-use↗

DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma

deep direct-use↗

DOE OSTI2019

Effect of oxygen stoichiometry on the magnetization profiles and negative magnetization in LSMO thin films

The depth-dependent magnetization in thin film oxygen stoichiometric and oxygen-deficient La 0.7 Sr 0.3 MnO 3 is investigated by using polarized neutron reflectivity and DC bulk magnetometry. The polarized neutron reflectivity results reveal that the stoichiometric sample shows enhanced interfacial magnetization relative to the rest of the film. The oxygen-deficient sample exhibits a reduced average magnetization from the optimized recipe. Both films show regions of suppressed magnetization at the surface regardless of the growth pressure. The oxygen stoichiometric film does not show an interfacial dead layer, whereas the oxygen-deficient film exhibits a dead layer whose thickness changes with temperature. At a low applied field, we observe striking differences in the depth dependence of the magnetic ordering, with the oxygen-deficient film exhibiting exchange spring behavior, while the stoichiometric film shows a constant magnetization direction across the film. Furthermore, these results suggest that the incorporation of oxygen vacancies during growth leads to an accumulation of vacancies at the interface, which is enhanced at higher temperature due to increased oxygen mobility, creating a region of reduced magnetism that couples to the rest of the film. These results offer insight into the complex behavior and role of oxygen vacancies in the magnetism of these systems. Additionally, the study reveals further details of the negative magnetization in La 0.7 Sr 0.3 MnO 3 reported in previous studies, which are discussed here.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways

Quantitative simulation of trivalent ƒ-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac 3+ –DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CN Ac,O w ≈ 1.7 arising from a dynamic equilibrium between one- and two-water states (37.5% and 59.9% of frames; three waters 2.5%). A 5 ns potential of mean force shows two low-lying basins at CN Ac,O w ≈ 1 and CN Ac,O w ≈ 2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac 3+ –DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.

Actinium↗

DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗