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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.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Computing Reaction Kinetics with MC-PDFT–OPESf: Combining Multireference Electronic Structure Theory and Enhanced Sampling

Accurate rate constants are crucial for understanding and optimizing catalytic reactions mediated by enzymes, metalloproteins, and heterogeneous catalysts. These systems frequently present a dual computational challenge. Multiconfigurational reaction sites require multireference techniques for the accurate treatment of the electronic structure, and high activation barriers prevent efficient sampling of unbiased reactive transitions. In this work, we combine multiconfiguration pair-density functional theory (MC-PDFT) as an accurate and efficient multireference electronic structure method with on-the-fly probability-enhanced sampling flooding (OPESf) as an enhanced sampling method capable of accelerating reactive transitions. We demonstrate the approach on the Diels–Alder [4+2] cycloaddition between cis-butadiene and ethene as a reaction characterized by a large activation barrier and multireference character. MC-PDFT–OPESf provides reaction rates in agreement with experiments at a fraction of the computational cost required by conventional unbiased ab initio calculations. Here, we propose MC-PDFT–OPESf as an efficient approach for computing kinetics in strongly correlated molecular systems.

Chemical calculations↗
DOE OSTI2019

The effects of active site and support on hydrogen elimination over transition-metal-functionalized yttria-decorated metal–organic frameworks

Hydrogen elimination from a metal alkyl complex and its reverse reaction are critical elementary steps in many catalytic cycles. In understanding such catalytic cycles, it is important to learn about the effect of the active site as well as effects beyond the active site, especially support effects. Here we employ density-functional-theory-based computational screening of transition-metal-functionalized yttria-decorated NU-1000 metal–organic frameworks to study the effect of metal sites and supports on the hydrogen elimination reactions. In this work, we consider six transition metals. The screening shows that a Nb-based catalyst has the lowest free energy of activation for both α-H elimination and β-H elimination, but for the latter we must also consider the free energy of release of the alkene. By the Sabatier principle, the optimal catalyst for β-H elimination is the one that interacts with molecules with intermediate strength for easy reactant activation as well as easy product desorption. By employing a volcano plot to find the optimum compromise, we identified V-functionalized, yttria-decorated NU-1000 as the most active catalyst among the trial candidates for the β-H elimination reaction. The analysis also reveals a support-specific charge transfer process in which the yttria and carboxylate linkers enable electron transfer between the transition-metal site and organic linkers of the NU-1000 simultaneously with a coordinated change of spin at the metal site and in the linker groups. Based on our analyses, we hypothesize that the observed charge transfer enables certain functional groups, such as propyl, to interact more strongly with the support and stabilize the catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ginkgo - A math library designed to accelerate Exascale Computing Project science applications

Large-scale simulations require efficient computation across the entire computing hierarchy. A challenge of the Exascale Computing Project (ECP) was to reconcile highly heterogeneous hardware with the myriad of applications that were required to run on these supercomputers. Mathematical software forms the backbone of almost all scientific applications, providing efficient abstractions and operations that are crucial to harness the performance of computing systems. Ginkgo is one such mathematical software library, nurtured by ECP, providing high-performance, user-friendly, and performance portable interfaces for applications in ECP and beyond. In this paper, we elaborate on Ginkgo’s philosophy of high-performance software that is sustainable, reproducible, and easy to use. We showcase the wide feature set of solvers and preconditioners available in Ginkgo and the central concepts involved in their design. We elaborate on four different ECP software integrations: MFEM, PeleLM + SUNDIALS, XGC, and ExaSGD that use Ginkgo to accelerate their science runs. Performance studies of different problems from these applications highlight the effectiveness of Ginkgo and the benefits incurred by these ECP applications.

Cojean, Terry↗
DOE OSTI2011

Computational estimation of rainbow trout estrogen receptor binding affinities for environmental estrogens

Environmental estrogens have been the subject of intense research due to their documented detrimental effects on the health of fish and wildlife and their potential to negatively impact humans. A complete understanding of how these compounds affect health is complicated because environmental estrogens are a structurally heterogeneous group of compounds. In this work, computational molecular dynamics simulations were utilized to predict the binding affinity of different compounds using rainbow trout (Oncorhynchus mykiss) estrogen receptors (ERs) as a model. Specifically, this study presents a comparison of the binding affinity of the natural ligand estradiol-17{beta} to the four rainbow trout ER isoforms with that of three known environmental estrogens 17{alpha}-ethinylestradiol, bisphenol A, and raloxifene. Two additional compounds, atrazine and testosterone, that are known to be very weak or non-binders to ERs were tested. The binding affinity of these compounds to the human ER{alpha} subtype is also included for comparison. The results of this study suggest that, when compared to estradiol-17{beta}, bisphenol A binds less strongly to all four receptors, 17{alpha}-ethinylestradiol binds more strongly, and raloxifene has a high affinity for the {alpha} subtype only. The results also show that atrazine and testosterone are weak or non-binders to the ERs. All of the results are in excellent qualitative agreement with the known in vivo estrogenicity of these compounds in the rainbow trout and other fishes. Computational estimation of binding affinities could be a valuable tool for predicting the impact of environmental estrogens in fish and other animals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Dimeric [Mo 2 S 12 ] 2− Cluster: A Molecular Analogue of MoS 2 Edges for Superior Hydrogen‐Evolution Electrocatalysis

Abstract Proton reduction is one of the most fundamental and important reactions in nature. MoS 2 edges have been identified as the active sites for hydrogen evolution reaction (HER) electrocatalysis. Designing molecular mimics of MoS 2 edge sites is an attractive strategy to understand the underlying catalytic mechanism of different edge sites and improve their activities. Herein we report a dimeric molecular analogue [Mo 2 S 12 ] 2− , as the smallest unit possessing both the terminal and bridging disulfide ligands. Our electrochemical tests show that [Mo 2 S 12 ] 2− is a superior heterogeneous HER catalyst under acidic conditions. Computations suggest that the bridging disulfide ligand of [Mo 2 S 12 ] 2− exhibits a hydrogen adsorption free energy near zero (−0.05 eV). This work helps shed light on the rational design of HER catalysts and biomimetics of hydrogen‐evolving enzymes.

Huang, Zhongjie↗
DOE OSTI2015

Dimeric [Mo₂S₁₂]²⁻ Cluster: A Molecular Analogue of MoS₂ Edges for Superior Hydrogen-Evolution Electrocatalysis

Proton reduction is one of the most fundamental and important reactions in nature. MoS2 edges have been identified as the active sites for hydrogen evolution reaction (HER) electrocatalysis. Designing molecular mimics of MoS2 edge sites is an attractive strategy to understand the underlying catalytic mechanism of different edge sites and improve their activities. Herein we report a dimeric molecular analogue [Mo₂S₁₂]²⁻, as the smallest unit possessing both the terminal and bridging disulfide ligands. Our electrochemical tests show that [Mo₂S₁₂]²⁻ is a superior heterogeneous HER catalyst under acidic conditions. Computations suggest that the bridging disulfide ligand of [Mo₂S₁₂]²⁻ exhibits a hydrogen adsorption free energy near zero (-0.05eV). This work helps shed light on the rational design of HER catalysts and biomimetics of hydrogen-evolving enzymes.

electrocatalysis↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2010

Progress on an integrated multi-physics simulation predictive capability for plasma chamber nuclear components

Understanding the behavior of a plasma chamber component in the fusion environment requires a simulation technique that is capable of integrating multi-disciplinary computational codes while appropriately treating geometric heterogeneity and complexity. Such a tool should be able to interpret phenomena from mutually dependent scientific disciplines and predict performance with sufficient accuracy and consistency. Integrated multi-physics simulation predictive capability (ISPC) relies upon advanced numerical simulation techniques and is being applied to ITER first wall/shield and Test Blanket Module (TBM) designs. In this paper, progress in ISPC development is described through the presentation of a number of integrated simulations. The simulations cover key physical phenomena encountered in a fusion plasma chamber system, including tritium permeation, fluid dynamics, and structure mechanics. Interface engines were developed in order to pass field data, such as surface deformation or nuclear heating rate, from the structural analysis to the thermo-fluid MHD analysis code for magnetohydrodynamic (MHD) velocity profile assessments, or from the neutronics analysis to the thermo-fluid analysis for temperature calculations, respectively. Near-term effort toward further ISPC development is discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Achieving geometric accuracy in FFT-based micromechanical models using conformal grid

Owing to its efficiency, simplicity and robustness, the FFT-based method has become the standard for computation of mechanical fields in a heterogeneous periodic unit cell. One of the main disadvantages of the FFT-based method is the inaccurate representation of the initial microstructure on a regular grid of voxels, which can be alleviated through the use of distorted initial grids. Here, in this paper, a method for generation of distorted initial grids conforming to the microstructural features (e.g. straight/curved boundaries) is proposed. The method determines the positions of the grid nodes in the initial configuration by solving a system of springs connecting the nodes. Microstructures consisting of layers, Voronoi tessellation and circular/spherical inclusions are considered, and mechanical fields simulated using the FFT-based method. It is found that distorted initial grids, conforming to the microstructural features, lead to more accurate mechanical fields in comparison to the corresponding non-distorted initial grid solution. The effect of initial grid distortion on the convergence of the FFT-based method is analyzed and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Prostate treatment with helical TomoTherapy in patients with bilateral hip prostheses—Two case studies

Prostate cancer patients with bilateral hip prostheses require additional planning and treatment considerations. Visualization of the organs at risk (OAR) and the planning target volume (PTV) is hindered by the large amount of artifact streaking between the metallic implants. The density of this area must be accounted for when using heterogeneity correction. Fusion of a megavolt computed tomography (MVCT) to the planning CT aids in viewing the prostate, bladder, and rectum, and implantation of 6 fiducial markers along the posterior border of the prostate helps with the delineation between it and the rectum. When planning, the amount of beam entry angles is limited because of the position of the prostheses and prevents the use of any lateral beams. Therefore, TomoTherapy presents an effective option for these patients because of the ability to use a directional block, which prevents any primary beams from entering through the bilateral hip prostheses (but the beams may exit through the structures), use of MVCT for daily localization, and delivery of radiation with intensity modulation allows for effective sparing of the OAR.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

Site-averaged kinetics for catalysts on amorphous supports: an importance learning algorithm

Ab initio calculations have greatly advanced our understanding of homogeneous catalysts and crystalline heterogeneous catalysts. In contrast, amorphous heterogeneous catalysts remain poorly understood. The principal difficulties include (i) the nature of the disorder is quenched and unknown; (ii) each active site has a different local environment and activity; (iii) active sites are rare, often less than ~20% of potential sites, depending on the catalyst and its preparation method. Few (if any) studies of amorphous heterogeneous catalysts have ever attempted to compute site-averaged kinetics, because the exponential dependence on variable activation energy requires an intractable number of ab initio calculations to converge. We present a new algorithm using machine learning techniques (metric learning kernel regression) and importance sampling to efficiently learn the distribution of activation energies. We demonstrate the algorithm by computing the site-averaged activity for a model amorphous catalyst with quenched disorder.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Equilibrium Core Model for Micro Pebble Bed Reactors Using OpenMC

Estimating the equilibrium state for pebble bed reactors (PBRs) presents complex challenges as it requires simultaneous consideration of changes in the pebbles’ movement as well as their fuel compositions. Whereas traditional approaches use multigroup diffusion codes for neutronics calculations of PBRs’ equilibrium state, the double-heterogeneity of PBRs complicates neutron cross-section generation. Continuous-energy Monte Carlo (MC) methods are better suited for detailed PBR analysis because of their natural handling of double-heterogeneity, but they demand substantially more computational resources. Here, this study introduces a novel method for efficiently estimating the equilibrium state in small and micro PBRs with reduced computational cost. The method is anticipated to accelerate the processes of core design and performing parametric studies for utilizing advanced fuel and structural materials. The HTR-10 reactor design was used for validating the method’s predictions and evaluating its computational efficiency. When compared to reference calculation values from the literature, criticality (k-effective) was predicted to be approximately within the margin of error of the MC transport calculation, average core power density (in megawatts per cubic meter) was predicted within 2.5% relative error, and maximum thermal flux (10 13 n/cm 2 .s −1 ) was predicted within 1.8% relative error. The calculated inventory of fission products and fuel composition in the equilibrium core were within 15% and 16.6%, respectively, when compared to reported values from the literature. The difference is attributed to variance in the considered values of the core temperature, which was found to significantly affect the depletion analyses.

Equilibrium core↗
DOE OSTI2018

Modeling I/O Performance Variability Using Conditional Variational Auto Encoders

Storage system performance modeling is crucial for efficient use of heterogeneous shared resources on leadership class computers. Variability in application performance, particularly variability arising from concurrent applications sharing I/O resources, is a major hurdle in the development of accurate performance models. We adopt a deep learning approach based on conditional variational auto encoders (CVAE) for I/O performance modeling, and use it to quantify performance variability. We illustrate our approach using the data collected on Edison, a production supercomputing system at the National Energy Research Scientific Computing Center (NERSC). The CVAE approach is investigated by comparing it to a previously proposed sensitivity-based Gaussian process (GP) model. We find that the CVAE model performs slightly better than the GP model in cases where training and testing data come from different applications, since CVAE can inherently leverage the whole data from multiple applications whereas GP partitions the data and builds separate models for each partition. Hence, the CVAE offers an alternative modeling approach that does not need preprocessing; it has enough flexibility to handle data from a wide variety of applications without changing the inference approach.

I/O perfornance variability↗
DOE OSTI2021

Evaluating the Performance of Integer Sum Reduction on an Intel GPU

Sum reduction is a primitive operation in parallel computing while SYCL is a promising heterogeneous programming language. In this paper, we describe the SYCL implementations of integer sum reduction using atomic functions, shared local memory, vectorized memory accesses, and parameterized workload sizes. Evaluating the reduction kernels shows that we can achieve 1.4X speedup over the open-source implementations of sum reduction for a sufficiently large number of integers on an Intel integrated GPU.

Jin, Zheming↗
DOE OSTI2020

Consistency and reproducibility in atomic layer deposition

Atomic layer deposition (ALD) is a thin film synthesis technique that can provide exquisite accuracy and precision in film thickness and composition even on complex, large area substrates. Based on self-limiting surface chemistry, ALD can be insensitive to process conditions and reactor designs, allowing an ALD process developed in one lab to be easily reproduced in other labs. In practice, however, ALD is sometimes difficult to reproduce or replicate, and the results can vary substantially between ALD reactors and between labs. This is exemplified by large deviations in reports on the growth of, e.g., Al2O3, FeOx, and TiO2 given the same precursors under similar conditions. Furthermore, the problem of irreproducibility seems to be growing as ALD is adopted by more researchers and integrated into new applications. In this article, the authors highlight some of the major sources of variations and errors and common misconceptions related to ALD. In particular, the authors focus on issues related to precursors, substrates, and deposition tools. The authors illustrate these problems through examples from the literature, and they present results from numerical simulations that describe how nonidealities would manifest in thickness profiles in a typical cross-flow reactor. They also describe how reproducibility in ALD is linked to consistent experimental practice and reporting between labs. The authors’ hope is that by educating newcomers to ALD and advocating for consistent reporting of deposition conditions, they can minimize irreproducibility and enable ALD practitioners to realize the full potential afforded by self-limiting surface chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys

Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. Furthermore, we demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.

density functional theory↗