Chemically Controllable Magnetic Transition Temperature and Magneto-Elastic Coupling in MnZnSb Compo
Explore the source record for details and available documents.
Murgatroyd, Philip A.E.↗
SEARCH · Engineering Papers
Results for “computing methods”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Resolving the Heat Generated from ZrO 2 Atomic Layer Deposition Surface Reactions
Abstract In situ pyroelectric calorimetry and spectroscopic ellipsometry were used to investigate surface reactions in atomic layer deposition (ALD) of zirconium oxide (ZrO 2 ). Calibrated and time‐resolved in situ ALD calorimetry provides new insights into the thermodynamics and kinetics of saturating surface reactions for tetrakis(dimethylamino)zirconium(IV) (TDMAZr) and water. The net ALD reaction heat ranged from 0.197 mJ cm −2 at 76 °C to 0.155 mJ cm −2 at 158 °C, corresponding to an average of 4.0 eV/Zr at all temperatures. A temperature dependence for reaction kinetics was not resolved over the range investigated. The temperature dependence of net reaction heat and distribution among metalorganic and oxygen source exposure is attributed to factors including growth rate, equilibrium surface hydroxylation, and the extent of the reaction. ZrO 2 ‐forming surface reactions were investigated computationally using DFT methods to better understand the influence of surface hydration on reaction thermodynamics.
Bielinski, Ashley R.↗
Resolving the Heat Generated from ZrO 2 Atomic Layer Deposition Surface Reactions
Abstract In situ pyroelectric calorimetry and spectroscopic ellipsometry were used to investigate surface reactions in atomic layer deposition (ALD) of zirconium oxide (ZrO 2 ). Calibrated and time‐resolved in situ ALD calorimetry provides new insights into the thermodynamics and kinetics of saturating surface reactions for tetrakis(dimethylamino)zirconium(IV) (TDMAZr) and water. The net ALD reaction heat ranged from 0.197 mJ cm −2 at 76 °C to 0.155 mJ cm −2 at 158 °C, corresponding to an average of 4.0 eV/Zr at all temperatures. A temperature dependence for reaction kinetics was not resolved over the range investigated. The temperature dependence of net reaction heat and distribution among metalorganic and oxygen source exposure is attributed to factors including growth rate, equilibrium surface hydroxylation, and the extent of the reaction. ZrO 2 ‐forming surface reactions were investigated computationally using DFT methods to better understand the influence of surface hydration on reaction thermodynamics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Machine Learning‐Augmented Molecular Dynamics Simulations (MD) Reveal Insights Into the Disconnect Between Affinity and Activation of ZTP Riboswitch Ligands
Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.
Chemistry↗
Reactivity of Yellow Arsenic towards Cyclic (Alkyl)(Amino) Carbenes (CAACs)
Different cyclic (alkyl)(amino)carbenes (CAACs) were reacted with yellow arsenic. Several products [(CAAC-n) 2 (μ,η 1:1 -As 2 )] (n=1 (1), 4 (2)), [(CAAC-2) 3 (μ 3 ,η 1:1:1 -As 4 )] (3) and [(CAAC-3) 4 (μ 4 ,η 1:1:1:1 - As 8 )] (6) were isolated due to the differing steric properties of CAAC-1-4. The products contain As 2 , As 4 or As 8 units and represent the first examples of CAACs-substituted products of yellow arsenic. The reactivity of As 4 was compared with the reactivities of P 4 and the interpnictogen compound AsP 3 , which led to a series of phosphorus-containing derivatives such as ([(CAAC-3) 3 (μ 3 ,η 1:1:1 -P 4 )] (4) and [(CAAC-3) 4 (μ 4 ,η 1:1:1:1 -P 8 )] (7)) and [(CAAC-3) 3 (μ 3 ,η 1:1:1 -AsP 3 )] (5). The products were characterized by spectroscopic and crystallographic methods and DFT computations were performed to clarify their formation pathway.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Flexible nonstationary spatiotemporal modeling of high-frequency monitoring data
Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of half-spectral covariance functions to the case of nonstationarity in space and time and demonstrate that this method provides a natural and tractable way to incorporate complex behaviors into a covariance model. Further, we use this method with fully time-domain computations to obtain bona fide maximum likelihood estimators—as opposed to using Whittle-type likelihood approximations, for example—that can still be computed conveniently. Additionally, we apply this method to very high-frequency Doppler LIDAR vertical wind velocity measurements, demonstrating that the model can expressively capture the extreme nonstationarity of dynamics above and below the atmospheric boundary layer and, more importantly, the interaction of the process dynamics across it.
54 ENVIRONMENTAL SCIENCES↗
Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes
Abstract Predicting the structure of multi‐protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics‐inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end‐to‐end learning from molecular structures containing tens of thousands of atoms: a point‐based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.
Eismann, Stephan↗
Structure constants in $ \mathcal{N} $ = 4 SYM at finite coupling as worldsheet g-function
We develop a novel nonperturbative approach to a class of three-point functions in planar $ \mathcal{N} $ N = 4 SYM based on Thermodynamic Bethe Ansatz (TBA). More specifically, we study three-point functions of a non-BPS single-trace operator and two determinant operators dual to maximal Giant Graviton D-branes in AdS 5 × S 5 . They correspond to disk one-point functions on the worldsheet and admit a simpler and more powerful integrability description than the standard single-trace three-point functions. We first introduce two new methods to efficiently compute such correlators at weak coupling; one based on large N collective fields, which provides an example of open-closed-open duality discussed by Gopakumar, and the other based on combinatorics. The results so obtained exhibit a simple determinant structure and indicate that the correlator can be interpreted as a generalization of g -functions in 2d QFT; an overlap between an integrable boundary state and a state corresponding to the single-trace operator. We then determine the boundary state at finite coupling using the symmetry, the crossing equation and the boundary Yang-Baxter equation. With the resulting boundary state, we derive the ground-state g -function based on TBA and conjecture its generalization to other states. This is the first fully nonperturbative proposal for the structure constants of operators of finite length. The results are tested extensively at weak and strong couplings. Finally, we point out that determinant operators can provide better probes of sub-AdS locality than single-trace operators and discuss possible applications.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Analytic reconstruction with massive particles: one-loop amplitudes for $0\to \overline{q} qt\overline{t}H$
We present an analytic reconstruction of one-loop amplitudes for the process $0\to \overline{q} qt\overline{t}H$. Our calculation is a novel use of analytic reconstruction, retaining explicit covariance in the massive spin states through the massive spinor-helicity formalism. The analytic reconstruction relies on embedding the massive five-point kinematics in a fully massless eight-point phase space while still building a minimal ansatz directly in the five-point phase space. In order to obtain compact analytic expressions it is necessary to identify suitable partial fraction decompositions and extract common numerator factors, which we achieve through careful inspection of limits in which pairs of denominators vanish. We find that the resulting amplitudes are more numerically efficient than ones computed using automatic methods but that the gains are not as significant as in the massless case, at least at present. The method opens the door to applications at two-loop order, where numerical efficiency and improvements in the reconstruction methodology are more crucial, especially with regards to the number of free parameters in the ansatz.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
$\mathrm{BPS}$ coherent states and localization
We introduce coherent states averaged over a gauge group action to study correlators of half BPS states in $\mathcal{N}$= 4 SYM theory. The overlaps of these averaged coherent states are a generating function of correlators and can be written in terms of the Harish-Chandra-Itzykzon-Zuber (HCIZ) integral. We show that this formula immediately leads to a computation of the normalization of two point functions in terms of characters obtained originally in the work of Corley, Jevicki and Ramgoolam. We also find various generalizations for A n–1 quivers that follow directly from other solvable integrals over unitary groups. All of these can be computed using localization methods. When we promote the parameters of the generating function to collective coordinates, there is a dominant saddle that controls the effective action of these coherent states in the regime where they describe single AdS giant gravitons. We also discuss how to add open strings to this formulation. These will produce calculations that rely on correlators of matrix components of unitaries in the ensemble that is determined by the HCIZ integral to determine anomalous dimensions. We also discuss how sphere giants arise from Grassman integrals, how one gets a dominant saddle and how open strings are added in that case. The fact that there is a dominant saddle helps to understand how a 1/N expansion arises for open strings. We generalize the coherent state idea to study 1/4 and 1/8 BPS states as more general integrals over unitary groups.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Projection-based multifidelity linear regression for data-scarce applications
Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations with limited, costly high-fidelity evaluations. We introduce two projection-based multifidelity linear regression approaches with linear and nonlinear features that leverage principal component basis vectors for dimensionality reduction and combine multifidelity data through: (i) a direct data augmentation using low-fidelity data, and (ii) a data augmentation incorporating explicit linear corrections between low-fidelity and high-fidelity data. The data augmentation approaches combine high-fidelity and low-fidelity data into a unified training set and train the linear regression model through weighted least squares with fidelity-specific weights. We introduce a proximity-based weighting scheme with automatic weight selection strategy through cross-validation. Here, the proposed multifidelity linear regression methods are demonstrated on approximating the surface pressure field of a hypersonic vehicle in flight and the temperature field on an aircraft disc braking system. In an ultra low-data regime of no more than twelve high-fidelity samples, multifidelity linear regression achieves approximately 2% – 12% improvement in median accuracy and a higher R 2 score relative to single-fidelity methods at comparable computational cost.
data augmentation↗
Grain boundary segregation in BCC vanadium-based alloys: Quantum-accurate computed segregation spectra and targeted experimental validations
Grain boundaries are critically important to the material performance of fusion reactor materials such as vanadium, particularly mechanical properties and irradiation resistance. A key challenge to the design and control of grain boundaries in vanadium alloys is the lack of quantitative data on grain boundary segregation. In this study, we combine computational and experimental methods to address this gap. Furthermore, using a machine learning-accelerated quantum mechanics/molecular mechanics approach, we calculated the segregation spectra for 28 transition metal elements in polycrystalline vanadium, and validated these predictions experimentally for a subset of solutes that sample a range of segregation behavior, specifically zirconium, titanium, and tungsten, using analytical transmission electron microscopy. Furthermore, the agreement between experiment and theory highlights the predictive capability of our approach. Critically, this work provides a comprehensive database of quantum-accurate solute segregation enthalpies in vanadium, enabling the development of advanced alloys for fusion reactors applications.
Fusion materials↗
Thermal hydraulic characteristics of liquid sodium and supercritical CO 2 in a diffusion bonded heat exchanger
Compact heat exchangers are being examined for use in advanced, high-temperature nuclear or concentrated solar power plants to improve thermal efficiencies and reduce capital costs. At the time of this work, no experimental investigation of liquid sodium in the popular diffusion-bonded heat exchanger design had been performed. Herein this paper describes the thermal-hydraulic and overall operations performance observed during testing of a 316/L stainless steel “zig-zag” microchannel heat exchanger operating between a (0.5 kg/s) sodium loop and a (0.3 kg/s) recuperated supercritical CO 2 system with outlet conditions up to 550 °C at 16 MPa. Observed pressure drop is presented in the form of an effective friction factor correlation which aligns closely with recent experimental data. The thermal performance of this heat exchanger, measured by its overall conductance under various flow conditions, was decomposed to examine the independent convection behaviors of both the CO 2 and sodium streams. These results are presented in the form of Nusselt correlations fitted to the experimental data. The sodium heat transfer correlation aligns closely with the widely accepted Lubarsky-Kaufman correlation, while the CO 2 heat transfer correlation lies between previous correlations developed by computational and experimental methods. This was the first long-duration use of sodium in a microchannels (~1.8 mm hydraulic diameter) heat exchanger. The use of an oxide control system enabled trouble-free operation of the heat exchanger for over 150 h of high-temperature operation. This first-of-a-kind demonstration using liquid sodium and supercritical carbon dioxide shows great promise for further application of this technology in advanced nuclear or solar power plants.
42 ENGINEERING↗
Design and evaluation of a dilute flow particle-to-air heat exchanger for energy storage applications
The use of inert and redox-active particles for high-temperature energy storage requires the development of components that can efficiently transfer energy to high-pressure working fluids like supercritical carbon dioxide (sCO 2 ). Dilute flow reactors can enable high working fluid outlet temperatures and minimal parasitic losses compared to moving packed bed and fluidized bed reactors. This research uses both computational and experimental methods to explore the design trade-offs and practical challenges of a novel component for transferring energy from dilute flows of hot, reduced metal oxide (MO x ) particles to sCO 2 in tubes. A discretized thermal resistance network model, which accounts for particle hydrodynamics, multi-mode heat transfer, and reaction equilibrium, guides the design of a prototype device. This device is experimentally tested with a surrogate heat transfer fluids and inert particle temperatures up to 400°C and a heat duty exceeding 1 kW. The data are used to validate the thermal hydraulic sub-models, allowing for the simulation of reacting particle scenarios. Under nominal design conditions, the flow rate of reactive particles is predicted to be 30% lower than that of inert particles for the same energy recovered, with over 70% of the stored particle energy transferred to the sCO 2 . Furthermore, these findings can inform the design of more efficient energy recovery reactors for particle-based systems and can be integrated into system-level concentrated solar power models with thermal storage to optimize operating conditions.
14 SOLAR ENERGY↗
Optimizing the design and operation of water networks: Two decomposition approaches
We consider the design and operation of water networks simultaneously. Water network problems can be divided into two categories: the design problem and the operation problem. The design problem involves determining the appropriate pipe sizing and placements of pump stations, while the operation problem involves scheduling pump stations over multiple time periods to account for changes in supply and demand. Our focus is on networks that involve water co-produced with oil and gas. While solving the optimization formulation for such networks, we found that obtaining a primal (feasible) solution is more challenging than obtaining dual bounds using off-the-shelf mixed-integer nonlinear programming solvers. Therefore, we propose two methods to obtain good primal solutions. One method involves a decomposition framework that utilizes a convex reformulation, while the other is based on time decomposition. To test our proposed methods, we conduct computational experiments on a network derived from the PARETO case study.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An in-situ view cell system for investigating swelling behavior of elastomers upon high-pressure hydrogen exposure
The transition to hydrogen as a clean and efficient energy carrier is impeded by challenges in the compatibility of hydrogen with materials used within hydrogen infrastructure. Elastomers, crucial in sealing components, often exhibit premature failures in high-pressure hydrogen environments due to excessive swelling. This study employs an innovative in-situ view cell system to assess the swelling behavior of hydrogenated nitrile butadiene rubber (HNBR) under various hydrogen conditions. The system, designed to withstand pressures up to 96.5 MPa, incorporates Digital Image Correlation (DIC) for strain measurements and volume estimation. Results reveal non-linear volume increases during depressurization, challenging conventional assumptions. Furthermore, investigations into peak hydrogen pressures and pressure-holding scenarios during decompression highlight complex swelling trends. The introduction of a novel computer vision (CV) method enhances precision in volume estimation, overcoming DIC limitations. The study provides insights into mitigating elastomer swelling, crucial for developing robust materials to support future hydrogen-driven energy systems.
Elastomer↗
Neutron diffraction discriminates between models for the nanoarchitecture of graphene sheets in glassy carbon
Glassy carbon is a chemically inert, biocompatible, disordered material with the graphene sheet as its basic building block. Structural characterisation techniques have so far been unable to provide definitive distinction between the many proposed models for its structure. Computer based simulation methods have made a step forward by predicting structure from interaction potentials, but the results are sensitive to the choice of the potential. Here we use the white neutron beam of the Spallation Neutron Source at Oak Ridge National Laboratory coupled with the SNAP time-of-flight diffractometer to sample the reciprocal space of glassy carbon and calculate accurate radial distribution functions. From the radial distribution information, we determine graphene sheet dimensions, registration, curvature, and assess defect content to rank the proposed structures in terms of their agreement with experimental data. In this work, we find that the recent models derived from empirical potentials give the best agreement.
36 MATERIALS SCIENCE↗
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↗