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At least 1,243 records · Page 69

Thermal-shock experiments for separate-effects validation of UO 2 fuel fracture models

Because of the important role that fracture plays in the behavior of ceramic UO2 fuel in a nuclear reactor environment, fracture models are a major component of fuel performance codes. As with any aspect of fuel performance, it is crucial to validate these fracture models against experimental data; however, obtaining well-controlled data for conditions representative of a reactor environment is difficult. Quenching is proposed here as a relatively simple approach for using a laboratory environment to achieve conditions that approximate those of a reactor environment. In this paper, an experimental apparatus containing a single instrumented fuel pellet in a sealed section of copper tubing is developed. It is then applied to a series of seven experiments in which the apparatus is first heated to a high temperature (580-680°C) by immersion in a molten salt bath, then quenched in a cold bath (-10-4 °C). Development of these experiments was guided by numerical simulations, and post-test simulations were performed to aid in understanding the experimental behavior and assessing the accuracy of the predictions of fracture initiation and propagation. In addition, similar experiments were performed on solid copper rods to provide temperature-dependent heat transfer coefficients for use in simulations of these experiments. Here, this study demonstrates that quenching is a viable approach for generating thermal gradients representative of those in prototypical light-water reactor conditions at powers of about 5–10 kW/m. Fracture is expected to begin at these power levels, and moderate amounts of radial and axial cracking was observed in these quenching tests.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

First-Principles Treatment of Vibrational Broadening in X-Ray Excited Valence Band Spectra for n-SrTiO 3 (001)

The valence band maximum (VBM) is an important quantity for semiconductors as it locates the Fermi level relative to the band edge. Accurate measurement of this quantity in near-surface regions of semiconductors by photoemission is a first step toward determining the electronic properties of heterostructures involving these materials. While extrapolating the leading edge of the valence band to the energy axis in photoemission spectra is a widely used way to find the VBM, this method can be ambiguous if the leading edge exhibits multiple slopes. Another way to determine the VBM is to fit the leading edge to an appropriately broadened, cross-section modulated theoretical density of states (DOS). Three kinds of broadening that should be included for maximum accuracy are those due to: (1) finite instrumental resolution, (2) valence hole lifetime, and (3) vibrational excitations. While steps (1) and (2) are straightforward to implement, (3) is more difficult because the appropriate amount of broadening is not known a priori. Here, we demonstrate that explicit inclusion of vibrational broadening using ab initio molecular dynamics facilitates accurate VBM determination for n-SrTiO 3 (001). The total DOS is constructed by summing time-averaged projections at elevated temperature onto s - , p - , and d orbitals for the constituent atoms and modulating with the associated photoemission cross sections. Subsequent convolutions of the total DOS, first with a Gaussian of width equal to the experimental energy resolution and second with a Lorentzian to simulate valence hole lifetime effects, yield line shapes that reproduce the experimental leading edges rather well. The VBM is then given by the energy at which the vibrationally broadened total DOS (prior to the convolutions) goes to zero. The VBMs generated by this method quantitatively agree with those resulting from extrapolating from the middle of the measured leading edge for SrTiO 3 .

36 MATERIALS SCIENCE↗

Verification of MOOSE/Bison's Heat Conduction Solver Using Combined Spatiotemporal Convergence Analysis

Bison is a computational physics code that uses the finite element method to model the thermo-mechanical response of nuclear fuel. Since Bison is used to inform high-consequence decisions, it is important that its computational results are reliable and predictive. One important step in assessing the reliability and predictive capabilities of a simulation tool is the verification process, which quantifies numerical errors in a discrete solution relative to the exact solution of the mathematical model. One step in the verification process—called code verification—ensures that the implemented numerical algorithm is a faithful representation of the underlying mathematical model, including partial differential or integral equations, initial and boundary conditions, and auxiliary relationships. In this paper, the code verification process is applied to spatiotemporal heat conduction problems in Bison. Simultaneous refinement of the discretization in space and time is employed to reveal any potential mistakes in the numerical algorithms for the interactions between the spatial and temporal components of the solution. For each verification problem, the correct spatial and temporal order of accuracy is demonstrated for both first- and second-order accurate finite elements and a variety of time-integration schemes. Furthermore, these results provide strong evidence that the Bison numerical algorithm for solving spatiotemporal problems reliably represents the underlying mathematical model in MOOSE. The selected test problems can also be used in other simulation tools that numerically solve for conduction or diffusion.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Automated Parameterized CFD Simulations of Phase-Change Material Embedded Heat Exchangers

Thermal storage can be implemented using Phase-Change Materials (PCM), which absorb significant latent heat with a relatively small temperature change. PCM phase-change processes are transient and are driven by thermal diffusion and natural convection – the latter, especially for melting process. Modeling and simulation of PCM heat exchangers is typically computationally intensive due to the relative complex time-dependent underlying physics. This paper presents a study investigating a single straight tube with circular transverse fins in the vertical orientation, using PCM’s with 37oC nominal melting temperature, leveraging automated parametric CFD simulations. The computational domain is reduced to 2D axisymmetric for a segment of the HX comprising a region in between two fins with constant wall temperature as boundary condition. This approach allows for faster evaluations of the transient thermal characteristics for a given design space of interest. Correlations and reduced order models (ROM’s) are developed. Results indicate that the ROM’s reproduce the CFD simulations with high accuracy for designs from the training data set, however, designs from cross-validation and test sets do not always have good agreement. The limited number of designs in the training set is a possible cause for the lack of robustness of the ROM’s and thus additional training designs will be added in future work. The ROM’s solve the simulations on average 500,000 times faster than CFD.

25 ENERGY STORAGE↗

Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017

Despite the rapid growth of online shopping and research interest in the relationship between online and in-store shopping, national-level modeling and investigation of the demand for online shopping with a prediction focus remain limited in the literature. Here, this paper differs from prior work and leverages two recent releases of the U.S. National Household Travel Survey (NHTS) data for 2009 and 2017 to develop machine learning (ML) models, specifically gradient boosting machine (GBM), for predicting household-level online shopping purchases. The NHTS data allow for not only conducting nationwide investigation but also at the level of households, which is more appropriate than at the individual level given the connected consumption and shopping needs of members in a household. We follow a systematic procedure for model development including employing Recursive Feature Elimination algorithm to select input variables (features) in order to reduce the risk of model overfitting and increase model explainability. Among several ML models, GBM is found to yield the best prediction accuracy. Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables. In doing so, two latest advances in machine learning techniques, namely Shapley value-based feature importance and Accumulated Local Effects plots, are adopted to overcome inherent drawbacks of the popular techniques in current ML modeling. The modeling and investigation are performed at the national level, with a number of findings obtained. The models developed and insights gained can be used for online shopping-related freight demand generation and may also be considered for evaluating the potential impact of relevant policies on online shopping demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Efficient Computation of Doppler-Broadened Elastic Scattering Kernel Moments Using Ladder-Operator Formulation

Anefficient routine for computing Legendre moments of the Doppler-broadened elastic scattering kernel, including resonance scattering effects, has been implemented in the ISOXML module of Griffin. Isotropic scattering in the center-of-mass system and the ideal gas model for target motion are assumed. A ladder-operator formulation is introduced to compute all Legendre moments from order 0 to N simultaneously, enabling near-linear scaling of computational cost with respect to the maximum Legendre order. A physics-based strategy for constructing outgoing energy grids has also been developed, in which a tailored base grid is combined with adaptive refinement to maintain accuracy while limiting the number of outgoing energy points. For energies between resonances, a constant cross-section model is employed to further reduce computational cost. In addition, a quantitative criterion is derived to determine isotope-wise cut-off incident energies based on a prescribed up-scattering probability coverage. For 238U, up to incident energies of approximately 75, 230, and 661 eV at 294, 900, and 2500 K (corresponding to a 2% up-scattering probability threshold), computation of P0 kernels requires 1–8 s and computation of P0–P5 kernels requires 0.4–4 min using a single thread, while maintaining 1–3% relative error in up-scattering probability. These results demonstrate that the proposed formulation enables accurate and computationally practical Doppler-broadened kernel generation for online multigroup cross-section production in Griffin.

Doppler-broadening↗

Environmental drivers of increased ecosystem respiration in a warming tundra

Abstract Arctic and alpine tundra ecosystems are large reservoirs of organic carbon 1,2 . Climate warming may stimulate ecosystem respiration and release carbon into the atmosphere 3,4 . The magnitude and persistency of this stimulation and the environmental mechanisms that drive its variation remain uncertain 5–7 . This hampers the accuracy of global land carbon–climate feedback projections 7,8 . Here we synthesize 136 datasets from 56 open-top chamber in situ warming experiments located at 28 arctic and alpine tundra sites which have been running for less than 1 year up to 25 years. We show that a mean rise of 1.4 °C [confidence interval (CI) 0.9–2.0 °C] in air and 0.4 °C [CI 0.2–0.7 °C] in soil temperature results in an increase in growing season ecosystem respiration by 30% [CI 22–38%] (n = 136). Our findings indicate that the stimulation of ecosystem respiration was due to increases in both plant-related and microbial respiration (n = 9) and continued for at least 25 years (n = 136). The magnitude of the warming effects on respiration was driven by variation in warming-induced changes in local soil conditions, that is, changes in total nitrogen concentration and pH and by context-dependent spatial variation in these conditions, in particular total nitrogen concentration and the carbon:nitrogen ratio. Tundra sites with stronger nitrogen limitations and sites in which warming had stimulated plant and microbial nutrient turnover seemed particularly sensitive in their respiration response to warming. The results highlight the importance of local soil conditions and warming-induced changes therein for future climatic impacts on respiration.

Science & Technology - Other Topics↗

Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD

A central challenge in high-throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and postprocessing techniques that can be used across all materials classes, while also managing accuracy-cost tradeoffs. To investigate the effects of these parameter choices, we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW), the Materials Project (MP), and the Open Quantum Materials Database (OQMD), and compare reported properties across each pair of databases for materials calculated using the same initial crystal structure. We find that HT-DFT formation energies and volumes are generally more reproducible than band gaps and total magnetizations; for instance, a notable fraction of records disagree on whether a material is metallic (up to 7%) or magnetic (up to 15%). The variance between calculated properties is as high as 0.105 eV/atom (median relative absolute difference, or MRAD, of 6%) for formation energy, 0.65 Å 3 / atom (MRAD of 4%) for volume, 0.21 eV (MRAD of 9%) for band gap, and 0.15 μB/ formula unit (MRAD of 8%) for total magnetization, comparable to the differences between DFT and experiment. Here, we trace some of the larger discrepancies to choices involving pseudopotentials, the DFT + U formalism, and elemental reference states, and argue that further standardization of HT-DFT would be beneficial to reproducibility.

36 MATERIALS SCIENCE↗

Classical shadows for quantum process tomography on near-term quantum computers

Quantum process tomography is a powerful tool for understanding quantum channels and characterizing the properties of quantum devices. Inspired by recent advances using classical shadows in quantum state tomography [H.-Y. Huang, R. Kueng, and J. Preskill, .], we have developed ShadowQPT, a classical shadow method for quantum process tomography. We introduce two related formulations with and without ancilla qubits. ShadowQPT stochastically reconstructs the Choi matrix of the device allowing for an classical evaluation of the device on arbitrary inputs with respect to arbitrary outputs. Using shadows, we then show how to compute overlaps, generate all k -weight reduced processes, and perform reconstruction via Hamiltonian learning. These latter two tasks are efficient for large systems as the number of quantum measurements needed scales only logarithmically with the number of qubits. A number of additional approximations and improvements are developed, including the use of a pair-factorized Clifford shadow and a series of postprocessing techniques that significantly enhance the accuracy for recovering the quantum channel. We have implemented ShadowQPT using both Pauli and Clifford measurements on the IonQ trapped ion quantum computer for quantum processes up to n = 4 qubits, and we achieved good performance. Published by the American Physical Society 2024

Levy, Ryan (ORCID:0000000349527156)↗

Magnesium isotopes of the bulk solar wind from Genesis diamond-like carbon films

–NASA’s Genesis Mission returned solar wind (SW) to the Earth for analysis to derive the composition of the solar photosphere from solar material. SW analyses control the precision of the derived solar compositions, but their ultimate accuracy is limited by the theoretical or empirical models of fractionation due to SW formation. Mg isotopes are “ground truth” for these models since, except for CAIs, planetary materials have a uniform Mg isotopic composition (within ≤1&) so any significant isotopic fractionation of SW Mg is primarily that of SW formation and subsequent acceleration through the corona. This study analyzed Mg isotopes in a bulk SW diamond-like carbon (DLC) film on silicon collector returned by the Genesis Mission. A novel data reduction technique was required to account for variable ion yield and instrumental mass fractionation (IMF) in the DLC. The resulting SW Mg fractionation relative to the DSM-3 laboratory standard was ( 14.4&, 30.2&) (4.1&, 5.5&), where the uncertainty is 2σ SE of the data combined with a 2.5& (total) error in the IMF determination. Two of the SW fractionation models considered generally agreed with our data. Their possible ramifications are discussed for O isotopes based on the CAI nebular composition of McKeegan et al. (2011).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Distances to Prompt Effects for a Nuclear Device

In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

Frost prediction using machine learning and deep neural network models

This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.

97 MATHEMATICS AND COMPUTING↗

The SRG/eROSITA All-Sky Survey: Optical identification and properties of galaxy clusters and groups in the western galactic hemisphere

The first SRG/eROSITA All-Sky Survey (eRASS1) provides the largest intracluster medium-selected galaxy cluster and group catalog covering the western Galactic hemisphere. Compared to samples selected purely on X-ray extent, the sample purity can be enhanced by identifying cluster candidates using optical and near-infrared data from the DESI Legacy Imaging Surveys. Using the red-sequence-based cluster findereROMaPPer, we measured individual photometric properties (redshiftz λ , richnessλ, optical center, and BCG position) for 12000 eRASS1 clusters over a sky area of 13 116 deg 2 , augmented by 247 cases identified by matching the candidates with known clusters from the literature. The median redshift of the identified eRASS1 sample isz= 0.31, with 10% of the clusters atz> 0.72. The photometric redshifts have an accuracy ofδz/(1 +z) ≲ 0.005 for 0.05 specand velocity dispersionσ) were measured a posteriori for a subsample of 3210 and 1499 eRASS1 clusters, respectively, using an extensive compilation of spectroscopic redshifts of galaxies from the literature. We infer that the primary eRASS1 sample has a purity of 86% and optical completeness >95% forz> 0.05. For these and further quality assessments of the eRASS1 identified catalog, we applied our identification method to a collection of galaxy cluster catalogs in the literature, as well as blindly on the full Legacy Surveys covering 24069 deg 2 . Using a combination of these cluster samples, we investigated the velocity dispersion-richness relation, finding that it scales with richness as log(λ norm ) = 2.401 × log(σ) − 5.074 with an intrinsic scatter ofδ in = 0.10 ± 0.01 dex. The primary product of our work is the identified eRASS1 cluster catalog with high purity and a well-defined X-ray selection process, opening the path for precise cosmological analyses presented in companion papers.

Astronomy & Astrophysics↗

Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics

The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models, such as those required to calculate accurate gravitational wave signals in numerical relativity simulations of binary neutron stars. This work explores the use of machine learning methods to speed up the recovery of primitives in relativistic hydrodynamics. Artificial neural networks are trained to replace either the interpolations of a tabulated equation of state or directly the conservative-to-primitive map. The application of these neural networks to simple benchmark problems shows that both approaches improve over traditional root finders with tabular equation-of-state and multi-dimensional interpolations. In particular, the neural networks for the conservative-to-primitive map accelerate the variable recovery by more than an order of magnitude over standard methods while maintaining accuracy. Neural networks are thus an interesting option to improve the speed and robustness of relativistic hydrodynamics algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗

Modeling electron temperature profiles in the pedestal with simple formulas for ETG transport

Abstract This paper reports on the refinement (building on (Hatch D.R. et al 2022 Phys. Plasmas 29 062501)) and application of simple formulas for electron heat transport from electron temperature gradient (ETG) driven turbulence in the pedestal. The formulas are improved by (1) improving the parameterization for certain key parameters and (2) carefully accounting for the impact of geometry and shaping in the underlying gyrokinetic simulation database. Comparisons with nonlinear gyrokinetic simulations of ETG transport in the MAST pedestal demonstrate the model’s applicability to spherical tokamaks in addition to standard aspect ratio tokamaks. We identify bounds for model applicability: the model is accurate in the steep gradient region, where the ETG turbulence is largely slab-like, but accuracy decreases as the temperature gradient becomes weaker in the pedestal top. We use the formula to model the electron temperature profile in the pedestal for four experimental scenarios while extensively varying input parameters to represent uncertainties. In all cases, the predicted electron temperature profile exhibits extreme sensitivity to separatrix temperature and density, which has implications for core-edge integration. The model reproduces the electron temperature profile for high η e = L n e / L T e scenarios but not for low η e scenarios in which microtearing modes (MTMs) have been identified. We develop a proof-of-concept model for MTM transport and explore the relative roles of ETG and MTM in setting the electron temperature profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabytescale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling.

Li, Chaojian [ORNL] (ORCID:0000000340309777)↗