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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluating Ensemble Predictions of South Asian Monsoon Low Pressure System Genesis

Abstract Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.

Suhas, D. L.↗
DOE OSTI2013

Fast MAP Search for Compact Additive Tree Ensembles (CATE)

Since their inception, additive ensembles have been shown to provide a highly effective and robust frame-work for a variety of classification and prediction tasks. Their successful application in a practical setting, however, usually depends on specifying a priori several operational parameters that can be difficult or time-consuming to estimate. In particular, the number of constituent models (e.g., decision trees, logistic models) that comprise the ensemble induces a tradeoff between performance and efficiency that may not be readily characterized a priori. This dichotomy varies with several key factors (e.g., the nature of the underlying models, availability of training data, etc.) and is especially critical in contexts where memory is at a premium (e.g., limited hardware [19]) and/or computational speed is paramount (e.g., real-time stock trading, cybersecurity). We have frequently observed that the classification performance of the ensemble tends to plateau (i.e., achieve saturation) as the number of members in the ensemble (i.e., ensemble size) increases, so that eventually the marginal gain from adding further models falls below a desired tolerance. This observation suggests that superior performance may be achieved via overspecification of the ensemble size. In practice, however, this strategy can easily produce simultaneously redundant and overly complex ensembles at a significant expense to computational efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Preliminary Assessment of Tecplot Chorus for Analyzing Ensemble of CTH Simulations

The exploration of large parameter spaces in search of problem solution and uncertainty quantifcation produces very large ensembles of data. Processing ensemble data will continue to require more resources as simulation complexity and HPC platform throughput increase. More tools are needed to help provide rapid insight into these data sets to decrease manual processing time by the analyst and to increase knowledge the data can provide. One such tool is Tecplot Chorus, whose strengths are visualizing ensemble metadata and linked images. This report contains the analysis and conclusions from evaluating Tecplot Chorus with an example problem that is relevant to Sandia National Laboratories. This report documents a preliminary evaluation of Tecplot Chorus for analyzing ensemble data from CTH simulations. The project that funded this report and evaluation is also evaluating and guiding development with SNL’s Slycat. Slycat and Tecplot Chorus each have their strengths, weaknesses, and overlapping capabilities. It is quite likely that, as the scale of ensemble data increases, both of these tools (and possibly others) will be needed for different processing goals. This report will focus on Tecplot Chorus and its application to an example ensemble of data supplied by David J. Peterson and John P. Korbin; this example is of a flyer plate impact and weld study henceforth referred to as CTH Impact Example. This evaluation also defines a workflow for analysts that can help reduce the time and resources for processing ensemble data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Investigation of short-term effective radiative forcing of fire aerosols over North America using nudged hindcast ensembles

Aerosols from fire emissions can potentially have large impact on clouds and radiation. However, fire aerosol sources are often intermittent, and their effect on weather and climate is difficult to quantify. Here we investigated the short-term effective radiative forcing of fire aerosols using the global aerosol–climate model Community Atmosphere Model version 5 (CAM5). Different from previous studies, we used nudged hindcast ensembles to quantify the forcing uncertainty due to the chaotic response to small perturbations in the atmosphere state. Daily mean emissions from three fire inventories were used to consider the uncertainty in emission strength and injection heights. The simulated aerosol optical depth (AOD) and mass concentrations were evaluated against in situ measurements and reanalysis data. Overall, the results show the model has reasonably good predicting skills. Short (10-day) nudged ensemble simulations were then performed with and without fire emissions to estimate the effective radiative forcing. Results show fire aerosols have large effects on both liquid and ice clouds over the two selected regions in April 2009. Ensemble mean results show strong negative shortwave cloud radiative effect (SCRE) over almost the entirety of southern Mexico, with a 10-day regional mean value of −3.0 W m −2 . Over the central US, the SCRE is positive in the north but negative in the south, and the regional mean SCRE is small (−0.56 W m −2 ). For the 10-day average, we found a large ensemble spread of regional mean shortwave cloud radiative effect over southern Mexico (15.6 % of the corresponding ensemble mean) and the central US (64.3 %), despite the regional mean AOD time series being almost indistinguishable during the 10-day period. Moreover, the ensemble spread is much larger when using daily averages instead of 10-day averages. This demonstrates the importance of using a large ensemble of simulations to estimate the short-term aerosol effective radiative forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Future climate emulations using quantile regressions on large ensembles

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Statistical Ensemble of Large Eddy Simulations

A statistical ensemble of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an ensemble averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the ensemble averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the ensemble of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The ensemble averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical ensemble provided that the ensemble contains at least 16 realizations.

Carati, Daniele↗
NASA NTRS2014

Hybrid Data Assimilation without Ensemble Filtering

The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.

Kalman Filter↗
DOE OSTI2024

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Catalytic site ensembles: A context to reexamine the Langmuir-Hinshelwood kinetic description

The Langmuir-Hinshelwood formalism describes catalytic reactions of Langmuirian surface species under the assumption that all adsorbates are randomly-distributed – enabling adjacency of surface-bound intermediates to be determined solely by coverages of single-site occupants. We demonstrate herein that this approximation is inappropriate even for simple catalytic reactions (e.g. A + A → A 2 ) and manifestly neglects islanding of slowly-consumed species and partitioning of highly-reactive species inherently engendered by ≥ two-site elementary steps (e.g. A*–A* → A 2(g) + *–*). Rigorous description of kinetically-consequential islanding/partitioning phenomena requires explicit description of the coverage and chemical dynamics of all multi-site ensembles. Higher-order, ensemble-specific rate terms identify the particular microscopic events relevant to each ensemble, and, in doing so, reveal that each elementary step (e.g. A (g) adsorption) describes not one event (e.g. A (g) + * → A*) , but a sum over all ensemble-specific paths (e.g. A (g) + *–* → A*–* and A (g) + A*–* → A*–A*). De-convoluting each elementary step into its constituent multi-site paths proffers kinetic detail otherwise inaccessible – enabling (i) identification of rate- and selectivity-determining site ensembles, (ii) calculation of rates and degrees of rate control of ensemble-specific elementary steps, (iii) incorporation of adsorbate surface diffusion, (iv) incorporation of lateral adsorbate interactions, and (v) quantitative description of catalysis of multi-site-occupying intermediates (e.g. *C n H m * species in hydrocarbon (de-)hydrogenation and C-C bond coupling/cleavage reactions) which we demonstrate here is inaccessible to the Langmuir-Hinshelwood formalism even if adsorbate surface diffusion is infinitely-fast.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Assessment of enzyme active site positioning and tests of catalytic mechanisms through X-ray–derived conformational ensembles

How enzymes achieve their enormous rate enhancements remains a central question in biology, and our understanding to date has impacted drug development, influenced enzyme design, and deepened our appreciation of evolutionary processes. While enzymes position catalytic and reactant groups in active sites, physics requires that atoms undergo constant motion. Numerous proposals have invoked positioning or motions as central for enzyme function, but a scarcity of experimental data has limited our understanding of positioning and motion, their relative importance, and their changes through the enzyme’s reaction cycle. To examine positioning and motions and test catalytic proposals, we collected “room temperature” X-ray crystallography data for Pseudomonas putida ketosteroid isomerase (KSI), and we obtained conformational ensembles for this and a homologous KSI from multiple PDB crystal structures. Here, ensemble analyses indicated limited change through KSI’s reaction cycle. Active site positioning was on the 1- to 1.5-Å scale, and was not exceptional compared to noncatalytic groups. The KSI ensembles provided evidence against catalytic proposals invoking oxyanion hole geometric discrimination between the ground state and transition state or highly precise general base positioning. Instead, increasing or decreasing positioning of KSI’s general base reduced catalysis, suggesting optimized Ångstrom-scale conformational heterogeneity that allows KSI to efficiently catalyze multiple reaction steps. Ensemble analyses of surrounding groups for WT and mutant KSIs provided insights into the forces and interactions that allow and limit active-site motions. Most generally, this ensemble perspective extends traditional structure–function relationships, providing the basis for a new era of “ensemble–function” interrogation of enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

eFESTA: Ensemble Feature Exploration with Surface Density Estimates

We propose surface density estimate (SDE) to model the spatial distribution of surface features-isosurfaces, ridge surfaces, and streamsurfaces-in 3D ensemble simulation data. The inputs of SDE computation are surface features represented as polygon meshes, and no field datasets are required (e.g., scalar fields or vector fields). The SDE is defined as the kernel density estimate of the infinite set of points on the input surfaces and is approximated by accumulating the surface densities of triangular patches. We also propose an algorithm to guide the selection of a proper kernel bandwidth for SDE computation. An ensemble Feature Exploration method based on Surface densiTy EstimAtes (eFESTA) is then proposed to extract and visualize the major trends of ensemble surface features. For an ensemble of surface features, each surface is first transformed into a density field based on its contribution to the SDE, and the resulting density fields are organized into a hierarchical representation based on the pairwise distances between them. The hierarchical representation is then used to guide visual exploration of the density fields as well as the underlying surface features. We demonstrate the application of our method using isosurface in ensemble scalar fields, Lagrangian coherent structures in uncertain unsteady flows, and streamsurfaces in ensemble fluid flows.

Computational modeling↗
DOE OSTI2018

Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties

Internal variability in the climate system can contribute substantial uncertainty in climate projections, particularly at regional scales. Internal variability can be quantified using large ensembles of simulations that are identical but for perturbed initial conditions. Here we compare methods for quantifying internal variability. Our study region spans the west coast of North America, which is strongly influenced by El Niño and other large-scale dynamics through their contribution to large-scale internal variability. Using a statistical framework to simultaneously account for multiple sources of uncertainty, we find that internal variability can be quantified consistently using a large ensemble or an ensemble of opportunity that includes small ensembles from multiple models and climate scenarios. The latter also produce estimates of uncertainty due to model differences. Here, we conclude that projection uncertainties are best assessed using small single-model ensembles from as many model-scenario pairings as computationally feasible, which has implications for ensemble design in large modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach

A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

17 WIND ENERGY↗
DOE OSTI2018

Evolution of the mean jet shape and dijet asymmetry distribution of an ensemble of holographic jets in strongly coupled plasma

Some of the most important experimentally accessible probes of the quark- gluon plasma (QGP) produced in heavy ion collisions come from the analysis of how the shape and energy of sprays of energetic particles produced within a cone with a specified opening angle (jets) in a hard scattering are modified by their passage through the strongly coupled, liquid, QGP. We model an ensemble of back-to-back dijets for the purpose of gaining a qualitative understanding of how the shapes of the individual jets and the asymmetry in the energy of the pairs of jets in the ensemble are modified by their passage through an expanding cooling droplet of strongly coupled plasma, in the model in a holographic gauge theory that is dual to a 4+1-dimensional black-hole spacetime that is asymptotically anti-de Sitter (AdS). We build our model by constructing an ensemble of strings in the dual gravitational description of the gauge theory. We model QCD jets in vacuum using strings whose endpoints are moving “downward” into the gravitational bulk spacetime with some fixed small angle, an angle that represents the opening angle (ratio of jet mass to jet energy) that the QCD jet would have in vacuum. Such strings must be moving through the gravitational bulk at (close to) the speed of light; they must be (close to) null. This condition does not specify the energy distribution along the string, meaning that it does not specify the shape of the jet being modeled. We study the dynamics of strings that are initially not null and show that strings with a wide range of initial conditions rapidly accelerate and become null and, as they do, develop a similar distribution of their energy density. We use this distribution of the energy density along the string, choose an ensemble of strings whose opening angles and energies are distributed as in perturbative QCD, and show that we can then fix one of the two model parameters such that the mean jet shape for the jets in the ensemble that we have built matches that measured in proton-proton collisions reasonably well. This is a novel way for hybridizing relevant inputs from perturbative QCD and a strongly coupled holographic gauge theory in the service of modeling jets in QGP. We send our ensemble of strings through an expanding cooling droplet of strongly coupled plasma, choosing the second model parameter so as to get a reasonable value for $R$ $^{AA}_{jet}$, the suppression in the number of jets, and study how the mean jet shape and the dijet asymmetry are modified, comparing both to measurements from heavy ion collisions at the LHC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

On the Local Equivalence Between the Canonical and the Microcanonical Ensembles for Quantum Spin Systems

We study a quantum spin system on the d-dimensional hypercubic lattice Λ with N=L{sup d} sites with periodic boundary conditions. We take an arbitrary translation invariant short-ranged Hamiltonian. For this system, we consider both the canonical ensemble with inverse temperature β{sub 0} and the microcanonical ensemble with the corresponding energy U{sub N}(β{sub 0}). For an arbitrary self-adjoint operator  whose support is contained in a hypercubic block B inside Λ, we prove that the expectation values of  with respect to these two ensembles are close to each other for large N provided that β{sub 0} is sufficiently small and the number of sites in B is o(N{sup 1/2}). This establishes the equivalence of ensembles on the level of local states in a large but finite system. The result is essentially that of Brandao and Cramer (here restricted to the case of the canonical and the microcanonical ensembles), but we prove improved estimates in an elementary manner. We also review and prove standard results on the thermodynamic limits of thermodynamic functions and the equivalence of ensembles in terms of thermodynamic functions. The present paper assumes only elementary knowledge on quantum statistical mechanics and quantum spin systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2010

Spectral properties of embedded Gaussian unitary ensemble of random matrices with Wigner's SU(4) symmetry

For m fermions in {Omega} number of single particle orbitals, each fourfold degenerate, we introduce and analyze in detail embedded Gaussian unitary ensemble of random matrices generated by random two-body interactions that are SU(4) scalar [EGUE(2)-SU(4)]. Here the SU(4) algebra corresponds to the Wigner's supermultiplet SU(4) symmetry in nuclei. Embedding algebra for the EGUE(2)-SU(4) ensemble is U(4{Omega}) contains U({Omega}) x SU(4). Exploiting the Wigner-Racah algebra of the embedding algebra, analytical expression for the ensemble average of the product of any two m particle Hamiltonian matrix elements is derived. Using this, formulas for a special class of U({Omega}) irreducible representations (irreps) {l_brace}4{sup r}, p{r_brace}, p = 0, 1, 2, 3 are derived for the ensemble averaged spectral variances and also for the covariances in energy centroids and spectral variances. On the other hand, simplifying the tabulations of Hecht for SU({Omega}) Racah coefficients, numerical calculations are carried out for general U({Omega}) irreps. Spectral variances clearly show, by applying Jacquod and Stone prescription, that the EGUE(2)-SU(4) ensemble generates ground state structure just as the quadratic Casimir invariant (C{sub 2}) of SU(4). This is further corroborated by the calculation of the expectation values of C{sub 2}[SU(4)] and the four periodicity in the ground state energies. Secondly, it is found that the covariances in energy centroids and spectral variances increase in magnitude considerably as we go from EGUE(2) for spinless fermions to EGUE(2) for fermions with spin to EGUE(2)-SU(4) implying that the differences in ensemble and spectral averages grow with increasing symmetry. Also for EGUE(2)-SU(4) there are, unlike for GUE, non-zero cross-correlations in energy centroids and spectral variances defined over spaces with different particle numbers and/or U({Omega}) [equivalently SU(4)] irreps. In the dilute limit defined by {Omega} {yields} {infinity}, r >> 1 and r/{Omega} {yields} 0, for the {l_brace}4{sup r}, p{r_brace} irreps, we have derived analytical results for these correlations. All correlations are non-zero for finite {Omega} and they tend to zero as {Omega} {yields} {infinity}.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Local indistinguishability and incompleteness of entangled orthogonal bases: Method to generate two-element locally indistinguishable ensembles

Highlights: • Local indistinguishability of orthogonal quantum states. • Bipartite and multipartite quantum ensembles. • Unextendible entangled bases and uncompletable entangled bases. • Two-element locally indistinguishable quantum ensembles. • Multiparty unextendible entangled bases with unextendibility in all partitions. We relate the phenomenon of local indistinguishability of orthogonal states with the properties of unextendibility and uncompletability of entangled bases for bipartite and multipartite quantum systems. We prove that all two-qubit unextendible entangled bases are of size three and they cannot be perfectly distinguished by separable measurements. We identify a method of constructing two-element orthogonal ensembles, based on the concept of unextendible entangled bases, that can potentially lead to information sharing applications. Two-element ensembles form the fundamental unit of ensembles, and yet does not offer locally indistinguishable ensembles for pure state elements. Going over to mixed states does open this possibility, but can be difficult to identify. The method provided using unextendible entangled bases can be used for their systematic generation. In multipartite systems, we find a class of unextendible entangled bases for which the unextendibility property remains conserved across all bipartitions. We also identify nonlocal operations, local implementation of which require entangled resource states from a higher-dimensional quantum system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Ensemble Effects on Hydroxide Bond Dissociation Free Energies in Polyoxovanadate Clusters

Understanding structure-property relationships is foundational to numerous modern chemistries, such as proton-coupled electron transfer (PCET). However, an experimentally measured property is the result of the behavior from an ensemble of molecules. Neglecting ensemble effects, especially under complex chemical environments, may obfuscate these relationships and lead to discrepancies between theory and experiment. In this work, we demonstrate the impact of configurational entropy and local chemical environments on hydroxide bond dissociation free energies [BDFE- (O−H)] for a set of polyoxovanadate nanoclusters, at ambient conditions. The O−H bond strengths are investigated via density functional theory (DFT) coupled with statistical thermodynamic analysis and bilinear modeling, and compared with previous experimental results on the same systems, namely electrochemical solutions of: [V 6 O 13−x (OH) x (TRIOL R ) 2 ] −2 (x = 2, 4, 6; R = NO 2 , Me) and [V 6 O 11−x (OMe) 2 (OH) x (TRIOL NO 2 ) 2 ] −2 (x = 2, 4). Interestingly, we find that ensemble effects, even at room temperature, can account for a significant portion of the BDFE(O−H) trend with the degree of reduction via H atom binding, which cannot be fully captured by single-structure, static DFT calculations. Moreover, we find that the ensemble effects may be replicated statistically, requiring only enumeration of energetically accessible H-binding sites. With the ensemble effects resolved, we present a simple bilinear model to reconcile remaining biases between experiment and ensemble-informed theory, which corelate with clusterspecific electronic environment differences. The bilinear model achieves outstanding accuracy vs experiments with a root-mean squared error of 0.4 kcal/mol. Finally, based on the physicochemical characteristics of hydrogen interaction with polyoxometalates, we present a simple methodology that captures the BDFE(O−H) trend while dramatically reducing required DFT calculations by 98% and achieving accuracy within 1 kcal/mol. Overall, this work elucidates the roles and structural origins of configurational entropy and chemical effects on polyoxometalate hydroxide bond energies, with potential applicability to various atomically precise metal oxide systems. Importantly, it introduces models for rapid and highly accurate property calculations in connection with experiments.

Cluster chemistry↗