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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

FAST: A multi-processed environment for visualization of computational fluid

Three dimensional, unsteady, multizoned fluid dynamics simulations over full scale aircraft is typical of problems being computed at NASA-Ames on CRAY2 and CRAY-YMP supercomputers. With multiple processor workstations available in the 10 to 30 Mflop range, it is felt that these new developments in scientific computing warrant a new approach to the design and implementation of analysis tools. These large, more complex problems create a need for new visualization techniques not possible with the existing software or systems available as of this time. These visualization techniques will change as the supercomputing environment, and hence the scientific methods used, evolve ever further. Visualization of computational aerodynamics require flexible, extensible, and adaptable software tools for performing analysis tasks. FAST (Flow Analysis Software Toolkit), an implementation of a software system for fluid mechanics analysis that is based on this approach is discussed.

Bancroft, Gordon V.↗
DOE OSTI2016

Anharmonicity and confinement in zeolites: Structure, spectroscopy, and adsorption free energy of ethanol in H-ZSM-5

Here, to account for thermal and entropic effects caused by the dynamics of the motion of the reaction intermediates, ethanol adsorption on the Brønsted acid site of the H-ZSM-5 catalyst has been studied at different temperatures and ethanol loadings using ab initio molecular dynamics (AIMD) simulations, infrared (IR) spectroscopy and calorimetric measurements. At low temperatures (T ≤ 400 K) and ethanol loading, a single ethanol molecule adsorbed in H-ZSM-5 forms a Zundel-like structure where the proton is equally shared between the oxygen of the zeolite and the oxygen of the alcohol. At higher ethanol loading, a second ethanol molecule helps to stabilize the protonated ethanol at all temperatures by acting as a solvating agent. The vibrational density of states (VDOS), as calculated from the AIMD simulations, are in excellent agreement with measured IR spectra for C 2 H 5 OH, C 2 H 5 OD and C 2 D 5 OH isotopomers and support the existence of both monomers and dimers. A quasi-harmonic approximation (QHA), applied to the VDOS obtained from the AIMD simulations, provides estimates of adsorption free energy within ~10 kJ/mol of the experimentally determined quantities, whereas the traditional approach, employing harmonic frequencies from a single ground state minimum, strongly overestimates the adsorption free energy by at least ~30 kJ/mol. This discrepancy is traced back to the inability of the harmonic approximation to represent the contributions to the vibrational motions of the ethanol molecule upon confinement in the zeolite. KA, MFR, GBM were supported by the Long Term Structural Methusalem Funding by the Flemish Government – grant number BOF09/01M00409. MSL, VAG, RR and JAL were supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences. PNNL is a multiprogram national laboratory operated for DOE by Battelle. Computational resources were provided at W. R. Wiley Environmental Molecular Science Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research located at PNNL, the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory and the Stevin Supercomputer Infrastructure at Ghent University.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

CO Oxidation on Au/TiO 2 : Condition-Dependent Active Sites and Mechanistic Pathways

We present results of ab initio electronic structure and molecular dynamics simulations (AIMD), as well as microkinetic modeling of CO oxidation catalyzed by TiO2 supported Au nanocatalysts. A coverage-dependent microkinetic analysis, based on energetics obtained with density functional methods, shows that the dominant kinetic pathway, activated oxygen species, and catalytic active sites are all strongly depended on both temperature and oxygen partial pressure. A dynamic single atom catalytic (DSAC) mechanism for CO oxidation is proposed to be the dominant (but not the exclusive) pathway for this reaction under oxidative conditions below T < 400 K. This mechanism proceeds via an Au+-CO species that transfers from the Au-cluster to a surface oxygen ad-atom, where it reacts with the TiO2 support with a Mars van Krevelen mechanism to form CO2 and subsequently reintegrates back into the gold cluster to complete the catalytic cycle. At 300 = T = 600 K oxygen-bound single Au+CO sites and the perimeter sites of the Au-nanoparticle work in tandem to optimally catalyze the reaction. Above 600 K a variety of alternate pathways associated with the perimeter sites of the Au nanoparticle and/or the single atom site are found to be the most active pathways. Overall our results highlight the importance of the dynamic behavior of interfacial area between the catalysts and the support, which allows for a high degree of adaptation. This work was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences and performed at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for Department of Energy by Battelle. Computational resources were provided at W. R. Wiley Environmental Molecular Science Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research located at PNNL and the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory.

Wang, Yang-Gang↗
DOE OSTI2022

Droplet formation simulation using mixed finite elements

Droplet formation happens in finite time due to the surface tension force. The linear stability analysis is useful to estimate the size of a droplet but fails to approximate the shape of the droplet. This is due to a highly nonlinear flow description near the point where the first pinch-off happens. A one-dimensional axisymmetric mathematical model was first developed by Eggers and Dupont [“Drop formation in a one-dimensional approximation of the Navier–Stokes equation,” J. Fluid Mech. 262, 205–221 (1994)] using asymptotic analysis. This asymptotic approach to the Navier–Stokes equations leads to a universal scaling explaining the self-similar nature of the solution. Numerical models for the one-dimensional model were developed using the finite difference [Eggers and Dupont, “Drop formation in a one-dimensional approximation of the Navier–Stokes equation,” J. Fluid Mech. 262, 205–221 (1994)] and finite element method [Ambravaneswaran et al., “Drop formation from a capillary tube: Comparison of one-dimensional and two-dimensional analyses and occurrence of satellite drops,” Phys. Fluids 14, 2606–2621 (2002)]. The focus of this study is to provide a robust computational model for one-dimensional axisymmetric droplet formation using the Portable, Extensible Toolkit for Scientific Computation. The code is verified using the Method of Manufactured Solutions and validated using previous experimental studies done by Zhang and Basaran [“An experimental study of dynamics of drop formation,” Phys. Fluids 7, 1184–1203 (1995)]. The present model is used for simulating pendant drops of water, glycerol, and paraffin wax, with an aspiration of extending the application to simulate more complex pinch-off phenomena.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A classical reactive potential for molecular clusters of sulphuric acid and water

We present a two state empirical valence bond (EVB) potential describing interactions between sulphuric acid and water molecules and designed to model proton transfer between them within a classical dynamical framework. The potential has been developed in order to study the properties of molecular clusters of these species, which are thought to be relevant to atmospheric aerosol nucleation. The particle swarm optimisation method has been used to fit the parameters of the EVB model to density functional theory (DFT) calculations. Features of the parametrised model and DFT data are compared and found to be in satisfactory agreement. In particular, it is found that a single sulphuric acid molecule will donate a proton when clustered with four water molecules at 300 K and that this threshold is temperature dependent. SMK was supported in part by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences; JLS and IJF were supported by the IMPACT scheme at University College London (UCL). We acknowledge the UCL Legion High Performance Computing Facility, and associated support services together with the resources of the National Energy Research Scientific Computing Center (NERSC), which is supported by the U.S. Department of Energy under Contract No. DE-AC02- 05CH11231. JLS thanks Dr. Gregory Schenter, Dr. Theo Kurtén and Prof. Hanna Vehkamäki for important guidance and discussions.

aerosols↗
NASA NTRS1993

CONVEX mini manual

The use of the CONVEX computers that are an integral part of the Supercomputing Network Subsystems (SNS) of the Central Scientific Computing Complex of LaRC is briefly described. Features of the CONVEX computers that are significantly different than the CRAY supercomputers are covered, including: FORTRAN, C, architecture of the CONVEX computers, the CONVEX environment, batch job submittal, debugging, performance analysis, utilities unique to CONVEX, and documentation. This revision reflects the addition of the Applications Compiler and X-based debugger, CXdb. The document id intended for all CONVEX users as a ready reference to frequently asked questions and to more detailed information contained with the vendor manuals. It is appropriate for both the novice and the experienced user.

Tennille, Geoffrey M.↗
DOE OSTI2026

Evaluation of LLVM Flang for Production HPC Applications and Modern Fortran Features

In 2025, LLVM released its first Flang Fortran compiler version considered ready for widespread evaluation. We know of no published assessment of Flang compiling a workload- derived portfolio of high-performance computing (HPC) applications. We address this gap using workload data from the National Energy Research Scientific Computing Center (NERSC), which supports more than 10,000 scientists on approximately 1,000 projects. The NERSC workload analyses identify many Fortran components in heavily used applications. We selected 10 such packages with available source code. We compiled them with Flang 22.1.3 on NERSC’s Perlmutter system. Six compiled without code modifications, though some required build-system changes. Three compiled after minor source edits, mostly to address Fortran standard violations. One built only without OpenMP enabled. We evaluated seven additional packages selected for their use of, or enablement of, standard Fortran parallel features: multi-image execution and do concurrent. Six such codes compiled with most or all unit tests passing.

Rasmussen, Katherine↗
DOE OSTI2018

Cloud Quantum Computing of an Atomic Nucleus

Here, we report a quantum simulation of the deuteron binding energy on quantum processors accessed via cloud servers. We use a Hamiltonian from pionless effective field theory at leading order. We design a low-depth version of the unitary coupled-cluster ansatz, use the variational quantum eigensolver algorithm, and compute the binding energy to within a few percent. Our work is the first step towards scalable nuclear structure computations on a quantum processor via the cloud, and it sheds light on how to map scientific computing applications onto nascent quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Thermal Management for FPGA Nodes in HPC Systems

The integration of FPGAs into large-scale computing systems is gaining attention. In these systems, real-time data handling for networking, tasks for scientific computing, and machine learning can be executed with customized datapaths on reconfigurable fabric within heterogeneous compute nodes. At the same time, thermal management, particularly battling the cooling cost and guaranteeing the reliability, is a continuing concern. The introduction of new heterogeneous components into HPC nodes only adds further complexities to thermal modeling and management. The thermal behavior of multi-FPGA systems deployed within large compute clusters is less explored. Here, we first show that the thermal behaviors of different FPGAs of the same generation can vary due to their physical locations in a rack and process variation, even though they are running the same tasks. We present a machine learning–based model to capture the thermal behavior of each individual FPGA in the cluster. We then propose two thermal management strategies guided by our thermal model. First, we mitigate thermal variation and hotspots across the cluster by proactive thermal-aware task placement. Under the tested system and benchmarks, we achieve up to 26.4° C and on average 13.3° C system temperature reduction with no performance penalty. Second, we utilize this thermal model to guide HLS parameter tuning at the task design stage to achieve improved thermal response after deployment.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

High Performance Distributed Computing in a Supercomputer Environment: Computational Services and Applications Issues

This tutorial proposes to be a practical guide for the uninitiated to the main topics and themes of high-performance computing (HPC), with particular emphasis to distributed computing. The intent is first to provide some guidance and directions in the rapidly increasing field of scientific computing using both massively parallel and traditional supercomputers. Because of their considerable potential computational power, loosely or tightly coupled clusters of workstations are increasingly considered as a third alternative to both the more conventional supercomputers based on a small number of powerful vector processors, as well as high massively parallel processors. Even though many research issues concerning the effective use of workstation clusters and their integration into a large scale production facility are still unresolved, such clusters are already used for production computing. In this tutorial we will utilize the unique experience made at the NAS facility at NASA Ames Research Center. Over the last five years at NAS massively parallel supercomputers such as the Connection Machines CM-2 and CM-5 from Thinking Machines Corporation and the iPSC/860 (Touchstone Gamma Machine) and Paragon Machines from Intel were used in a production supercomputer center alongside with traditional vector supercomputers such as the Cray Y-MP and C90.

Kramer, Williams T. C.↗
DOE OSTI2024

Basic Energy Sciences Roundtable: Foundational Science to Accelerate Nuclear Energy Innovation

Energy security, availability, and reliability are among the greatest challenges facing the nation and the planet. An abundant potential source of energy resides in the fundamental atomic building blocks of the universe in the form of nuclear fission and fusion reactions. In fact, energy from nuclear fission currently provides the majority of the world’s zero-carbon electricity, and future fusion energy systems offer great promise; carbon-free nuclear energy technologies can be key to the world’s decarbonized energy future. Although contemporary fission systems use well-established technologies to supply safe and efficient baseload power, they could be more fuel efficient and less costly. Moving beyond massive light-water fission reactors to a variety of advanced nuclear systems—which will vary in size and operate in extremes of temperature, corrosivity, and other parameters—will place stringent conditions on materials and chemical systems. New demands will be placed on the coolants and solvents, the materials, and the monitoring tools used in these reactors. Fusion-based nuclear energy will require superior materials to withstand extremely high temperatures, plasma exposure, radiation damage, and implanted gases. The advantages associated with these new fission and fusion technologies will be realized only through continued advancements in the fundamental science underpinning our knowledge of the physics and chemistry of nuclear systems gained via improved experimental and computational methods. In July 2022, the U.S. Department of Energy’s Office of Basic Energy Sciences—in coordination with the Offices of Nuclear Energy, Fusion Energy Sciences, and Advanced Scientific Computing Research—held a virtual roundtable titled “Foundational Science to Accelerate Nuclear Energy Innovation” to discuss the scientific and technical barriers for advanced nuclear energy systems. Five priority research opportunities were identified to address these scientific and technical challenges and to accelerate progress toward the realization of next-generation fusion and fission energy systems. The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous Basic Energy Sciences workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Modeling and simulation of multiphase flows

This presentation provides an overview of the National Energy Technology Laboratory’s (NETL) multiphase computational fluid dynamics codes. The highly successful Multiphase Flows with Interphase eXchanges (MFIX) suite has been used to model a wide range of applications including post-combustion carbon capture, bioreactor optimization, and bio-FCC regeneration. MFIX-Exa, a state-of-the-art CFD code, developed under DOE’s Exascale Computing Project, is built on the AMReX software framework (https://amrex-codes.github.io/) and is designed to leverage modern accelerator-based compute architectures. This presentation further reviews the underlying physical models of both MFIX and MFIX-Exa and contrasts their similarities and differences. Examples of past and present CFD simulations will illustrate how scientific computing at NETL is being used not only for scientific exploration but also for design, optimization and scale-up of multiphase flow devices.

Musser, Jordan [NETL]↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
NASA NTRS1987

Methods for design and evaluation of integrated hardware/software systems for concurrent computation

Two testbed programming environments to support the evaluation of a large range of parallel architectures have been implemented under the program Parallel Implementation of Scientific Computing Environments (PISCES). The PISCES 1 environment was applied to two areas of aerospace interest: a sparse matrix iterative equation solver and a dynamic scene analysis system. Currently, the NICE/SPAR testbed system for structural analysis is being modified for parallel operation under PISCES 2; the PISCES 1 applications are also being adapted for PISCES 2. A new formal model of concurrent computation has been developed, based on the mathematical system known as H graph semantics together with a timed Petri net model of the parallel aspects of a system.

Pratt, Terrence W.↗
DOE OSTI2017

Toward Rational Design of Cu/SSZ-13 Selective Catalytic Reduction Catalysts: Implications from Atomic-Level Understanding of Hydrothermal Stability

The hydrothermal stability of Cu/SSZ-13 SCR catalysts has been extensively studied, yet atomic level understanding of changes to the zeolite support and the Cu active sites during hydrothermal aging are still lacking. In this work, via the utilization of spectroscopic methods including solid-state 27Al and 29Si NMR, EPR, DRIFTS, and XPS, together with imaging and elemental mapping using STEM, detailed kinetic analyses, and theoretical calculations with DFT, various Cu species, including two types of isolated active sites and CuOx clusters, were precisely quantified for samples hydrothermally aged under varying conditions. This quantification convincingly confirms the exceptional hydrothermal stability of isolated Cu2+-2Z sites, and the gradual conversion of [Cu(OH)]+-Z to CuOx clusters with increasing aging severity. This stability difference is rationalized from the hydrolysis activation barrier difference between the two isolated sites via DFT. Discussions are provided on the nature of the CuOx clusters, and their possible detrimental roles on catalyst stability. Finally, a few rational design principles for Cu/SSZ-13 are derived rigorously from the atomic-level understanding of this catalyst obtained here. The authors gratefully acknowledge the US Department of Energy (DOE), Energy Efficiency and Renewable Energy, Vehicle Technologies Office for the support of this work. Computing time was granted by a user proposal at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL) and by the National Energy Research Scientific Computing Center (NERSC). The experimental studies described in this paper were performed in the EMSL, a national scientific user facility sponsored by the DOE’s Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is operated for the US DOE by Battelle.

Cu/SSZ-13↗
DOE OSTI2015

In Situ Fabrication of PtCo Alloy Embedded in Nitrogen-Doped Graphene Nanopores as Synergistic Catalyst for Oxygen Reduction Reaction

A novel PtCo alloy in situ etched and embedded in graphene nanopores (PtCo/NPG) as a high-performance catalyst for ORR was reported. Graphene nanopores were fabricated in situ while forming PtCo nanoparticles that were uniformly embedded in the graphene nanopores. Given the synergistic effect between PtCo alloy and nanopores, PtCo/NPG exhibited 11.5 times higher mass activity than that of the commercial Pt/C cathode electrocatalyst. DFT calculations indicated that the nanopores in NPG cannot only stabilize PtCo nanoparticles but can also definitely change the electronic structures, thereby change its adsorption abilities. This enhancement can lead to a favorable reaction pathway on PtCo/NPG for ORR. This study showed that PtCo/NPG is a potential candidate for the next generation of Pt-based catalysts in fuel cells. This study also offered a promising alternative strategy and enabled the fabrication of various kinds of metal/graphene nanopore nanohybrids with potential applications in catalysts and potential use for other technological devices. The authors acknowledge the financial support from the National Basic Research Program (973 program, No. 2013CB733501), Zhejiang Provincial Education Department Research Program (Y201326554) and the National Natural Science Foundation of China (No. 21306169, 21101137, 21136001, 21176221 and 91334013). D. Mei acknowledges the support from the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Division of Chemical Sciences, Geosciences & Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated for DOE by Battelle. Computing time was granted by the grand challenge of computational catalysis of the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL) and by the National Energy Research Scientific Computing Center (NERSC).

electrocatalysis↗
DOE OSTI2015

Effect of Graphene with Nanopores on Metal Clusters

Porous graphene, which is a novel type of defective graphene, shows excellent potential as a support material for metal clusters. In this work, the stability and electronic structures of metal clusters (Pd, Ir, Rh) supported on pristine graphene and graphene with different sizes of nanopore were investigated by first-principle density functional theory (DFT) calculations. Thereafter, CO adsorption and oxidation reaction on the Pd-graphene system were chosen to evaluate its catalytic performance. Graphene with nanopore can strongly stabilize the metal clusters and cause a substantial downshift of the d-band center of the metal clusters, thus decreasing CO adsorption. All binding energies, d-band centers, and adsorption energies show a linear change with the size of the nanopore: a bigger size of nanopore corresponds to a stronger metal clusters bond to the graphene, lower downshift of the d-band center, and weaker CO adsorption. By using a suitable size nanopore, supported Pd clusters on the graphene will have similar CO and O2 adsorption ability, thus leading to superior CO tolerance. The DFT calculated reaction energy barriers show that graphene with nanopore is a superior catalyst for CO oxidation reaction. These properties can play an important role in instructing graphene-supported metal catalyst preparation to prevent the diffusion or agglomeration of metal clusters and enhance catalytic performance. This work was supported by National Basic Research Program of China (973Program) (2013CB733501), the National Natural Science Foundation of China (NSFC-21176221, 21136001, 21101137, 21306169, and 91334013). D. Mei acknowledges the support from the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated for DOE by Battelle. Computing time was granted by the grand challenge of computational catalysis of the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL) and by the National Energy Research Scientific Computing Center (NERSC).

cobalt↗