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DOE OSTI2024

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. Here, this study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure–temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Thermal-Hydraulic Analysis of the AFIP-7 Irradiation Test in the Advanced Test Reactor - Oxide Growth Prediction and Correlation

Knowing the thickness of the oxide layer on the surface of aluminum fuel cladding is vitally important for predicting fuel temperature due to the low thermal conductivity of the oxide layer. Several correlation models for predicting oxide growth can be found in the literature. In previous research, the correlations were combined with heat transfer simulations in Abaqus, a finite element analysis code, to forecast the oxide growth. However, this approach requires heat transfer coefficients for modeling heat exchanges with the external flow field, and such coefficients were obtained through empirical equations. Since different empirical equations yield varying heat transfer coefficients, the cladding temperature and predicted oxide thickness both carry a high degree of uncertainty. Here, this research develops a new approach that integrates the fluid flow, fluid and solid heat transfer, and oxide growth correlation(s) into a single computational fluid dynamics model. We demonstrate this approach’s ability to predict oxide development on the AFIP-7 plates during two Advanced Test Reactor (ATR) irradiation cycles. The projected oxide thickness falls within the experimental measurements taken during post irradiation examination.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Determination of fundamental mechanical properties of biomass using the cubical triaxial tester to model biomass flow

The flowability of biomass is an indicator of how amenable biomass is to handling. The flowability measurements are often determined with empirical or tertiary experiments. This status quo impedes employing engineering principles to advance the design and operation of biomass handling systems. It is imperative to establish an experimental protocol minimizing empirical aspects of flowability characterization to account for the variability of biomass. This study demonstrates the operational principles of the large chamber cubical triaxial tester and the procedure of triaxial tests to quantify bulk flow behaviors of two milled biomass feedstocks, namely corn stover 2 mm (CS) and Douglas fir 1 mm (DF). The Mohr-Coulomb (MC), Drucker-Prager (DP) and modified Cam Clay (mCC) models are calibrated for both biomasses. Analysis indicates that CS will exhibit a cohesive flow with a larger cohesion coefficient of MC (3.8 ± 3.5 kPa) than DF (0.42 ± 0.9 kPa) and a larger d value of DP (6.9 kPa) than DF (0.0 kPa), respectively. CS exhibits a higher spring-back index of mCC (0.39 ± 0.05) than DF (0.27 ± 0.05) also suggesting handling issues, which agrees with the experiences in the industry. This study demonstrates the capability of a CTT in a quantitative investigation of biomass handling characteristics.

09 BIOMASS FUELS↗

DOE OSTI2024

Evolution of Superconducting-Transition Temperature with Superfluid Density and Conductivity in Pressurized Cuprate Superconductors

What factors fundamentally determine the value of superconducting transition temperature T c in high temperature superconductors has been the subject of intense debate. Following the establishment of an empirical law known as Homes' law, there is a growing consensus in the community that the T c value of the cuprate superconductors is closely linked to the superfluid density (ρ s ) of its ground state and the conductivity (σ) of its normal state. However, all the data supporting this empirical law (ρ s = AσT c ) have been obtained from the ambient-pressure superconductors. In this study, we present the first high-pressure results about the connection of the quantities of ρ s and σ with T c , through the studies on the Bi 1.74 Pb 0.38 Sr 1.88 CuO 6+δ and Bi 2 Sr 2 CaCu 2 O 8+δ , in which the value of their high-pressure resistivity (ρ = 1/σ) is achieved by adopting our newly established method, while the quantity of ρs is extracted using Homes' law. In conclusion, we highlight that the T c values are strongly linked to the joint response factors of magnetic field and electric field, i.e., ρ s and σ, respectively, implying that the physics determining T c is governed by the intrinsic electromagnetic fields of the system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Influence of discharge polarity on streamer breakdown criterion of ambient air in a non-uniform electric field

Streamer breakdown of atmospheric air with non-uniform dc electric field in a needle-to-plate electrode configuration is studied using a semi-analytic model and experimental measurements. A high voltage (either positive or negative) is applied to a hollow needle with 0.51 mm outer diameter and 0.25 mm inner diameter separated from a planar ground electrode by a gap distance of 0.1–1.4 cm. Breakdown voltages are recorded for both positive and negative discharge polarities. Empirical relations between the critical avalanche size for streamer breakdown and the gap distance are proposed. Using these empirical relations, a semi-analytic model based on Meek's criterion for streamer breakdown is developed to accurately predict the measured breakdown voltages. It is found that for pd > 380 Torr cm (or d > 0.5 cm at one atmosphere) streamer breakdown of ambient air occurs at a lower applied voltage for a positively biased needle compared to that with a negatively biased needle, referred as the polarity effect. For pd < 380 Torr cm breakdown is attained at a lower applied voltage with a negatively biased needle compared to that with a positively biased needle, and breakdown mode transits from the polarity effect to the so called inverted polarity effect.

42 ENGINEERING↗

DOE OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗

DOE OSTI2026

Numerical-heating effects in atmospheric pressure streamer discharges simulated with a PIC code

Artificial heating in plasma simulations is a well-known phenomenon which occurs when, among other things, the Debye length is poorly resolved by the simulation mesh. Here, in this work, the degree to which numerical-heating occurs during a simulation of a nanosecond atmospheric pressure streamer discharge is examined. The streamer is simulated using a two-dimensional finite-element, particle-in-cell code Empire, which uses direct simulation Monte Carlo for binary particle interactions. Initially, an estimate of the numerical-heating rate applied to Empire is performed using a simple plasma model. Second, a positive atmospheric pressure streamer discharge simulation is performed to study the effects of numerical heating on plasma density, electron temperature, and streamer velocity. The nominal Debye length is approximately 1 μm and the amount of numerical heating introduced in the simulation is varied by using mesh sizes ranging from 2 μm to 20 μm. A measurable numerical heating quantity is proposed that can be used to estimate the appropriate element size and quantify the numerical-heating that can be expected over the simulation time for an atmospheric pressure streamer. In conclusion while Δx/λ D violations can be an issue it is not likely to be an issue with streamer discharges that are temporally short and occur in environments where collision frequencies are high. This result validates the rationale of grid size choices for a large amount of previously published works where Δx/λ D violation was not clearly addressed. Primary finding of this work is that numerical heating is of minor concern for plasma simulations where electron–neutral collisions are numerous such that multiple collisions can occur within a single plasma period.

Nikic, Dejan [University of New Mexico, Albuquerqu↗

DOE OSTI2022

Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems

Abstract Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently outperform physics-based models for systems with few observable degrees of freedom; e.g. hydrological systems. Here, we provide an operator-theoretic explanation for this empirical success. To predict a partially-observed system’s future behavior with physics-based models, the missing degrees of freedom must be explicitly accounted for using data assimilation and model parametrization. Data-driven models, in contrast, employ delay-coordinate embeddings and their evolution under the Koopman operator to implicitly model the effects of the missing degrees of freedom. We describe in detail the statistical physics of partial observations underlying data-driven models using novel maximum entropy and maximum caliber measures. The resulting nonequilibrium Wiener projections applied to the Mori–Zwanzig formalism reveal how data-driven models may converge to the true dynamics of the observable degrees of freedom. Additionally, this framework shows how data-driven models infer the effects of unobserved degrees of freedom implicitly, in much the same way that physics models infer the effects explicitly. This provides a unified implicit-explicit modeling framework for predicting partially-observed systems, with hybrid physics-informed machine learning methods combining both implicit and explicit aspects.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

This paper describes a real-time capable algorithm for identifying the safe operating region around a tokamak operating point. The region is defined by a convex set of linear constraints, from which the distance of a point from a disruptive boundary can be calculated. The disruptivity of points is calculated from an empirical machine learning predictor that generates the likelihood of disruption. While the likelihood generated by such empirical models can be compared to a threshold to trigger a disruption mitigation system, the safe operating region calculation enables active optimization of the operating point to maintain a safe margin from disruptive boundaries. The proposed algorithm is tested using a random forest disruption predictor fit on data from DIII-D. The safe operating region identification algorithm is applied to historical data from DIII-D showing the evolution of disruptive boundaries and the potential impact of optimization of the operating point. Real-time relevant execution times are made possible by parallelizing many of the calculation steps and implementing the algorithm on a graphics processing unit. Lastly, a real-time capable algorithm for optimizing the target operating point within the identified constraints is also proposed and simulated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Predictive turbulence-driven flux model of scrape-off layer widths across confinement regimes in tokamaks

Reliable scrape-off layer (SOL) profile decay lengths predictions are needed to design and operate future tokamaks. The present manuscript describes a new model based on turbulent transport that is able to predict SOL widths for both L-mode and H-mode plasmas. The model is based upon the sheared-spectral filament paradigm (Peret et al (WEST Team) 2022 Phys. Plasmas 29 072306), however, incorporating the effects of thermal transport in order to calculate the parallel heat fluxes. The effects of magnetic shear and ExB shear on the cross-field transport are crucial to explain the shorter SOL decay lengths found in H-mode. The model is validated against a database of thousands of DIII-D L-mode and H-mode SOL profiles. We also calculate SOL decay length predictions in terms of plasma and engineer control parameters, which are in agreement with the multi-machine empirical H-mode scaling (Eich et al (ASDEX Upgrade Team and JET EFDA Contributors) 2013 Nucl. Fusion 53 093031), however, with an additional device geometry dependence. ITER SOL width predictions by the model are 3 times higher than the empirical scaling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Comment on ‘Carbon Intensity of corn ethanol in the United States: state of the science’

Abstract In their recent contribution, Scully et al (2021 Environ. Res. Lett. 16 043001) review and revise past life cycle assessments of corn-grain ethanol’s carbon (C) intensity to suggest that a current ‘central best estimate’ is considerably less than all prior estimates. Their conclusion emerges from selection and recombination of sector-specific greenhouse gas emission predictions from disparate studies in a way that disproportionately favors small values and optimistic assumptions without rigorous justification nor empirical support. Their revisions most profoundly reduce predicted land use change (LUC) emissions, for which they propose a central estimate that is roughly half the smallest comparable value they review (figure 1). This LUC estimate represents the midpoint of (a) values retained after filtering the predictions of past studies based on a set of unfounded criteria; and (b) a new estimate they generate for domestic (i.e. U.S.) LUC emissions. The filter the authors apply endorses a singular means of LUC assessment which they assert as the ‘best practice’ despite a recent unacknowledged review (Malins et al 2020 J. Clean. Prod. 258 120716) that shows this method almost certainly underestimates LUC. Moreover, their domestic C intensity estimate surprisingly suggests that cropland expansion newly sequesters soil C, counter to ecological theory and empirical evidence. These issues, among others, prove to grossly underestimate the C intensity of corn-grain ethanol and mischaracterize the state of our science at the risk of perversely affecting policy outcomes.

Spawn-Lee, Seth A. (ORCID:0000000188215345)↗

DOE OSTI2024

Seasonal variability and predictability of monsoon precipitation in Southern Africa

Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño–Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%–75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over SA. This makes them less effective at predicting monsoon precipitation than the empirical model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Filtering higher-order datasets

Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset's structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

The Interplay of Binary and Quantitative Structure on the Stability of Mutualistic Networks

Synopsis Understanding how the structure of biological systems impacts their resilience (broadly defined) is a recurring question across multiple levels of biological organization. In ecology, considerable effort has been devoted to understanding how the structure of interactions between species in ecological networks is linked to different broad resilience outcomes, especially local stability. Still, nearly all of that work has focused on interaction structure in presence-absence terms and has not investigated quantitative structure, i.e., the arrangement of interaction strengths in ecological networks. We investigated how the interplay between binary and quantitative structure impacts stability in mutualistic interaction networks (those in which species interactions are mutually beneficial), using community matrix approaches. We additionally examined the effects of network complexity and within-guild competition for context. In terms of structure, we focused on understanding the stability impacts of nestedness, a structure in which more-specialized species interact with smaller subsets of the same species that more-generalized species interact with. Most mutualistic networks in nature display binary nestedness, which is puzzling because both binary and quantitative nestedness are known to be destabilizing on their own. We found that quantitative network structure has important consequences for local stability. In more-complex networks, binary-nested structures were the most stable configurations, depending on the quantitative structures, but which quantitative structure was stabilizing depended on network complexity and competitive context. As complexity increases and in the absence of within-guild competition, the most stable configurations have a nested binary structure with a complementary (i.e., anti-nested) quantitative structure. In the presence of within-guild competition, however, the most stable networks are those with a nested binary structure and a nested quantitative structure. In other words, the impact of interaction overlap on community persistence is dependent on the competitive context. These results help to explain the prevalence of binary-nested structures in nature and underscore the need for future empirical work on quantitative structure.

Zoology↗

DOE OSTI2020

Generating synthetic cosmological data with GalSampler

As part of the effort to meet the needs of the Large Synoptic Survey Telescope Dark Energy Science Collaboration (LSST DESC) for accurate, realistically complex mock galaxy catalogues, in this work we have developed galsampler, an open-source python package that assists in generating large volumes of synthetic cosmological data. The key idea behind galsampler is to recast hydrodynamical simulations and semi-analytic models as physically motivated galaxy libraries. galsampler populates a new, larger volume halo catalogue with galaxies drawn from the baseline library; by using weighted sampling guided by empirical modelling techniques, galsampler inherits statistical accuracy from the empirical model and physically motivated complexity from the baseline library. We have recently used galsampler to produce the cosmoDC 2 extragalactic catalogue made for the LSST DESC Data Challenge 2 . Using cosmoDC 2 as a guiding example, we outline how galsampler can continue to support ongoing and near-future galaxy surveys such as the Dark Energy Survey, the Dark Energy Spectroscopic Instrument, WFIRST, and Euclid.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

The Universe at z > 10: predictions for JWST from the universemachine DR1

The James Webb Space Telescope (JWST) is expected to observe galaxies at z > 10 that are presently inaccessible. Here, we use a self-consistent empirical model, the universemachine, to generate mock galaxy catalogues and light-cones over the redshift range z = 0-15. These data include realistic galaxy properties (stellar masses, star formation rates, and UV luminosities), galaxy–halo relationships, and galaxy–galaxy clustering. Mock observables are also provided for different model parameters spanning observational uncertainties at z < 10. We predict that Cycle 1 JWST surveys will very likely detect galaxies with M* > 10 7 M⊙ and/or M 1500 < -17 out to at least z ~ 13.5. Number density uncertainties at z > 12 expand dramatically, so efforts to detect z > 12 galaxies will provide the most valuable constraints on galaxy formation models. Additionally, the faint-end slopes of the stellar mass/luminosity functions at a given mass/luminosity threshold steepen as redshift increases. This is because observable galaxies are hosted by haloes in the exponentially falling regime of the halo mass function at high redshifts. Hence, these faint-end slopes are robustly predicted to become shallower below current observable limits (M* < 10 7 M⊙ or M 1500 > -17). For reionization models, extrapolating luminosity functions with a constant faint-end slope from M 1500 = -17 down to M 1500 = -12 gives the most reasonable upper limit for the total UV luminosity and cosmic star formation rate up to z ~ 12. We compare to three other empirical models and one semi-analytic model, showing that the range of predicted observables from our approach encompasses predictions from other techniques. Public catalogues and light-cones for common fields are available online.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

The SAMI Galaxy Survey: stellar population and structural trends across the Fundamental Plane

ABSTRACT We study the Fundamental Plane (FP) for a volume- and luminosity-limited sample of 560 early-type galaxies from the SAMI survey. Using r-band sizes and luminosities from new multi-Gaussian expansion photometric measurements, and treating luminosity as the dependent variable, the FP has coefficients a = 1.294 ± 0.039, b = 0.912 ± 0.025, and zero-point c = 7.067 ± 0.078. We leverage the high signal-to-noise ratio of SAMI integral field spectroscopy, to determine how structural and stellar population observables affect the scatter about the FP. The FP residuals correlate most strongly (8σ significance) with luminosity-weighted simple stellar population (SSP) age. In contrast, the structural observables surface mass density, rotation-to-dispersion ratio, Sérsic index, and projected shape all show little or no significant correlation. We connect the FP residuals to the empirical relation between age (or stellar mass-to-light ratio Υ⋆ ) and surface mass density, the best predictor of SSP age amongst parameters based on FP observables. We show that the FP residuals (anti)correlate with the residuals of the relation between surface density and Υ⋆ . This correlation implies that part of the FP scatter is due to the broad age and Υ⋆ distribution at any given surface mass density. Using virial mass and Υ⋆, we construct a simulated FP and compare it to the observed FP. We find that, while the empirical relations between observed stellar population relations and FP observables are responsible for most (75 per cent) of the FP scatter, on their own they do not explain the observed tilt of the FP away from the virial plane.

D’Eugenio, Francesco↗