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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Probing catalytic activity in defect sites in transition metal oxides and sulfides using cluster models: A combined experimental and theoretical approach

Our research program has combined experimental and computational methods to study a range of cluster models for heterogeneous catalytic materials. The focus of our studies has been transition metal oxide and sulfide clusters in non-traditional oxidation states, and their chemical and physical interactions with water, to model steps in the electrocatalytic or photocatalytic hydrogen evolution reaction (HER). In the closing year of this project, we have also begun to explore addition systems in preparation for future grant proposals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Supervised and cluster block training statistics were used to analyze the thematic mapper simulation MSS data (both 1979 and 1980 data sets). Cover information classes identified on SAR imagery include: hardwood, pine, mixed pine hardwood, clearcut, pasture, crops, emergent crops, bare soil, urban, and water. Preliminary analysis of the HH and HV polarized SAR data indicate a high variance associated with each information class except for water and bare soil. The large variance for most spectral classes suggests that while the means might be statistically separable, an overlap may exist between the classes which could introduce a significant classification error. The quantitative values of many cover types are much larger on the HV polarization than on the HH, thereby indicating the relative nature of the digitized data values. The mean values of the spectral classes in the areas with larger look angles are greater than the means of the same cover type in other areas having steeper look angles. Difficulty in accurately overlaying the dual polarization of the SAR data was resolved.

Hoffer, R. M.↗
NASA NTRS2010

Device and method to enhance availability of cluster-based processing systems

An electronic computing device including at least one processing unit that implements a specific fault signal upon experiencing an associated fault, a control unit that generates a specific recovery signal upon receiving the fault signal from the at least one processing unit, and at least one input memory unit. The recovery signal initiates specific recovery processes in the at least one processing unit. The input memory buffers input data signals input to the at least one processing unit that experienced the fault during the recovery period.

Lupia, David J.↗
DOE OSTI2016

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

An efficient computational scheme for electronic excitation spectra of molecules in solution using the symmetry-adapted cluster–configuration interaction method: The accuracy of excitation energies and intuitive charge-transfer indices

Solvent effects on electronic excitation spectra are considerable in many situations; therefore, we propose an efficient and reliable computational scheme that is based on the symmetry-adapted cluster-configuration interaction (SAC-CI) method and the polarizable continuum model (PCM) for describing electronic excitations in solution. The new scheme combines the recently proposed first-order PCM SAC-CI method with the PTE (perturbation theory at the energy level) PCM SAC scheme. This is essentially equivalent to the usual SAC and SAC-CI computations with using the PCM Hartree-Fock orbital and integrals, except for the additional correction terms that represent solute-solvent interactions. The test calculations demonstrate that the present method is a very good approximation of the more costly iterative PCM SAC-CI method for excitation energies of closed-shell molecules in their equilibrium geometry. This method provides very accurate values of electric dipole moments but is insufficient for describing the charge-transfer (CT) indices in polar solvent. The present method accurately reproduces the absorption spectra and their solvatochromism of push-pull type 2,2{sup ′}-bithiophene molecules. Significant solvent and substituent effects on these molecules are intuitively visualized using the CT indices. The present method is the simplest and theoretically consistent extension of SAC-CI method for including PCM environment, and therefore, it is useful for theoretical and computational spectroscopy.

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

Deprotonated sulfamic acid and its homodimers: Does sulfamic acid adopt zwitterion during cluster growth?

Here we present a joint experimental and computational study on the geometric and electronic structures of deprotonated sulfamic acid (SA) clusters [(SA) n –H] – (n = 1, 2) employing negative ion photoelectron spectroscopy and high-level ab initio calculations. The photoelectron spectra provide the vertical/adiabatic detachment energy (VDE/ADE) of the sulfamate anion (SM – ) H 2 N•SO 3 – at 4.85 ± 0.05 and 4.58 ± 0.08 eV, respectively, and the VDE and ADE of the SM – •SA dimer at 6.41 ± 0.05 and 5.87 ± 0.08 eV, respectively. The significantly increased electron binding energies of the dimer confirm the enhanced electronic stability upon the addition of one SA molecule. The CCSD(T)-predicted VDEs/ADEs agree excellently with the experimental data, confirming the identified structures as the most stable ones. Two types of dimer isomers possessing different hydrogen bonding (HB) motifs are identified, corresponding to SM – binding to a zwitterionic SA (SM – •SA z ) and a canonical SA (SM – •SA c ), respectively. Two N–H$\cdots$O HBs and one superior O–H$\cdots$O HB are formed in the lowest-lying SM – •SA c , while SM – •SA z has three moderate N–H$\cdots$O HBs, with the former being 4.71 kcal/mol more stable. Further theoretical analyses reveal that the binding strength advantage of SM – •SA c over SM – •SA z arises from its significant contributions of orbital interactions between fragments, illustrating that sulfamate strongly interacts with its parent SA acid and preferably chooses the canonical SA in the subsequent cluster formations. Given the prominent presence of SA, this study provides the first evidence that the canonical dimer model of sulfamic acid should exist as a superior configuration during cluster growth.

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

Beyond real: alternative unitary cluster Jastrow models for molecular electronic structure calculations on near-term quantum computers

Near-term quantum devices require wavefunction ansätze that are expressive while also of shallow circuit depth in order to both accurately and efficiently simulate molecular electronic structure. While the unitary coupled cluster ansatz (e.g., UCCSD) has become a standard, the high gate count associated with the implementation of this limits its feasibility on noisy intermediate-scale quantum (NISQ) hardware. k -Fold unitary cluster Jastrow (uCJ) ansätze mitigate this challenge by providing O( kN 2 ) circuit scaling and favorable linear depth circuit implementation. Previous work has focused on the real orbitalrotation (Re-uCJ) variant of uCJ, which allows an exact (Trotter-free) implementation. Here we extend and generalize the k -fold uCJ framework by introducing two new variants, Im-uCJ and g-uCJ, which incorporate imaginary and fully complex orbital rotation operators, respectively. Similar to Re-uCJ, both of the new variants achieve quadratic gate-count scaling. Our results focus on the simplest k = 1 model, and show that the uCJ models frequently maintain energy errors within chemical accuracy (∼1 kcal mol −1 ). Both g-uCJ and Im-uCJ are more expressive in terms of capturing electron correlation and are also more accurate than the earlier Re-uCJ ansatz. We further show that Im-uCJ and g-uCJ circuits can also be implemented exactly, without any Trotter decomposition. Numerical tests using k = 1 on H 2 , H 3 + , Be 2 , C 2 H 4 , C 2 H 6 and C 6 H 6 in various basis sets confirm the practical feasibility of these shallow Jastrow-based ansätze for applications on near-term quantum hardware.

Tkachenko, Nikolay V. [University of California, B↗
DOE OSTI2019

Mass functions, luminosity functions, and completeness measurements from clustering redshifts

Abstract This paper presents stellar mass functions and i-band luminosity functions for Sloan Digital Sky Survey (SDSS) galaxies with i < 21 using clustering redshifts. From these measurements, we also compute targeting completeness measurements for the Baryon Oscillation Spectroscopic Survey (BOSS). Clustering redshifts is a method of obtaining the redshift distribution of a sample of galaxies with only photometric information by measuring the angular cross-correlation with a spectroscopic sample in different redshift bins. We construct a spectroscopic sample containing data from the BOSS + eBOSS surveys, allowing us to recover redshift distributions from photometric data out to z ≃ 2.5. We produce k-corrected i-band luminosity functions and stellar mass functions by applying clustering redshifts to SDSS DR8 galaxies in small bins of colour and magnitude. There is little evolution in the mass function between 0.2 < z < 0.8, implying that the most massive galaxies form most of their mass before z = 0.8. These mass functions are used to produce stellar mass completeness estimates for the BOSS, giving a stellar mass completeness of $80{{\ \rm per\ cent}}$ above M⋆ > 1011.4 between 0.2 < z < 0.7, with completeness falling significantly at redshifts higher than 0.7, and at lower masses. Large photometric data sets will be available in the near future (DECaLS, DES, Euclid), so this and similar techniques will become increasingly useful in order to fully utilize these data.

Bates, Dominic J.↗
DOE OSTI2018

Electronic Detection of Oxygen Adsorption and Size-Specific Doping of Few-Atom Gold Clusters on Graphene

Graphene's sensitivity to adsorbed particles has attracted widespread attention because of its potential sensor applications. Size-selected few-atom clusters are promising candidates as adparticles to graphene. Due to their small size, physicochemical properties are dominated by quantum size effects. In particular, few-atom gold clusters demonstrate a significant catalytic activity in various oxidation reactions. In this joint experimental and computational work, size-selected gold clusters with 3 and 6 atoms adsorbed on graphene field-effect transistors and their interaction with molecular oxygen are investigated. While oxygen adsorbs at both cluster sizes, there is a pronounced cluster size dependence in the corresponding doping, as demonstrated via first-principle calculations and electronic transport measurements. Furthermore, the doping of gold cluster decorated graphene changes sign from n- to p-doping upon oxygen adsorption, directly evidencing electron transfer to the oxygen molecules and hence their activation. These observations pinpoint graphene as a valuable platform to investigate and exploit size-dependent cluster properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Thermodynamics of Metal Nanoparticles: Energies and Enthalpies of Formation of Magnesium Clusters and Nanoparticles as Large as 1.3 nm

The major obstacle that prevents reliable electronic structure studies of nanoparticles is the rapid increasing computational cost for benchmark calculations using coupled-cluster methods. We show that a CCSD(T) scheme with an MP2/CBS correction can reproduce accurate cohesive energies for magnesium clusters, and this scheme is much less computationally demanding than other reliable methods, so it is applied to Mg n with n up to 19, which enters the realm of nanoparticles. (The diameters of all Mg clusters n ≥ 11 are >1 nm). With the extended benchmark data, we validate exchange–correlation functionals into the nanoparticle regime and use the two best-validated functionals to calculate the enthalpy of formation of Mg 28 , with a diameter of 1.30 nm. We also calculated the enthalpy of formation of all Mg clusters and nanoparticles from Mg 2 to Mg 19 . This kind of reliable thermodynamic data on size-selected metal nanoparticles has been hard to come by, either experimentally or theoretically, but it is badly needed to support applications in catalysis, electrochemistry, and other technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

The Development of Two Science Investigator-led Processing Systems (SIPS) for NASA's Earth Observation System (EOS)

In 2001, NASA Goddard Space Flight Center's Laboratory for Terrestrial Physics started the construction of a science Investigator-led Processing System (SIPS) for processing data from the Ozone Monitoring Instrument (OMI) which will launch on the Aura platform in mid 2004. The Ozone Monitoring Instrument (OMI) is a contribution of the Netherlands Agency for Aerospace Programs (NIVR) in collaboration with the Finnish Meteorological Institute (FMI) to the Earth Observing System (EOS) Aura mission. It will continue the Total Ozone Monitoring System (TOMS) record for total ozone and other atmospheric parameters related to ozone chemistry and climate. OMI measurements will be highly synergistic with the other instruments on the EOS Aura platform. The LTP previously developed the Moderate Resolution Imaging Spectrometer (MODIS) Data Processing System (MODAPS), which has been in full operations since the launches of the Terra and Aqua spacecrafts in December, 1999 and May, 2002 respectively. During that time, it has continually evolved to better support the needs of the MODIS team. We now run multiple instances of the system managing faster than real time reprocessings of the data as well as continuing forward processing. The new OMI Data Processing System (OMIDAPS) was adapted from the MODAPS. It will ingest raw data from the satellite ground station and process it to produce calibrated, geolocated higher level data products. These data products will be transmitted to the Goddard Distributed Active Archive Center (GDAAC) instance of the Earth Observing System (EOS) Data and Information System (EOSDIS) for long term archive and distribution to the public. The OMIDAPS will also provide data distribution to the OMI Science Team for quality assessment, algorithm improvement, calibration, etc. We have taken advantage of lessons learned from the MODIS experience and software already developed for MODIS. We made some changes in the hardware system organization, database and software to adapt the system for OMI. We replaced the fundamental database system, Sybase, with an Open Source RDBMS called PostgreSQL, and based the entire OMIDAPS on a cluster of Linux based commodity computers rather than the large SGI servers that MODAPS uses. Rather than relying on a central I/O server host, the new system distributes its data archive among multiple server hosts in the cluster. OMI is also customizing the graphical user interfaces and reporting structure to more closely meet the needs of the OMI Science Team. Prior to 2003, simulated OMI data and the science algorithms were not ready for production testing. We initially constructed a prototype system and tested using a 25 year dataset of Total Ozone Mapping Spectrometer (TOMS) and Solar Backscatter Ultraviolet Instrument (SBUV) data. This prototype system provided a platform to support the adaptation of the algorithms for OMI, and provided reprocessing of the historical data aiding in its analysis. In a recent reanalysis of the TOMS data, the OMIDAPS processed 108,000 full orbits of data through 4 processing steps per orbit, producing about 800,000 files (400 GiB) of level 2 and greater data files. More recently we have installed two instances of the OMIDAPS for integration and testing of OM1 science processes as they get delivered from the Science Team. A Test instance of the OMIDAPS has also supported a series of "Interface Confidence Tests" (ICTs) and End-to-End Ground System tests to ensure the launch readiness of the system. This paper will discuss the high-level hardware, software, and database organization of the OMIDAPS and how it builds on the MODAPS heritage system. It will also provide an overview of the testing and implementation of the production OMIDAPS.

Tilmes, Curt↗
NASA NTRS1994

X-ray clusters in a cold dark matter + lambda universe: A direct, large-scale, high-resolution, hydrodynamic simulation

A new, three-dimensional, shock-capturing, hydrodynamic code is utilized to determine the distribution of hot gas in a cold dark matter (CDM) + lambda model universe. Periodic boundary conditions are assumed: a box with size 85/h Mpc, having cell size 0.31/h Mpc, is followed in a simulation with 270(exp 3) = 10(exp 7.3) cells. We adopt omega = 0.45, lambda = 0.55, h identically equal to H/100 km/s/Mpc = 0.6, and then, from the cosmic background explorer (COBE) and light element nucleosynthesis, sigma(sub 8) = 0.77, omega(sub b) = 0.043. We identify the X-ray emitting clusters in the simulation box, compute the luminosity function at several wavelength bands, the temperature function and estimated sizes, as well as the evolution of these quantities with redshift. This open model succeeds in matching local observations of clusters in contrast to the standard omega = 1, CDM model, which fails. It predicts an order of magnitude decline in the number density of bright (h nu = 2-10 keV) clusters from z = 0 to z = 2 in contrast to a slight increase in the number density for standard omega = 1, CDM model. This COBE-normalized CDM + lambda model produces approximately the same number of X-ray clusters having L(sub x) greater than 10(exp 43) erg/s as observed. The background radiation field at 1 keV due to clusters is approximately the observed background which, after correction for numerical effects, again indicates that the model is consistent with observations.

Cen, Renyue↗
DOE OSTI2021

Dynamic Ride-Matching for Large-Scale Transportation Systems

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2015

Scattering Properties of Heterogeneous Mineral Particles with Absorbing Inclusions

We analyze the results of numerically exact computer modeling of scattering and absorption properties of randomly oriented poly-disperse heterogeneous particles obtained by placing microscopic absorbing grains randomly on the surfaces of much larger spherical mineral hosts or by imbedding them randomly inside the hosts. These computations are paralleled by those for heterogeneous particles obtained by fully encapsulating fractal-like absorbing clusters in the mineral hosts. All computations are performed using the superposition T-matrix method. In the case of randomly distributed inclusions, the results are compared with the outcome of Lorenz-Mie computations for an external mixture of the mineral hosts and absorbing grains. We conclude that internal aggregation can affect strongly both the integral radiometric and differential scattering characteristics of the heterogeneous particle mixtures.

minerals↗
DOE OSTI2020

Photodissociation and Theory to Investigate Uranium Oxide Cluster Cations

Uranium oxide cluster cations of the form U n O m + are produced by laser vaporization of a depleted uranium rod in a pulsed supersonic expansion. Ions are mass-analyzed and mass-selected with a time-of-flight spectrometer and studied with UV laser multiphoton dissociation. Cations of the stoichiometry UO 2 (UO 3 ) n + were observed as photofragments from all photodissociated cluster cations. (UO 3 ) n + clusters were also observed to result from dissociation of larger (UO 3 ) n + clusters, with UO 3 neutral as a common leaving group. Electronic structure calculations were used to investigate the stability of the prominent uranium oxide cluster cations using density functional theory (DFT) with the hybrid B3LYP exchange-correlation functional and at the CCSD(T) level and cc-pVnZ-PP basis sets (n = D,T), including diffuse orbitals as computational expense and availability permitted. Clustering energies, relative energies and dissociation energies of the cations are reported. The lowest energy neutral (UO 3 ) n clusters up to n = 3 are rings, n = 4 and 5 are chains with very low energy rings, and n = 6 is 3D. Finally, the lowest energy structures for UO 2 (UO 3 ) n + are composed of uranyl-like UO 2 + units bound by bridging oxygens to other UO 2 2+ units for n = 2 and 3, and for n = 4 a more complex 3D structure is predicted.

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

Cosmological constraints from galaxy clusters in the 2500 square-degree SPT-SZ survey

We present cosmological parameter constraints obtained from galaxy clusters identified by their Sunyaev–Zel’dovich effect signature in the 2500 square-degree South Pole Telescope Sunyaev Zel’dovich (SPT-SZ) survey. We consider the 377 cluster candidates identified at z>0.25 with a detection significance greater than five, corresponding to the 95% purity threshold for the survey. We compute constraints on cosmological models using the measured cluster abundance as a function of mass and redshift. We include additional constraints from multi-wavelength observations, including Chandra X-ray data for 82 clusters and a weak lensing-based prior on the normalization of the mass-observable scaling relations. Assuming a spatially flat ΛCDM cosmology, we combine the cluster data with a prior on H {sub 0} and find σ{sub 8}=0.784±0.039 and Ω{sub m}=0.289±0.042, with the parameter combination σ{sub 8}(Ω{sub m}/0.27){sup 0.3}=0.797±0.031. These results are in good agreement with constraints from the cosmic microwave background (CMB) from SPT, WMAP, and Planck, as well as with constraints from other cluster data sets. We also consider several extensions to ΛCDM, including models in which the equation of state of dark energy w, the species-summed neutrino mass, and/or the effective number of relativistic species (N{sub eff}) are free parameters. When combined with constraints from the Planck CMB, H {sub 0}, baryon acoustic oscillation, and SNe, adding the SPT cluster data improves the w constraint by 14%, to w=−1.023±0.042.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Reaction of CO 2 with Groups 4 and 6 Transition Metal Oxide Clusters

Density functional theory (DFT) and coupled cluster theory (CCSD(T)) were used to research the addition of CO 2 to group 4 (MO 2 ) n and group 6 (MO 3 ) n (n = 1, 2, 3) nanoclusters. The structures and energetics arising from Lewis acid–base addition (physisorption) and formation of CO 3 2– (chemisorption) of CO 2 to these clusters were predicted. Physisorption and chemisorption of CO 2 are predicted to be thermodynamically allowed for group 4 (MO 2 ) n clusters, with chemisorption being more favored energetically. Correlations of the ligand binding energies (LBEs) for the group 4 clusters are made with the fluoride affinities and M–O and M=O bond strengths of the clusters. The LBEs for chemisorption on the Zr and Ti clusters are consistent with published experimental and computational studies of bulk solids. Physisorption LBEs for the Ti and Zr clusters are more exothermic than the bulk values, as the cluster models allow for better relaxation at the metal sites. Chemisorption is not predicted to occur with group 6 (MO 3 ) n clusters, as the larger chemisorbed structures were all found to be metastable. Furthermore, CO 2 is predicted to weakly physisorb to (WO 3 ) n with physisorption correlating with the Lewis acidity of the metal site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗