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DOE OSTI2022

Transferability of data-driven, many-body models for CO 2 simulations in the vapor and liquid phases

Here, extending on the previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246–2257 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO 2 and systematically assess how the strength and anisotropy of the CO 2 –CO 2 interactions affect the models’ ability to predict vapor, liquid, and vapor–liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting one-body and two-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the charge model 5 scheme to determine the atomic charges and systematically scale the two-body energies to obtain more accurate descriptions of vapor, liquid, and vapor–liquid equilibrium properties. Challenges in model construction arise due to the anisotropic nature and small magnitude of the interaction energies in CO 2 , calling for the necessity of highly accurate descriptions of the multidimensional energy landscape of liquid CO 2 . These findings emphasize the key role played by the training set quality in the development of transferable, data-driven models, which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Validation of wind resource and energy production simulations for small wind turbines in the United States

Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

17 WIND ENERGY↗

DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗

DOE OSTI2022

Comparison of spherical harmonics method and discrete ordinates method for radiative transfer in a turbulent jet flame

Here, in this study, we systematically compared the accuracy and computational cost of two popular solution methods for the radiative transfer equation (RTE): the spherical harmonics method (P N ) and the discrete ordinates method (DOM). We first investigated convergence characteristics of different orders of P N and DOM in a series of 1D homogeneous configurations with varying optical thicknesses. Both solvers perform better for optically thicker cases. The accuracy of P N methods increases with its order, , but the gain in accuracy reduces with the increase in , i.e., improvement of P 7 over P 5 is less than that of P 3 over P 1 . This decreasing trend becomes more prominent as the optical thickness decreases. On the other hand, DOM’s accuracy increases almost linearly with the increase in the number of ordinates (or polar angles in this study) in all cases. While comparing the directional profile of radiative intensity, both solvers perform better when the radiative intensity is more isotropic. These solvers were then connected with a full spectrum k-distribution (FSK) spectral model and used to perform radiation-coupled simulations of a turbulent jet flame in an axi-symmetric cylindrical domain. Results obtained from P 1 to P 7 approximations for P N , and 2 x 4, 4 x 4, 4 x 8, 8 x 8 finite angles for DOM are compared with that from an optically thin model, and a reference solution from line-by-line (LBL) photon Monte Carlo (PMC) method. The choice of radiation solver shows a noticeable impact on the temperature distribution of the flame. The P N solvers lead to slightly higher radiant fractions and the DOM solvers lead to slightly lower radiant fractions than the PMC benchmark solution. Finally, the computational costs of each of these solvers are also reported and an intermittent evaluation / time blending scheme to improve the computational efficiency of radiation solvers in radiation-coupled simulations are also demonstrated.

42 ENGINEERING↗

DOE OSTI2023

From BEYONDPLANCK to COSMOGLOBE: Preliminary WMAP Q -band analysis

We present the first application of the COSMOGLOBE analysis framework by analyzing nine-year WMAP time-ordered observations that uses similar machinery to that of BEYONDPLANCK for the Planck Low Frequency Instrument (LFI). We analyzed only the Q-band (41 GHz) data and report on the low-level analysis process based on uncalibrated time-ordered data to calibrated maps. Most of the existing BEYONDPLANCK pipeline may be reused for WMAP analysis with minimal changes to the existing codebase. The main modification is the implementation of the same preconditioned biconjugate gradient mapmaker used by the WMAP team. Producing a single WMAP Q1-band sample requires 22 CPU-hrs, which is slightly more than the cost of a Planck 44 GHz sample of 17 CPU-hrs; this demonstrates that a full end-to-end Bayesian processing of the WMAP data is computationally feasible. In general, our recovered maps are very similar to the maps released by the WMAP team, although with two notable differences. In terms of temperature, we find a ~2 μK quadrupole difference that most likely is caused by different gain modeling, while in polarization we find a distinct 2.5 μK signal that has been previously referred to as poorly measured modes by the WMAP team. In the COSMOGLOBE processing, this pattern arises from temperature-to-polarization leakage from the coupling between the CMB Solar dipole, transmission imbalance, and sidelobes. No traces of this pattern are found in either the frequency map or TOD residual map, suggesting that the current processing has succeeded in modeling these poorly measured modes within the assumed parametric model by using Planck information to break the sky-synchronous degeneracies inherent in the WMAP scanning strategy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Detection of a MoSe{sub 2} secondary phase layer in CZTSe by spectroscopic ellipsometry

We demonstrate the application of Spectroscopic Ellipsometry (SE) for identification of secondary phase MoSe{sub 2} in polycrystalline Cu{sub 2}ZnSnSe{sub 4} (CZTSe) samples. A MoSe{sub 2} reference sample was analyzed, and its optical constants (ε{sub 1} and ε{sub 2}) were extracted by SE analysis. This dataset was implemented into an optical model for analyzing SE data from a glass/Mo/CZTSe sample containing MoSe{sub 2} at the back side of the absorber. We present results on the n and k values of CZTSe and show the extraction of the thickness of the secondary phase MoSe{sub 2} layer. Raman spectroscopy and scanning electron microscopy were applied to confirm the SE results.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years

The Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) together with the Modern-Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields were used to create a global emission dataset of biogenic VOCs available on a monthly basis for the time period of 1980 - 2010. This dataset is called MEGAN-MACC. The model estimated mean annual total BVOC emission of 760 Tg(C) yr1 consisting of isoprene (70%), monoterpenes (11%), methanol (6%), acetone (3%), sesquiterpenes (2.5%) and other BVOC species each contributing less than 2 %. Several sensitivity model runs were performed to study the impact of different model input and model settings on isoprene estimates and resulted in differences of * 17% of the reference isoprene total. A greater impact was observed for sensitivity run applying parameterization of soil moisture deficit that led to a 50% reduction of isoprene emissions on a global scale, most significantly in specific regions of Africa, South America and Australia. MEGAN-MACC estimates are comparable to results of previous studies. More detailed comparison with other isoprene in ventories indicated significant spatial and temporal differences between the datasets especially for Australia, Southeast Asia and South America. MEGAN-MACC estimates of isoprene and*-pinene showed a reasonable agreement with surface flux measurements in the Amazon andthe model was able to capture the seasonal variation of emissions in this region.

Sindelarova, K.↗

DOE OSTI2023

Measurement of electrons from beauty-hadron decays in $pp$ and Pb-Pb collisions at $\sqrt{s_{NN}}$ = $5.02$ TeV

The production of electrons from beauty-hadron decays was measured at midrapidity in proton-proton ($pp$) and central Pb-Pb collisions at center-of-mass energy per nucleon-nucleon pair $\sqrt{s_{NN}}$ = 5.02 TeV, using the ALICE detector at the LHC. The cross section measured in p p collisions in the transverse momentum interval $2$ < $p_T$ < $26$ $\mathrm{GeV}$ / $c$ was compared with models based on perturbative quantum chromodynamics calculations. The yield in the 10% most central Pb-Pb collisions, measured in the interval $2$ < $p_T$ < $26$ $\mathrm{GeV}$ / $c$, was used to compute the nuclear modification factor $R_{AA}$, extrapolating the $pp$ reference cross section to $p_T$ larger than 8 $\mathrm{GeV}$ / $c$. The measured $R_{AA}$ shows significant suppression of the yield of electrons from beauty-hadron decays at high $p_T$ and does not show a significant dependence above 8 $\mathrm{GeV}$ / $c$ within uncertainties. The results are described by several theoretical models based on different implementations of the interaction of heavy quarks with a quark-gluon plasma, which predict a smaller energy loss for beauty quarks compared to light and charm quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Toward Engineering of Solution Microenvironments for the CO 2 Reduction Reaction: Unraveling pH and Voltage Effects from a Combined Density-Functional–Continuum Theory

Engineering the electrolyte microenvironment represents an attractive route to tuning the selectivity of electrocatalytic reactions beyond catalyst composition and morphology. However, harnessing the full potential of this approach requires understanding the interplay between voltage, electrolyte composition, and adsorbate binding within the electrical double layer, which is absent from the usual theoretical approaches. In this study, we apply a recently developed density functional theory (DFT)–continuum approach based on the effective screening medium method and reference interaction site model (ESM–RISM) to explore electrolyte effects with an enhanced description of the electrochemical interface. Furthermore, applying this method to the binding of CO adsorbates in potassium-containing electrolytes on copper, a problem of direct relevance to CO 2 electroreduction to value-added products, we show that the interdependence of voltage and pH leads to an unexpected change in adsorption site preference on Cu(001) terraces. Our findings highlight the often-overlooked importance of the electrical double-layer structure for predicting catalyst operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Drift kinetic effects on plasma response to resonant magnetic perturbation for EU DEMO design

Abstract A systematic investigation of the plasma response to an externally applied resonant magnetic perturbation (RMP) field, for the purpose of controlling edge localized modes, is carried out for an EU DEMO reference plasma. Particular emphasis is placed on the role of kinetic effects associated with both thermal particles and fusion-born alphas. The single fluid, resistive model predicts a large peak amplification of the n = 1 ( n is the toroidal mode number) plasma response to the target equilibrium, which is found to be close to the Troyon no-wall limit. A more advanced response model, including kinetic resonances between the RMP perturbation and drift motions of thermal and energetic particles, on the other hand, finds a strong suppression of the n = 1 field amplification. A major role is played by the precessional drift resonance of fusion-born alphas. A strong parallel sound wave damping model is found to well reproduce the full kinetic response results for the DEMO plasma, in terms of both the resonant field response amplitude and the plasma displacement. Finally, both fluid and kinetic models produce similar responses for the n = 2 and 3 RMP fields for the considered DEMO plasma, whilst kinetic effects again become important for the n = 4 RMP due to proximity of the reference plasma to the no-wall limit for the n = 4 ideal kink instability.

Physics↗

DOE OSTI2023

HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Abstract Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km 2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

Science & Technology - Other Topics↗

DOE OSTI2016

Assessing the distinguishable cluster approximation based on the triple bond-breaking in the nitrogen molecule

Obtaining the correct potential energy curves for the dissociation of multiple bonds is a challenging problem for ab initio methods which are affected by the choice of a spin-restricted reference function. Coupled cluster (CC) methods such as CCSD (coupled cluster singles and doubles model) and CCSD(T) (CCSD + perturbative triples) correctly predict the geometry and properties at equilibrium but the process of bond dissociation, particularly when more than one bond is simultaneously broken, is much more complicated. New modifications of CC theory suggest that the deleterious role of the reference function can be diminished, provided a particular subset of terms is retained in the CC equations. The Distinguishable Cluster (DC) approach of Kats and Manby [J. Chem. Phys. 139, 021102 (2013)], seemingly overcomes the deficiencies for some bond-dissociation problems and might be of use in quasi-degenerate situations in general. DC along with other approximate coupled cluster methods such as ACCD (approximate coupled cluster doubles), ACP-D45, ACP-D14, 2CC, and pCCSD(α, β) (all defined in text) falls under a category of methods that are basically obtained by the deletion of some quadratic terms in the double excitation amplitude equation for CCD/CCSD (coupled cluster doubles model/coupled cluster singles and doubles model). Here these approximate methods, particularly those based on the DC approach, are studied in detail for the nitrogen molecule bond-breaking. The N{sub 2} problem is further addressed with conventional single reference methods but based on spatial symmetry-broken restricted Hartree–Fock (HF) solutions to assess the use of these references for correlated calculations in the situation where CC methods using fully symmetry adapted SCF solutions fail. The distinguishable cluster method is generalized: 1) to different orbitals for different spins (unrestricted HF based DCD and DCSD), 2) by adding triples correction perturbatively (DCSD(T)) and iteratively (DCSDT-n), and 3) via an excited state approximation through the equation of motion (EOM) approach (EOM-DCD, EOM-DCSD). The EOM-CC method is used to identify lower-energy CC solutions to overcome singularities in the CC potential energy curves. It is also shown that UHF based CC and DC methods behave very similarly in bond-breaking of N{sub 2}, and that using spatially broken but spin preserving SCF references makes the CCSD solutions better than those for DCSD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Static and dynamic polarizability for C{sup 2+} in Rydberg states

This work presents results from a non-perturbative calculation of dynamic polarizability of C III ions in 1s{sup 2}2sns ({sup 1}S{sup e}) Rydberg states. We employ a two-state model for dressed atomic states to investigate the effect of the frequency-dependent polarizability of optically dressed 1s{sup 2}2sns({sup 1}S{sup e}) states (n = 5 − 12) on transitions to nearby states (1s{sup 2}2pns({sup 1}P{sub 1}{sup o})). Our model calculation results indicate that the resonance structure of the polarizabilities is entirely captured by the transition terms whereas the free electron polarizability only provides a smooth background. The resonance structure is evident in the plots and the widths increase with increasing principal quantum number. This work refers to highly excited 1s{sup 2}2sns ({sup 1}S) Rydberg states, embedded in the electric dipole field of the 2s – 2p core transition in Li-like C{sup 3+} ion. The contributions of the individual transitions to the static polarizabilities of these Rydberg states are obtained from the use of the sum-over-state method. To this aim, both the C{sup 2+} ground state and the C{sup 3+} target state energies have been carefully calculated based on the configuration interactions method implemented in the General-purpose Relativistic Atomic Structure Package. Agreement is reasonably good with existing data wherever available. These results are believed to be the first such values for this system and will be important for ionic spectroscopy and plasma diagnostics.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Setup and verification of a SCALE/KENO platform for generic FHR benchmark calculations

The work presented in this article is preliminary to downstream analysis of a generic fluoride salt-cooled high-temperature reactor (gFHR) core performed by the University of Tennessee in collaboration with Kairos Power (KP). A Monte Carlo transport model of the publicly available gFHR equilibrium core is developed in SCALE/KENO with multigroup energy treatment. Several output quantities of interest are used to verify the simulations against a benchmark model developed by KP using the continuous energy Monte Carlo code Serpent 2. Good agreement is seen in flux and fission rate profiles with a maximum relative difference of 1.4% and 2.8% respectively. Furthermore, an effective multiplication factor bias of 44 pcm was observed between the two simulations. The fuel temperature reactivity coefficient calculated with SCALE is within uncertainty to the reference model. This verification acts as a publicly reproducible benchmark for the gFHR in SCALE/KENO. A simplified depletion model is also presented where a single fuel pebble is depleted to discharge burnup through the equilibrium core while the equilibrium core is assumed to be invariant. This method produces results that intercept the equilibrium core concentrations in every case, however, an interesting artifact of this particular depletion model is uncovered. The phenomenon is shown to be a fundamental feature of the differential rate equations and inspires questions about how this system behaves when the time evolution of the equilibrium core is considered. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Comparison of removal and spatial mark‐resight models for estimating wild pig density

Density estimation is critical to effectively manage invasive species and elucidate areas of highest concern. For wild pigs (Sus scrofa), the ability to estimate density is complicated because of their variable home range sizes and social structure. Common methods for estimating density (e.g., mark-recapture) may be unsuitable in management applications because additional data needs to be collected before and after management. Removal models offer a suitable alternative to estimate density changes following management and can be applied broadly across areas where management of wild pigs is ongoing. We collected wild pig removal and camera trap data from 25 private properties ranging in size from approximately 0.5 km 2 to 95 km 2 across 3 ecoregions in South Carolina, USA, from 2020–2023. We compared factors affecting consistency and precision of property-level density estimates between removal and spatial mark-resight (SMR) models. In general, excluding 1 large outlier, density estimates from removal models were between 0.60 and 15.85 wild pigs/km 2 (median = 5.34) with a median coefficient of variation (CV) of 0.76 and 95% confidence intervals for the CV between 0.70 and 0.94. Similarly, excluding 1 large outlier, density estimates from SMR were between 0.22 and 30.97 wild pigs/km 2 (median = 5.48) with a median CV of 0.39 and 95% confidence intervals for the CV between 0.38 and 1.20. We found the precision of removal models was affected primarily by the number of wild pigs dispatched in the removal period (3 months) and the ecoregion in which they were removed. None of the covariates, including the number of recaptures (a corresponding measure of sample size), influenced precision of the SMR models, although recaptures did influence the density estimates. At the individual property level, density estimates from our 2 estimators were dissimilar from each other in approximately 80% of instances, although none of the covariates we examined influenced dissimilarity. Our results provide unique insight into how sample size affects density estimates using 2 common methods and into novel SMR models that incorporate both marked and unmarked detections. In addition, the density estimates in this study can be used as a reference for wild pig densities in common land cover types throughout the southeastern United States.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

The Economic Impacts of High Wind Penetration Scenarios in the United States

The U.S. electric sector is experiencing rapid increases in renewable generation with an expectation of continued growth. We examine the impacts of increased wind electricity on the U.S. economy using a hybrid model that links a detailed electric sector model (the National Renewable Energy Laboratory's Regional Energy Deployment System [ReEDS]) with a computable general equilibrium model of the U.S. economy (the Massachusetts Institute of Technology's U.S. Regional Energy Policy [USREP] model). Increasing wind capacity displaces fossil fuels for electricity generation, which depresses fossil fuel prices and reduces economy-wide CO 2 emissions. Competitive wind deployment in the reference scenario achieves these outcomes with lower electricity prices than a scenario with wind capacity fixed at 2016 levels. Lower fossil fuel and electricity prices benefit low-income households, but the dominant economic impacts are driven by increased electric sector investment and capital returns, which primarily benefit those with higher incomes. Overall, cost-competitive wind provides benefits to the U.S. economy that are initially low but rise beyond 2030 to achieve cumulative welfare and GDP improvements of $110 and $111 billion through 2050. Prescribing additional wind deployment (meeting 35% of demand by 2050) increases post-2030 electricity prices but not more so than keeping wind capacity fixed at 2016 levels. The negative effect of higher prices on welfare, however, is outweighed by higher returns from the additional wind investment. While these changes favor higher income classes, we find economy-wide benefits through 2040. These benefits diminish by 2050, but the cumulative additional gains through 2050, beyond the reference case, are $192 billion in welfare and $150 billion in GDP.

17 WIND ENERGY↗

DOE OSTI2025

Generative diffusion model surrogates for mechanistic agent-based biological models

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

High-Entropy Alloys (HEA): Fundamentals and Applications

This book provides a systematic and comprehensive description of high-entropy alloys (HEAs). The authors summarize key properties of HEAs from the perspective of both fundamental understanding and applications, which are supported by in-depth analyses. The book also contains computational modeling in tackling HEAs, which help elucidate the formation mechanisms and properties of HEAs from various length and time scales.<br>High-entropy alloys (HEAs) [1,2] loosely refer to multi-principal-element solid solution alloys due to their high configurational entropy, in contrast to traditional alloys, which focus on the edge or corner of phase diagrams with one principal component. The unique compositions and the resulting attractive properties of HEAs have stimulated growing research interest due to scientific curiosity and potential industrial applications [3,4]. In order to balance the properties for targeted applications, the microstructure of HEAs can be a single phase or multiphases, and traditional physical metallurgy principles have been applied to develop a variety of HEAs, including high-entropy stainless steels, high-entropy superalloys, high-entropy refractory alloys, high-entropy light-weight alloys, high-entropy oxides, high-entropy metallic compounds, etc. However, great challenges still remain in the fundamental understanding of HEAs formation and their properties, and potential high-performance HEAs are yet to be explored. This special issue is meant to collect contributions from authors working in the various fields of HEAs to timely disseminate the rapid progress in this fascinating and expanding class of advanced materials.

Gao, Michael↗