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DOE OSTI2022

Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations

Knowledge of the chemical bonding of HfO and HfB ground and low-lying electronic states provides essential insights into a range of catalysts and materials that contain Hf–O or Hf–B moieties. Here, we carry out high-level multi-reference configuration interaction theory and coupled cluster quantum chemical calculations on these systems. We compute full potential energy curves, excitation energies, ionization energies, electronic configurations, and spectroscopic parameters with large quadruple-ζ and quintuple-ζ quality correlation consistent basis sets. We also investigate equilibrium chemical bonding patterns and effects of correlating core electrons on property predictions. Differences in the ground state electron configuration of HfB(X 4 Σ - ) and HfO(X 1 Σ + ) lead to a significantly stronger bond in HfO than HfB, as judged by both dissociation energies and equilibrium bond distances. We extend our analysis to the chemical bonding patterns of the isovalent HfX (X = O, S, Se, Te, and Po) series and observe similar trends. We also note a linear trend between the decreasing value of the dissociation energy (D e ) from HfO to HfPo and the singlet–triplet energy gap (ΔE S–T ) of the molecule. Finally, we compare these benchmark results to those obtained using density functional theory (DFT) with 23 exchange–correlation functionals spanning multiple rungs of “Jacob’s ladder.” When comparing DFT errors to coupled cluster reference values on dissociation energies, excitation energies, and ionization energies of HfB and HfO, we observe semi-local generalized gradient approximations to significantly outperform more complex and high-cost functionals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Toward DMRG-tailored coupled cluster method in the 4c-relativistic domain

There are three essential problems in computational relativistic chemistry: Electrons moving at relativistic speeds, close lying states, and dynamical correlation. Currently available quantum-chemical methods are capable of solving systems with one or two of these issues. However, there is a significant class of molecules in which all the three effects are present. These are the heavier transition metal compounds, lanthanides, and actinides with open d or f shells. For such systems, sufficiently accurate numerical methods are not available, which hinders the application of theoretical chemistry in this field. In this paper, we combine two numerical methods in order to address this challenging class of molecules. These are the relativistic versions of coupled cluster methods and the density matrix renormalization group (DMRG) method. To the best of our knowledge, this is the first relativistic implementation of the coupled cluster method externally corrected by DMRG. The method brings a significant reduction of computational costs as we demonstrate on the system of TlH, AsH, and SbH.

Brandejs, Jan (ORCID:0000000221073095)↗

DOE OSTI2018

Optimization of stepwise clustering algorithm in backward trajectory analysis

In recent years, the backward trajectory model has been widely used in the research of meteorological and atmospheric environmental quality. This paper presents a comprehensive study on a stepwise clustering analysis algorithm in the clustering process of backward trajectory model and an application of the clustering analysis of single-particle backward trajectory in 2016 in Changchun City. This study starts with an analysis of the original stepwise clustering algorithm and its application to a clustering process of 8784 backward trajectories during 48 h in Changchun City as a benchmark test case. Then, two improvements are made in the algorithm: First, in the process of finding the optimal classification, the algorithm complexity is improved from original O(n 3 ) to O(log(n)*n 2 ) through algorithm improvement. The algorithm performance is enhanced by log(n) times. Next, in the process of re-establishing the classification, the algorithm complexity is improved from the original O(m*n 2 ) to O(m*log(n)*n), that is another algorithm performance improvement by a factor of log(n). Therefore, the accumulative execution efficiency improvement through the algorithm optimization is 2*log(n) times, which has been further verified in the practical application in Changchun City.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Benchmark relativistic delta-coupled-cluster calculations of K-edge core-ionization energies of third-row elements

Here a benchmark computational study of K-edge core-ionization energies of third-row elements using relativistic delta-coupled-cluster (ΔCC) methods and a revised core-valence separation (CVS) scheme is reported. High-level relativistic (HLR) corrections beyond the spin-free exact two-component theory in its one-electron variant (SFX2C-1e), including the contributions from two-electron picture-change effects, spin–orbit coupling, the Breit term, and quantum electrodynamics effects, have been taken into account and demonstrated to play an important role. Relativistic ΔCC calculations are shown to provide accurate results for core-ionization energies of third-row elements. The SFX2C-1e-CVS-ΔCC results augmented with HLR corrections show a maximum deviation of less than 0.5 eV with respect to experimental values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Quantum Solvers for Plane-Wave Hamiltonians: Abridging Virtual Spaces Through the Optimization of Pairwise Correlations

For many-body methods such as MCSCF and CASSCF, in which the number of one-electron orbitals is optimized and independent of the basis set used, there are no problems with using plane-wave basis sets. However, for methods currently used in quantum computing such as select configuration interaction (CI) and coupled cluster (CC) methods, it is necessary to have a virtual space that is able to capture a significant amount of electron-electron correlation in the system. The virtual orbitals in a pseudopotential plane-wave Hartree–Fock calculation, because of Coulomb repulsion, are often scattering states that interact very weakly with the filled orbitals. As a result, very little correlation energy is captured from them. The use of virtual spaces derived from the one-electron operators has also been tried, and while some correlations are captured, the amount is quite low. To overcome these limitations, we have been developing new classes of algorithms to define virtual spaces by optimizing orbitals from small pairwise CI Hamiltonians, which we term as correlation optimized virtual orbitals with the abbreviation COVOs. With these procedures, we have been able to derive virtual spaces, containing only a few orbitals, which are able to capture a significant amount of correlation. The focus in this manuscript is on using these derived basis sets to target full CI (FCI) quality results for H2 on near-term quantum computers. However, the initial results for this approach were promising. We were able to obtain good agreement with FCI/cc-pVTZ results for this system with just 4 virtual orbitals, using both FCI and quantum simulations. The quality of the results using COVOs suggests that it may be possible to use them in other many-body approaches, including coupled cluster and Møller–Plesset perturbation theories, and open up the door to many-body calculations for pseudopotential plane-wave basis set methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Microseismic Focal Mechanisms and Implications for Changes in Stress during the 2014 Newberry EGS Stimulation

We adapt the relative polarity method from Shelly et al. (2016) to compute focal mechanisms for microearthquakes associated with the 2014 hydroshearing stimulation at the Newberry volcano geothermal site. We focus the analysis on events relocated by Aguiar and Myers (2018), who report that six event clusters predominantly comprise the 2014 sequence. Data quality allows focal mechanism analysis for four of the six event clusters. We use Hardebeck and Shearer (2002, 2003; hereafter HASH) to compute focal mechanisms based on first–motion polarities and S/P amplitude ratios. We manually determine P– and S–wave polarities for a well–recorded reference event in each cluster, then use waveform cross correlation to determine whether recordings of other events in the cluster are the same or reversed polarity at each network station. Here, most waveform polarities are consistent with the affiliated reference event, indicating similar focal mechanisms within each cluster. The deeper clusters are east–west–striking normal faults, whereas the shallower clusters, close to the top of the open–hole section of the borehole, are strike slip with east–west motion. Regional studies and prestimulation borehole breakouts find the maximum stress direction is vertical and maximum horizontal stress is approximately north–south. Fault geometry and focal mechanisms of microseismicity during the stimulation suggest that increased pressure from fluid injection predominantly caused changes in horizontal stress, consistent with predictions from numerical studies of stress change caused by fluid injection. At shallow depths, where previous studies suggest the difference between vertical and horizontal stress is lowest, injection appears to have rotated the direction of maximum stress from vertical to horizontal, resulting in strike–slip motion. At greater depth, vertical stress continued to be the dominant direction during the stimulation, but fault orientation indicates either reactivation of pre–existing fractures or rotation of the direction of maximum horizontal stress from approximately north–south to east–west.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Real-time equation-of-motion CC cumulant and CC Green’s function simulations of photoemission spectra of water and water dimer

Newly developed coupled-cluster (CC) methods enable simulations of ionization potentials and spectral functions of molecular systems in a wide range of energy scales ranging from core-binding to valence. This paper discusses the results obtained with the real-time equation-of-motion CC cumulant (RT-EOM-CC) approach and CC Green’s function (CCGF) approaches in applications to the water and water dimer molecules. Here, we compare the ionization potentials obtained with these methods for the valence region with the results obtained with the coupled-cluster with singles, doubles, and perturbative triples formulation as a difference of energies for N and N – 1 electron systems. All methods show good agreement with each other. They also agree well with the experiment with errors usually below 0.1 eV for the ionization potentials. We also analyze unique features of the spectral functions, associated with the position of satellite peaks, obtained with the RT-EOM-CC and CCGF methods employing single and double excitations, as a function of the monomer OH bond length and the proton transfer coordinate in the dimer. Finally, we analyze the impact of the basis set effects on the quality of calculated ionization potentials and find that the basis set effects are less pronounced for the augmented-type sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

High-Spin Superatom Stabilized by Dual Subshell Filling

Quantum confinement in small symmetric clusters leads to the bunching of electronic states into closely packed shells, enabling the classification of clusters with well-defined valences as superatoms. Like atoms, superatomic clusters with filled shells exhibit enhanced electronic stability. Here, we show that octahedral transition-metal chalcogenide clusters can achieve filled shell electronic configurations when they have 100 valence electrons in 50 orbitals or 114 valence electrons in 57 orbitals. While these stable clusters are intrinsically diamagnetic, we use our understanding of their electronic structures to theoretically predict that a cluster with 107 valence electrons would uniquely combine high stability and high-spin magnetic moment, attained by filling a majority subshell of 57 electrons and a minority subshell of 50 electrons. Further, we experimentally demonstrate this predicted stability, high-spin magnetic moment (S = 7/2), and fully delocalized electronic structure in a new cluster, [NEt 4 ] 5 [Fe 6 S 8 (CN) 6 ]. This work presents the first computational and experimental demonstration of the importance of dual subshell filling in transition-metal chalcogenide clusters.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Large-scale atomistic calculations of clusters in intense x-ray pulses

Here, we present the methodology of our recently developed Monte-Carlo/ Molecular-Dynamics method for studying the fundamental ultrafast dynamics induced by high-fluence, high-intensity x-ray free electron laser (XFEL) pulses in clusters. The quantum nature of the initiating ionization process is accounted for by a Monte Carlo method to calculate probabilities of electronic transitions, including photo absorption, inner-shell relaxation, photon scattering, electron collision and recombination dynamics, and thus track the transient electronic configurations explicitly. The freed electrons and ions are followed by classical particle trajectories using a molecular dynamics algorithm. These calculations reveal the surprising role of electron-ion recombination processes that lead to the development of nonuniform spatial charge density profiles in x-ray excited clusters over femtosecond timescales. In the high-intensity limit, it is important to include the recombination dynamics in the calculated scattering response even for a 2- fs pulse. We also demonstrate that our numerical codes and algorithms can make e!cient use of the computational power of massively parallel supercomputers to investigate the intense-field dynamics in systems with increasing complexity and size at the ultrafast timescale and in non-linear x-ray interaction regimes. In particular, picosecond trajectories of XFEL clusters with attosecond time resolution containing millions of particles can be e!ciently computed on upwards of 262,144 processes.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Quantifying Errors in Effective Cluster Interactions of Lattice Gas Cluster Expansions

The promise of lattice gas (LG) cluster expansions (CEs) is that they can describe a given system property to any level of accuracy since the orthogonal “cluster basis functions” span the complete space of available configurations. Such an approach can be constructed to an arbitrarily large surface of a finite number of distinct adsorption sites. Unfortunately, this is only true for the case of an ideal, fixed lattice decorated with components at precise lattice points (the lattice “sites”) with no distortions or relaxations subsequently allowed. Since most systems, and surfaces specifically, do not conform to such an ideal set of constraints, errors in LG CEs must be expected or CE convergence severely hampered. Beyond this, numerical errors in the provided data can complicate the proper construction of a truly predictive and/or physically significant CE. In this work, we show here how reliance on typical statistical tools like confidence intervals cannot be expected to provide an accurate representation of the uncertainty of effective cluster interactions (ECIs) in the CE due to the nature of the target ab initio data and the nature of CEs themselves. We develop a method for estimating these errors that does not rely on statistical assumptions about the model or data. We then use these ECI errors to quantify fundamental consequences on the uncertainty of ECIs in CEs built from O/Fe(100) data whose surface and adsorbates have been allowed to relax in a typical manner and from O/Fe(100) data whose surface and adsorbates are fixed in ideal lattice positions. We also quantify the effect of using a different density functional theory exchange–correlation functional, using these ECI errors to assess the significance in any deviations. In both cases, our method is shown to have remarkable utility in the quantification of errors in the ECIs of CEs. While we stick to the lattice gas convention in this work, the method is equally applicable to the Ising convention or, in principle, any linear model of sufficient complexity.

08 HYDROGEN↗

DOE OSTI2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II - Clustering Extremely High-Dimensional Grid-Based Data

Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Flexible Acyclic Polyol-Chloride Anion Complexes and Their Characterization by Photoelectron Spectroscopy and Variable Temperature Binding Constant Determinations

Flexible acyclic alcohols with 1–5 hydroxyl groups were bound to chloride anion and these complexes were interrogated by negative ion photoelectron spectroscopy and companion density functional theory computations. The resulting vertical detachment energies are reproduced on average to 0.10 eV by M06-2X/aug-cc-pVTZ predictions and range from 4.45 – 5.96 eV. These values are 0.84 – 2.35 eV larger than the adiabatic detachment energy of Cl– as a result of the larger hydrogen bond networks in the bigger polyols. Adiabatic detachment energies of the alcohol–Cl– clusters are more difficult to determine both experimentally and computationally. This is due to the large geometry changes that occur upon photodetachment and the large bond dissociation energy of H–Cl which enables the resulting chlorine atom to abstract a hydrogen from any of the methylene (CH2) or methine (CH) positions. Both ionic and non-ionic hydrogen bonds (i.e., OH•••Cl– and OH•••OH•••Cl–) form in the larger polyols complexes, and are found to be energetically comparable. Subtle structural differences, consequently can lead to the formation of different types of hydrogen bonds and maximizing the ionic ones is not always preferred. Solution equilibrium binding constants between the alcohols and tetrrabuylammonium chloride (TBACl) in acetonitrile at -24.2, 22.0, and 53.6 °C were also determined. The free energies of association are nearly identical for all of the substrates (i.e., ΔG° = -2.8 ± 0.7 kcal mol–1). Compensating enthalpy and entropy values reveal, contrary to expectation and the intrinsic gas-phase preferences, that the bigger systems with more hydroxyl groups are entropically favored and enthalpically disfavored relative to the smaller species. This suggests that more solvent molecules are released upon binding TBACl to alcohols with more hydroxyl groups and is consistent with the measured negative heat capacities. These quantities increase with molecular complexity of the substrate, however, contrary to common interpretation of these values.

anion molecular recognition↗

DOE OSTI2024

Adsorbate-induced adatom formation on Au-Cu bimetallic alloys and its possible consequences for CO 2 electroreduction

The adsorbate-induced formation of sub-nanometer clusters on transition-metal single crystals observed in previous high-pressure microscopic studies hinted at the in-situ formation of unique active sites even on large nanoparticle catalysts. We propose that the adatom formation energy can be used as an energetic descriptor for the initial step toward the adsorbate-induced metal-cluster formation process. This descriptor can be efficiently computed using density functional theory (DFT) calculations and applied for screening and identification of metal catalysts where this phenomenon may play an important role in generating active sites in-situ. As a proof of concept, here, we construct an adatom formation energy database for three Au x Cu y alloys (x:y = 3:1, 1:1, or 1:3) and eighteen adsorbates (H, C, N, O, F, S, Cl, Br, I, CH x , NH x (x = 1 – 3), CO, NO, and OH) commonly involved in catalytic reactions. The energetics of adatom formation were examined in all cases where the (111) terrace, (211) step-edge, and (874) kink were the sources of the adatom. We demonstrate that the presence of an adsorbate could alter not only the energetics for adatom formation but also the elemental nature of the preferred adatom being formed. Using our database, we identified promising systems which favor adsorbate-induced adatom formation under near-ambient conditions. Specifically, CO-induced adatom formation on all three Au-Cu alloy surfaces could occur under CO 2 electroreduction (CO 2 RR) conditions. This phenomenon offers a qualitative explanation for the experimentally observed CO 2 RR activity on Au-Cu alloy catalysts. As a result, our methodology offers an easily expandable and efficient approach for large-scale catalyst screening with regards to adatom/cluster formation under reaction conditions and provides insight into the possible nature of active sites on alloy catalysts from a novel perspective.

Active site↗

DOE OSTI2021

Quantitative Account of the Bonding Properties of a Rubredoxin Model Complex [Fe(SCH3)4]q, q = -2, -1, +2, +3

Iron-sulfur clusters play important roles in biology as parts of electron transfer chains and catalytic cofactors. Here, we report a detailed computational analysis of a structural model of the simplest natural iron-sulfur cluster of rubredoxin and its cationic counterparts. Specifically, we report results for the ground and low-lying electronically excited states of the complex [Fe(SCH3)4]2-/1-/2+/3+, using Multi-Reference (CASSCF, MRCISD), and Coupled Cluster [CCSD(T)] methodology in order to provide accurate adiabatic reduction energies, dissociation energies and insights into the bonding analysis. The nature of the Fe-S chemical bond and the magnitude of the ionization potentials in the anionic and cationic [Fe(SCH3)4] complexes offer a physical rationale for the relative stabilization, structure and speciation of these complexes. Anionic and cationic complexes present different types of chemical bonds: prevalently ionic in [Fe(SCH3)4]2-/1- complexes and covalent in [Fe(SCH3)4]2+/3+ complexes. The ionic bonds result in an energy gain for the transition [Fe(SCH3)4]2-®[Fe(SCH3)4]- (i.e., FeII®FeIII) of 1.5 eV, while the covalent bonds result in an energy loss for the transition [Fe(SCH3)4]2+®[Fe(SCH3)4]3+ of 16.6 eV, almost half of the IP of Fe2+. The ionic vs covalent bond character influences the Fe-S bond strength and length, i.e., ionic Fe-S bonds are longer than covalent ones by about 0.2 Å (for FeII) and 0.04 Å (for FeII). Finally, the average Fe-S heterolytic bond strength is 6.7 eV (FeII) and 14.6 (FeIII) eV at the RCCSD(T) level of theory.

Tzeli, Demeter↗

DOE OSTI2014

Nanothermodynamics of large iron clusters by means of a flat histogram Monte Carlo method

The thermodynamics of iron clusters of various sizes, from 76 to 2452 atoms, typical of the catalyst particles used for carbon nanotubes growth, has been explored by a flat histogram Monte Carlo (MC) algorithm (called the σ-mapping), developed by Soudan et al. [J. Chem. Phys. 135, 144109 (2011), Paper I]. This method provides the classical density of states, g{sub p}(E{sub p}) in the configurational space, in terms of the potential energy of the system, with good and well controlled convergence properties, particularly in the melting phase transition zone which is of interest in this work. To describe the system, an iron potential has been implemented, called “corrected EAM” (cEAM), which approximates the MEAM potential of Lee et al. [Phys. Rev. B 64, 184102 (2001)] with an accuracy better than 3 meV/at, and a five times larger computational speed. The main simplification concerns the angular dependence of the potential, with a small impact on accuracy, while the screening coefficients S{sub ij} are exactly computed with a fast algorithm. With this potential, ergodic explorations of the clusters can be performed efficiently in a reasonable computing time, at least in the upper half of the solid zone and above. Problems of ergodicity exist in the lower half of the solid zone but routes to overcome them are discussed. The solid-liquid (melting) phase transition temperature T{sub m} is plotted in terms of the cluster atom number N{sub at}. The standard N{sub at}{sup −1/3} linear dependence (Pawlow law) is observed for N{sub at} >300, allowing an extrapolation up to the bulk metal at 1940 ±50 K. For N{sub at} <150, a strong divergence is observed compared to the Pawlow law. The melting transition, which begins at the surface, is stated by a Lindemann-Berry index and an atomic density analysis. Several new features are obtained for the thermodynamics of cEAM clusters, compared to the Rydberg pair potential clusters studied in Paper I.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Stochastic economic dispatch of wind power under uncertainty using clustering-based extreme scenarios

Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. Here, this article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.

17 WIND ENERGY↗

DOE OSTI2024

Cluster expansion by transfer learning for phase stability predictions

Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. Furthermore, this algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations.

36 MATERIALS SCIENCE↗