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DOE OSTI2021

Microstructural Effects of High Dose Helium Implantation in ErD 2

Metal hydrides can store hydrogen isotopes with high volumetric density. In metal tritides, tritium beta decay can result in accumulation of helium within the solid, in some cases exceeding 10 at.% helium after only 4 years of aging. Helium is insoluble in most materials, but often does not readily escape, and instead coalesces to form nanoscale bubbles when helium concentrations are near 1 at.%. Blistering or spallation often occurs at higher concentrations. Radioactive particles shed during this process present a potential safety hazard. This study investigates the effects of high helium concentrations on erbium deuteride (ErD 2 ), a non-radioactive surrogate material for erbium tritide (ErT 2 ). To simulate tritium decay in the surrogate, high doses of 120 keV helium ions were implanted into ErD 2 films at room temperature. Scanning and transmission electron microscopy indicated spherical helium bubble formation at a critical concentration of 1.5 at.% and bubble linkage leading to nanoscale crack formation at a concentration of 7.5 at.%. Additionally, crack propagation occurred through the nanocrack region, resulting in spallation extending from the implantation peak to the surface. Electron energy loss spectroscopy was utilized to confirm the presence of high-pressure helium in the nanocracks, suggesting that helium gas plays a predominant role in deformation. This work improves the overall understanding of helium behavior in ErD 2 by using modern characterization techniques to determine: the critical helium concentration required for bubble formation, the material failure mechanism at high concentration, and the nanoscale mechanisms responsible for material failure in helium implanted ErD 2 .

36 MATERIALS SCIENCE↗

DOE OSTI2025

Effect of alloying on intrinsic ductility in WTaCrV high entropy alloys

Tungsten (W) exhibits desirable properties for extreme applications, such as the divertor in magnetic fusion reactors, but its practicality remains limited due to poor formability and insufficient irradiation resistance. In this work, we study the intrinsic ductility of body-centered cubic WTaCrV based high entropy alloys (HEAs), which are known to exhibit excellent irradiation resistance. The ductility evaluations are carried out using a criterion based on the competition between the critical stress intensity factors for emission (K Ie ) and cleavage (K Ic ) in the {110} slip planes and {110} crack planes, which are evaluated within the linear elastic fracture mechanics framework and computed using density functional theory. The results suggest that increasing the alloying concentrations of V and reducing the concentrations of W can significantly improve the ductility in these HEAs. The elastic anisotropy for these HEAs is analyzed using the Zener anisotropy ratio and its correlation with the concentration of W in the alloys is studied. Results indicate that these alloys tend to be fairly isotropic independently from the concentration of W in them. The computed data for the elastic constants of these HEAs is also compared against available experimental data. The results are in good agreement, validating the robustness and accuracy of the computational methods. Multiple phenomenological ductility metrics were also computed and analyzed against the analytical model. Some metrics, mainly the surrogate D parameter, show a good correlation with the Rice model. The potential of these empirical metrics to serve as surrogate screening models for optimizing the compositional space is also discussed.

Anisotropy↗

DOE OSTI2019

Verification of Reduced Order Modeling Based Uncertainty/Sensitivity Estimator (ROMUSE)

This article presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Design optimization methods for high-performance research reactor core design

Typical objectives of high-performance research reactors include neutron scattering, materials irradiation, and isotope production. The creation of a core that optimizes these multiple objectives often presents a multidimensional (i.e., in design space), multiobjective optimization problem. The developed systematic approach discussed herein draws on response surface methodology to validate and leverage a surrogate model to serve in place of high-fidelity computational analyses. Optimization and design analysis methods leverage this surrogate model to provide a flexible tool for generating optimized designs and understanding the impact of design decisions on desired metrics. In applications to High Flux Isotope Reactor (HFIR) low-enriched uranium (LEU) core designs, neutronic, isotopic evolution, and thermal hydraulic analyses are used to generate key performance and safety metrics for assessing the feasibility and fitness of given designs. Three optimized designs that determine different desired metrics and constraints (e.g., key metric weighting and fabrication constraints) are presented, providing potential design options that satisfy the requirements for HFIR’s conversion from high enriched uranium to LEU fuel.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors

This work exploits a connection between optimal transport theory and the physics of image propagation to yield a locally low-dimensional model of turbulence-corrupted imagery. Optimal transport produces an invertible, pixel-wise linear trajectories to approximate the globally nonlinear turbulence between a clean and turbulence corrupted image pair. We use the low-dimensional model to fit subsets of the optimal transport vector fields and stitch the local models into a surrogate for the global map to be used for image cleaning. Experiments are performed on laboratory generated data of beam propagation using different values of the Fried parameter (a scale measuring turbulence coherence) as well as a toy data set. The results suggest this is a fruitful direction, and first step, towards using multiple realizations of turbulence corrupted images to learn a blind surrogate for the optimal transport vector field for image cleaning.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Inference of reaction rate parameters based on summary statistics from experiments

Here, we present the results of an application of Bayesian inference and maximum entropy methods for the estimation of the joint probability density for the Arrhenius rate para meters of the rate coefficient of the H 2 /O 2 -mechanism chain branching reaction H + O 2 → OH + O. Available published data is in the form of summary statistics in terms of nominal values and error bars of the rate coefficient of this reaction at a number of temperature values obtained from shock-tube experiments. Our approach relies on generating data, in this case OH concentration profiles, consistent with the given summary statistics, using Approximate Bayesian Computation methods and a Markov Chain Monte Carlo procedure. The approach permits the forward propagation of parametric uncertainty through the computational model in a manner that is consistent with the published statistics. A consensus joint posterior on the parameters is obtained by pooling the posterior parameter densities given each consistent data set. To expedite this process, we construct efficient surrogates for the OH concentration using a combination of Pad'e and polynomial approximants. These surrogate models adequately represent forward model observables and their dependence on input parameters and are computationally efficient to allow their use in the Bayesian inference procedure. We also utilize Gauss-Hermite quadrature with Gaussian proposal probability density functions for moment computation resulting in orders of magnitude speedup in data likelihood evaluation. Despite the strong non-linearity in the model, the consistent data sets all res ult in nearly Gaussian conditional parameter probability density functions. The technique also accounts for nuisance parameters in the form of Arrhenius parameters of other rate coefficients with prescribed uncertainty. The resulting pooled parameter probability density function is propagated through stoichiometric hydrogen-air auto-ignition computations to illustrate the need to account for correlation among the Arrhenius rate parameters of one reaction and across rate parameters of different reactions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar

Model parameters are a major source of uncertainty in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the most sensitive parameters has the potential to improve solar irradiance predictions. Here, we adopted a multiobjective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error (NMAE) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California, in order to focus on parameter calibration under cloudy conditions with different aerosol loading. The results show that generalized linear model (GLM)-based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in NMAE reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of NMAE. Our investigation reveals, however, that optimizing TE14 has a limited impact on irradiance simulations under less-polluted conditions in the SGP.

14 SOLAR ENERGY↗

DOE OSTI2013

Predicting the oral uptake efficiency of chemicals in mammals: Combining the hydrophilic and lipophilic range

Environmental risk assessment requires models for estimating the bioaccumulation of untested compounds. So far, bioaccumulation models have focused on lipophilic compounds, and only a few have included hydrophilic compounds. Our aim was to extend an existing bioaccumulation model to estimate the oral uptake efficiency of pollutants in mammals for compounds over a wide K{sub ow} range with an emphasis on hydrophilic compounds, i.e. compounds in the lower K{sub ow} range. Usually, most models use octanol as a single surrogate for the membrane and thus neglect the bilayer structure of the membrane. However, compounds with polar groups can have different affinities for the different membrane regions. Therefore, an existing bioaccumulation model was extended by dividing the diffusion resistance through the membrane into an outer and inner membrane resistance, where the solvents octanol and heptane were used as surrogates for these membrane regions, respectively. The model was calibrated with uptake efficiencies of environmental pollutants measured in different mammals during feeding studies combined with human oral uptake efficiencies of pharmaceuticals. The new model estimated the uptake efficiency of neutral (RMSE = 14.6) and dissociating (RMSE = 19.5) compounds with logK{sub ow} ranging from − 10 to + 8. The inclusion of the K{sub hw} improved uptake estimation for 33% of the hydrophilic compounds (logK{sub ow} < 0) (r{sup 2} = 0.51, RMSE = 22.8) compared with the model based on K{sub ow} only (r{sup 2} = 0.05, RMSE = 34.9), while hydrophobic compounds (logK{sub ow} > 0) were estimated equally by both model versions with RMSE = 15.2 (K{sub ow} and K{sub hw}) and RMSE = 15.7 (K{sub ow} only). The model can be used to estimate the oral uptake efficiency for both hydrophilic and hydrophobic compounds. -- Highlights: ► A mechanistic model was developed to estimate oral uptake efficiency. ► Model covers wide logK{sub ow} range (- 10 to + 8) and several mammalian species. ► K{sub ow} and the heptane water partition coefficient K{sub hw} were combined. ► K{sub ow} and K{sub hw} reflect the inner and the outer membrane diffusion resistance. ► Combining K{sub ow} and K{sub hw} improved uptake estimation for hydrophilic compounds.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

Design of top covers supporting aerobic in situ stabilization of old landfills - An experimental simulation in lysimeters

Highlights: Black-Right-Pointing-Pointer Tested engineered covers as surrogate to gas extraction during and after in situ aeration. Black-Right-Pointing-Pointer Examined how covers influence gas emissions, water balance and leachate generation. Black-Right-Pointing-Pointer Investigated effect of top covers on air-distribution in waste mass during aeration. Black-Right-Pointing-Pointer We suggest criteria and cover design to meet the demands during and after aeration. Black-Right-Pointing-Pointer Such cover systems may offer greenhouse gas emission reduction also after active aeration. - Abstract: Landfill aeration by means of low pressure air injection is a promising tool to reduce long term emissions from organic waste fractions through accelerated biological stabilization. Top covers that enhance methane oxidation could provide a simple and economic way to mitigate residual greenhouse gas emissions from in situ aerated landfills, and may replace off-gas extraction and treatment, particularly at smaller and older sites. In this respect the installation of a landfill cover system adjusted to the forced-aerated landfill body is of great significance. Investigations into large scale lysimeters (2 Multiplication-Sign 2 Multiplication-Sign 3 m) under field conditions have been carried out using different top covers including compost materials and natural soils as a surrogate to gas extraction during active low pressure aeration. In the present study, the emission behaviour as well as the water balance performance of the lysimeters has been investigated, both prior to and during the first months of in situ aeration. Results reveal that mature sewage sludge compost (SSC) placed in one lysimeter exhibits in principle optimal ambient conditions for methanotrophic bacteria to enhance methane oxidation. Under laboratory conditions the mature compost mitigated CH{sub 4} loadings up to 300 l CH{sub 4}/m{sup 2} d. In addition, the compost material provided high air permeability even at 100% water holding capacity (WHC). In contrast, the more cohesive, mineral soil cover was expected to cause a notably uniform distribution of the injected air within the waste layer. Laboratory results also revealed sufficient air permeability of the soil materials (TS-F and SS-Z) placed in lysimeter C. However, at higher compaction density SS-Z became impermeable at 100% WHC. Methane emissions from the reference lysimeter with the smaller substrate cover (12-52 g CH{sub 4}/m{sup 2} d) were significantly higher than fluxes from the other lysimeters (0-19 g CH{sub 4}/m{sup 2} d) during in situ aeration. Regarding water balance, lysimeters covered with compost and compost-sand mixture, showed the lowest leachate rate (18-26% of the precipitation) due to the high water holding capacity and more favourable plant growth conditions compared to the lysimeters with mineral, more cohesive, soil covers (27-45% of the precipitation). On the basis of these results, the authors suggest a layered top cover system using both compost material as well as mineral soil in order to support active low-pressure aeration. Conventional soil materials with lower permeability may be used on top of the landfill body for a more uniform aeration of the waste due to an increased resistance to vertical gas flow. A compost cover may be built on top of the soil cover underlain by a gas distribution layer to improve methane oxidation rates and minimise water infiltration. By planting vegetation with a high transpiration rate, the leachate amount emanating from the landfill could be further minimised. The suggested design may be particularly suitable in combination with intermittent in situ aeration, in the later stage of an aeration measure, or at very small sites and shallow deposits. The top cover system could further regulate water infiltration into the landfill and mitigate residual CH{sub 4} emissions, even beyond the time of active aeration.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive

Three machine learning techniques (multilayer perceptron, random forest and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modelling and real-time control applications. The machine learning models use a database of more than 16 000 GENRAY/CQL3D simulations for training, validation and testing. Latin hypercube sampling methods ensure that the database covers the range of nine input parameters ( $n_{e0}$ , $T_{e0}$ , $I_p$ , $B_t$ , $R_0$ , $n_{\|}$ , $Z_{{\rm eff}}$ , $V_{{\rm loop}}$ and $P_{{\rm LHCD}}$ ) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to $\sim$ ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of >200 000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or particularly low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important geometric feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find that many previously published proxies do correlate well with both the heat flux and stability boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Low-Temperature Electrochemical Upgrading of Bio-oils Using Polymer Electrolyte Membranes

While bio-oil-derived fuels hold much promise as a replacement for petroleum, transformation of the highly oxygenated mixture has proven challenging. In particular, bio-oils are reactive and difficult to upgrade through catalytic pyrolysis. To reach a stabilized product capable of deep deoxygenation at elevated pressure and temperature, conversion or separation of reactive groups is required. This paper describes an electrochemical process for stabilization and upgrading of bio-oils prior to hydrotreating at high pressure and temperature. This electrolytic process uses a three-compartment cell designed to hydrogenate reactive carbonyl components while separating small acid molecules, such as acetic and formic acids, which act as catalysts for condensation reactions and consume hydrogen gas to produce low-value gases in hydrotreating. To avoid conductivity issues, electrodes are appended to anion- and cation-exchange membranes. The cell was tested using a mixed acetic acid and formic acid surrogate fed to the cathode compartment, where the decrease in the concentration followed the applied charge to the cell. Experiments performed using pine pyrolysis oil demonstrated a significant reduction in the total acid number (TAN), an increase in pH from 2.6 to over 4, and a modest reduction of the carbonyl concentration. Analysis showed the reduction in TAN was primarily due to removal of carboxylic compounds. Experiments observed a decrease in the reactive carbonyl (aldehydes and ketones) concentration that followed applied charge. The results with the newly devised reactor show promise for the electrochemical route for upgrading bio-oils, but significant improvements in TAN removal and carbonyl conversion are needed. Given the distributed nature of biomass, an electrochemical process paired with pyrolysis could be used to densify and stabilize an oil product near the source. The densified liquid could then be shipped to centralized refineries for final upgrading to fuel and/or chemical products.

Lister, Tedd↗

DOE OSTI2020

Evaluation of a New Chemical Mechanism for 2-Amino-2-methyl-1-propanol in a Reactive Environment from CSIRO Smog Chamber Experiments

Amines are considered as an emerging class of atmospheric pollutants that are of great importance to atmospheric chemistry and new particle formation. As a typical amine, 2-amino-2-methyl-1-propanol (AMP) is one of the proposed solvents for capturing CO 2 from flue gas streams in amine-based post-combustion CO 2 capture plants, and it is expected to result in AMP emission and secondary products formation in the atmosphere. However, the current knowledge of its atmospheric chemistry and kinetics is poorly understood, particularly in a reactive environment. As such, in this work we used the CSIRO smog chamber to study the photo-oxidation of AMP in the presence of VOCs–NOx surrogate mixtures over a range of initial amine concentrations. O 3 formation was significantly inhibited when AMP was added to the surrogate VOCs–NOx mixtures, implying that AMP could alter known atmospheric chemical reaction pathways and the prevailing reactivity. Simultaneously, a large amount of AMP-derived secondary aerosol was formed, with a considerably high aerosol mass yield (i.e. ratio of aerosol formed to reacted AMP) of 1.06±0.20. Based on updated knowledge of its kinetics, oxidation pathways and product yields, we have developed a new mechanism (designated as CSIAMP-19) and integrated into the Carbon Bond 6 (CB6) chemical mechanism, and evaluated it against available smog chamber data. Compared with the existing AMP mechanism (designated as CarterAMP-08), the modified CB6 with CSIAMP-19 mechanism improves prediction against AMP–VOCs–NOx experiments across a range of initial AMP concentrations, within ±10% model error for gross ozone production. Our results contribute to scientific understanding of AMP photochemistry and to the development of the chemical mechanism of other amines. The updated AMP chemical reactions scheme can be further embedded into the chemical transport model for regional modelling scenarios where AMP-related emissions are of concern.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Plutonium and Cerium Perrhenate/Pertechnetate Coordination Polymers and Frameworks

Spent nuclear fuel (SNF) contains transuranic and lanthanide species, which are sometimes recovered and repurposed. One particularly problematic fission product, 99 TcO 4 – , hampers this recovery via coextraction with high valence metals, perhaps by complexation during aqueous reprocessing of SNF. There is limited molecular-level knowledge concerning the coordination chemistry between TcO 4 – or its well-known surrogate ReO 4 – and transuranic/lanthanide species. In the current study, we investigated the coordination of ReO 4 – /TcO 4 – with plutonium and cerium cations by structural and chemical characterization of a series of isolated extended solids. In this study, Ce represents both trivalent lanthanides and is considered a surrogate for Pu, respectively, in its common trivalent and tetravalent oxidation states. The structural elucidation of the seven isolated crystalline solids revealed that ReO 4 – /TcO 4 – directly connects to Pu IV , Pu VI O 2 2 + , Ce III , and Ce IV in the terminal and bridging coordination modes, leading to 1-, 2-, and 3-dimensional frameworks. For example, ReO 4 – coordination to Pu(IV) formed a 1D chain or 2D framework, isostructural with previously isolated Th(IV) compounds. However, Pu VI O 2 2 + alternating with ReO 4 – led to a unique 1D chain, different from the prior-reported U(VI)/Np(VI)-ReO 4 – /TcO 4 – structures. Coordination of ReO 4 – /TcO 4 – with Ce(III) promotes the assembly of 3D frameworks. Finally, attempted synthesis of a Ce(IV)-ReO 4 – compound resulted in a 2D framework with a mixed-valence Ce III/IV . The highly acidic reaction conditions supported the reduction of both Ce IV and Tc VII , challenging isolation of compounds featuring these species. Only one TcO 4 -containing structure was obtained in this study (Ce III –TcO 4 3D framework), vs the six total Ce/Pu-ReO 4 compounds. Furthermore, our three Pu-ReO 4 crystal structures are the first reported and translated to atomic-level information about Pu-TcO 4 coordination in nuclear fuel reprocessing scenarios, in addition to broadening our knowledge of bonding trends in the early, high-valence actinides.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2016

Hexavalent Americium Recovery Using Copper(III) Periodate

Separation of americium from the lanthanides is considered one of the most difficult separation steps in closing the nuclear fuel cycle. One approach to this separation could involve oxidizing americium to the hexavalent state to form a linear dioxo cation while the lanthanides remain as trivalent ions. This work considers aqueous soluble Cu3+ periodate as an oxidant under molar nitric acid conditions to separate hexavalent Am with diamyl amylphosphonate (DAAP) in n-dodecane. Initial studies assessed the kinetics of Cu3+ periodate auto-reduction in acidic media to aid in development of the solvent extraction system. Following characterization of the Cu3+ periodate oxidant, solvent extraction studies optimized the recovery of Am from varied nitric acid media and in the presence of other fission product, or fission product surrogate, species. Short aqueous/organic contact times encouraged successful recovery of Am (distribution values as high as 2) from nitric acid media in the absence of redox active fission products. In the presence of a post-PUREX simulant aqueous feed, precipitation of tetravalent species (Ce, Ru, Zr) occurred and the distribution values of 241Am were suppressed, suggesting some oxidizing capacity of the Cu3+ periodate is significantly consumed by other redox active metals in the simulant. The manuscript demonstrates Cu3+ periodate as a potentially viable oxidant for Am oxidation and recovery and notes the consumption of oxidizing capacity observed in the presence of the post-PUREX simulant feed will need to be addressed for any approach seeking to oxidize Am for separations relevant to the nuclear fuel cycle.

McCann, Kevin↗

DOE OSTI2019

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers

Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties—are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. Here, the burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature T g as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure–property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured T g values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified T g . While the present contribution is focused specifically on T g , the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Force-Free Identification of Minimum-Energy Pathways and Transition States for Stochastic Electronic Structure Theories

Here, the accurate mapping of potential energy surfaces (PESs) is crucial to our understanding of the numerous physical and chemical processes mediated by atomic rearrangements, such as conformational changes and chemical reactions, and the thermodynamic and kinetic feasibility of these processes. Stochastic electronic structure theories, e.g., Quantum Monte Carlo (QMC) methods, enable highly accurate total energy calculations that in principle can be used to construct the PES. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method, previously developed for QMC structural optimization, to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and at saddle points, we show that it is possible to identify MEPs and TSs by using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia (NH 3 ) molecule and the nucleophilic substitution (S N 2) reaction F – + CH 3 F → FCH 3 + F – . We validate our results using Density Functional Theory (DFT)- and Coupled Cluster (CCSD, CCSD(T))-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities, free energy differences, rate constants, and equilibrium constants that incorporates stochastically optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations at significantly reduced computational expense.

Iyer, Gopal R.↗