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At least 343 records · Page 19

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DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗

DOE OSTI2020

Cool flame chemistry of diesel surrogate compounds: n -Decane, 2-methylnonane, 2,7-dimethyloctane, and n -butylcyclohexane

Elucidating the formation of combustion intermediates is crucial to validate reaction pathways, develop reaction mechanisms and examine kinetic modeling predictions. While high-temperature pyrolysis and oxidation intermediates of alkanes have been thoroughly studied, comprehensive analysis of cool flame intermediates from alkane autoxidation is lacking and challenging due to the complexity of intermediate species produced. Here, jet-stirred reactor autoxidation of four C 10 alkanes: n -decane, 2-methylnonane, 2,7-dimethyloctane, and n -butylcyclohexane, as model compounds of diesel fuel, was investigated from 500 to 630 K using synchrotron vacuum ultraviolet photoionization molecular beam mass spectrometry (SVUV-PIMS). Around 100 intermediates were detected for each fuel. The classes of molecular structures present during the autoxidation of the representative paraffinic functional groups in transport fuels, i.e., n -alkanes, branched alkanes, and cycloalkanes were established and were found to be similar from the oxidation of various alkanes. A theoretical approach was applied to estimate the photoionization cross sections of the intermediates with the same carbon skeleton as the reactants, e.g., alkene, alkenyl keto, cyclic ether, dione, keto-hydroperoxide, diketo-hydroperoxide, and keto-dihydroperoxide. These species are indicators of the first, second, and third O 2 addition reactions for the four C 10 hydrocarbons, as well as bimolecular reactions involving keto-hydroperoxides. Chemical kinetic models for the oxidation of these four fuels were examined by comparison against mole fraction of the reactants and final products obtained in additional experiments using gas chromatography analysis, as well as the detailed species pool and mole fractions of aforementioned seven types of intermediates measured by SVUV-PIMS. This works reveals that the models in the literature need to be improved, not only the prediction of the fuel reactivity and final products, but also the reaction network to predict the formation of many previous undetected intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Study of alkaline carbonate cooling to mitigate Ex-Vessel molten corium accidents

To mitigate adverse effects from molten corium following a reactor pressure vessel failure (RPVF), some new reactor designs employ a core catcher and a sacrificial material (SM), such as ceramic or concrete, to stabilize the molten corium and avoid containment breach. Existing reactors cannot easily be modified to include these SMs but could be modified to allow injectable cooling materials. Current reactor designs are limited to using water to stabilize the corium, but this can create other issues such as reaction of water with the concrete forming hydrogen gas. Here the novel SM proposed is a granular carbonate mineral that can be used in existing light water reactor plants. The granular carbonate will decompose when exposed to heat, inducing an endothermic reaction to quickly solidify the corium in place and producing a mineral oxide and carbon dioxide. Corium spreading is a complex process strongly influenced by coupled chemical reactions, including decay heat from the corium, phase change, and reactions between the concrete containment and available water. A recently completed Sandia National Laboratories laboratory directed research and development (LDRD) project focused on two research areas: experiments to demonstrate the feasibility of the novel SM concept, and modeling activities to determine the potential applications of the concept to actual nuclear plants. Small-scale experiments using lead oxide (PbO) as a surrogate for molten corium demonstrate that the reaction of the SM with molten PbO results in a fast solidification of the melt due to the endothermic carbonate decomposition reaction and the formation of open pore structures in the solidified PbO from CO 2 released during the decomposition. A simplified carbonate decomposition model was developed to predict thermal decomposition of carbonate mineral in contact with corium. This model was incorporated into MELCOR, a severe accident nuclear reactor code. A full-plant MELCOR simulation suggests that by the introduction of SM to the reactor cavity prior to RPVF ex-vessel accident progression, e.g., core-concrete interaction and core spreading on the containment floor, could be delayed by at least 15 h; this may be enough for additional accident management to be implemented to alleviate the situation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Impact of large-scale effects on mass transfer and concentration polarization in Reverse Osmosis membrane systems

Here, we present well-resolved computational fluid dynamics simulations of a large-scale reverse osmosis membrane-spacer configuration (1 m). Our computational model solves the flow and transport equations with variable solute-dependent properties. We utilize a high resolution computational mesh to resolve all relevant length scales associated with spacer-induced mixing and thin concentration boundary layers. An important contribution of this work is the development of a modified mass-transfer correlation that accounts for the development of the concentration boundary layer along the channel. A set of 2D axisymmetric simulations were performed for a spiral wound module layer with varying cross-flow conditions and spacer diameters which indicate a significant entrance length effect for concentration profile development at lower flow rates while mixing effects dominate at higher flow rates. The mass-transfer correlations at higher flow rates compare well with published correlations while a surrogate model for Sherwood number was obtained that depends on an additional similarity variable that accounted for entrance length effects at lower flow rates. Finally, a large-scale membrane-spacer design relevant to high-pressure reverse osmosis is studied with a non-uniform arrangement of spacers, which indicate a substantial saving in pressure drop (~40%) compared to traditional uniformly spaced pattern with minor variations (~2%) in concentration polarization, product water quality (~1%) and water recovery (~7%) compared to a uniform spacer pattern.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Real-time estimation of the electron temperature profile in DIII-D by leveraging neural-network surrogate models

Control of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high-performance plasmas. In order to reject disturbances in real time, feedback-control algorithms rely on accurate real-time knowledge of the plasma state. When diagnostics alone are insufficient, either because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Here, neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real-time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real-time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback-control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Process‐Oriented Calibration of a Turbulence Scheme in the DOE's Global Storm‐Resolving Model Using Machine Learning

A process‐oriented calibration framework is developed for the Simplified Higher‐Order Closure (SHOC) turbulence scheme in DOE's Simple Cloud Resolving E3SM Atmospheric Model (SCREAM). This framework leverages machine learning surrogates and observational constraints to efficiently calibrate SHOC adjustable parameters across two convective regimes: clear‐sky dry convective boundary layer and fair‐weather shallow cumulus clouds from ARM observations. We use perturbed‐parameter ensembles of a doubly periodic version of SCREAM to train surrogates and apply Markov Chain Monte Carlo sampling guided by cost functions based on benchmarking large‐eddy simulations and observations to identify optimized parameter sets that perform well in both regimes. The calibrated SHOC parameters substantially improve boundary‐layer turbulence and cloud boundaries, and modeled cloud fraction and radiative effects align better with observations than the default. These results demonstrate that combining multiple process‐specific convective regimes with machine‐learning surrogates can reduce parametric uncertainties and yield a model more faithful to cloud–turbulence interactions.

58 GEOSCIENCES↗

DOE OSTI2021

In vivo kinetics of SARS-CoV-2 infection and its relationship with a person’s infectiousness

Significance Quantifying the kinetics of SARS-CoV-2 infection and individual infectiousness is important for understanding SARS-CoV-2 transmission and evaluating intervention strategies. Here, we developed within-host models of SARS-CoV-2 infection, and by fitting them to clinical data, we estimated key within-host viral dynamic parameters. We also developed a mechanistic model for viral transmission and show that the logarithm of the viral load in the upper respiratory tract serves as an appropriate surrogate for a person’s infectiousness. Using data on how viral load changes during infection, we further evaluated the effectiveness of PCR and antigen-based testing strategies for averting transmission and identifying infected individuals.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Validation of Pronghorn for Natural-Circulation Molten Salt Loops

This paper presents the development and validation of a high-fidelity thermal-hydraulic model of a molten salt natural circulation flow loop, designed for integration within a digital twin framework. The study evaluates the performance of Idaho National Laboratory’s Pronghorn against experimental data from Texas A&M University Molten Salt Flow Loop (MSFL) four Hitec-salt test benchmark data. Natural circulation of high-Prandtl-number fluids exhibits complex, counter-intuitive flow patterns that make pointwise thermocouple readings unreliable. Experimental work at TAMU’s MSFL provides benchmark data, including flow visualization at a test-section and centerline steady-state temperature measurements along the loop. Validation includes four single-phase natural circulation test cases with Hitec salt. Key metrics include flow profile agreement and steady-state temperature accuracy. Pronghorn results for two-dimensional single-phase agree qualitatively with the experimental flow profile. This paper illustrates the importance of Computational Fluid Dynamics (CFD) in elucidating the behavior of high-Prandtl-number thermal-hydraulics, along with how misleading centerline temperature measurements can be. Pronghorn reproduces the axial and radial stratification that makes single thermocouple readings unreliable. Future research will focus on reduced-order modeling techniques to enable rapid simulation suitable for real-time digital twin applications. The validated cases provide a basis for developing reduced-order surrogates aimed at real-time digital-twin applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Insights into mixing of non-isothermal multi-polymer melts for complex plastics recycling

Catalytic recycling or upcycling of plastics is often limited not by catalyst performance, but by transport, arising from highly viscous, non-Newtonian polymer melts. In this work, we develop a reactor-scale framework that integrates rheological measurements, constitutive modeling, computational fluid dynamics (CFD), and experiments to quantify mixing, heat transfer, and dispersion in surrogate hydrocarbon melts representing mixed plastics systems. Temperature- and shear rate-dependent viscosity of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) is measured to create two surrogate polymers (PLD and PHD) that capture the dominant shear-thinning flow behavior while neglecting strong elastic effects, enabling tractable simulation of non-isothermal, polymer-melt mixing using a Carreau-Arrhenius generalized Newtonian framework. Three-dimensional CFD simulations are employed to evaluate impeller performance in PLD using mixing time, cavern volume, thermal uniformity, and interfacial area for regimes in which viscoelastic effects are not dominant. We show that magnetic stir bars commonly used in lab-scale studies produce large thermal gradients (~60 °C) and poor mixing, even under idealized power delivery and polymer flow conditions. In contrast, close-clearance anchor impellers achieve near-isothermal operation, reduce mixing times by up to 5×, and provide >90% active circulation volume. We further demonstrate that, at low pseudo-Deborah number (De*), motor power requirements can be predicted directly from shear rate-dependent rheology using the Carreau-Arrhenius framework, enabling rational selection of operating conditions. Extension to surrogate immiscible multi-polymer systems based on PLD and PHD shows that interfacial area is highly sensitive to operating conditions and impeller design, with coaxial anchor-turbine configurations enhancing dispersion by up to 4 × .

Close-clearance impellers↗

DOE OSTI2022

A comprehensive experimental and kinetic modeling study of di-isobutylene isomers: Part 1

We report di-isobutylene has received significant attention as a promising fuel blendstock, as it can be synthesized via biological routes and is a short-listed molecule from the Co-Optima initiative. Di-isobutylene is also popularly used as an alkene representative in multi-component surrogate models for engine studies of gasoline fuels. However, there is limited experimental data available in the literature for neat di-isobutylene under engine-like conditions. Hence, most existing di-isobutylene models have not been extensively validated, particularly at lower temperatures (< 1000 K). Most gasoline surrogate models include the di-isobutylene sub-mechanism published by Metcalfe et al. with little or no modification. The current study is undertaken to develop a detailed kinetic model for di-isobutylene and validate the model using a wide range of relevant experimental data. Part 1 of this study exclusively focuses on the low- to intermediate temperature kinetics of di-isobutylene. An upcoming part 2 discusses the high-temperature model development and validation of the relevant experimental targets. Ignition delay time measurements for the di-isobutylene isomers were performed at pressures ranging from 15 - 30 bar at equivalence ratios of 0.5, 1.0, and 2.0 diluted in air and in the temperature range 650 - 900 K using two independent rapid compression machine facilities. In addition, measurements of species identified during the oxidation of these isomers were performed in a jet-stirred reactor and in a rapid compression machine. A detailed kinetic model for the di-isobutylene isomers is developed to capture the wide range of new experimental targets. For the first time, a comprehensive low-temperature chemistry submodel is included. The differences in the important reaction pathways for the accurate prediction of the oxidation of the two DIB isomers are compared using reaction path analysis. In conclusion, the most sensitive reactions controlling the ignition delay times of the DIB isomers under the pressure and temperature conditions necessary for autoignition in engines are identified.

09 BIOMASS FUELS↗

DOE OSTI2021

Thermal Energy Storage Model Development within the Integrated Energy Systems Hybrid Repository

This publication details newly created energy storage models developed within the HYBRID Modelica repository as part of the Department of Energy Office of Nuclear Energy (DOE-NE) Integrated Energy Systems (IES) program, led by Idaho National Laboratory (INL). Model development to-date includes creation of dynamic systems-level models of concrete, latent heat, and packed-bed thermocline energy storage technologies for deployment in the IES-based HYBRID repository. Models are developed using the latest publicly available data and incorporate the possibility of control strategy inclusion for use with the existing IES modeling, analysis, and optimization toolset. Simulations showcase the abilities of each technology to cyclically charge and discharge when exposed to time-varying boundary conditions. In addition, low-level surrogate models for some of the thermal energy storage technologies were created using Python. These lower order Python files are much cheaper to run (i.e., computationally faster) and thus operate well when incorporated within the stochastic optimization problems run for the IES program. Moreover, economic data was collected for use within the INL-developed Framework for Optimization of ResourCes and Economics (FORCE). This information is incorporated within the FORCE platform. This work has resulted in creation of systems-level models for concrete, latent heat, and thermocline thermal energy storage systems with associated control systems. Now that these models are available, they can be utilized within different integrated energy park concepts to understand optimal system operation, control, and dispatching. Moreover, given the generic nature of the models, industrial partner technologies (e.g., Storworks Power, EnergyNest) can be quickly added to the repository using the existing models as a basis. Additional dynamic models for thermal energy storage concepts can be developed and added to the HYBRID repository as needed.

25 ENERGY STORAGE↗

DOE OSTI2021

Thermal Energy Transport in Oxide Nuclear Fuel

To efficiently capture the energy of the nuclear bond, advanced nuclear reactor concepts seek solid fuels that must withstand unprecedented temperature and radiation extremes. In these advanced fuels, thermal energy transport under irradiation is directly related to reactor performance as well as reactor safety. The science of thermal transport in nuclear fuel is a grand challenge as a result of both computational and experimental complexities. Here we provide a comprehensive review of thermal transport research on two actinide oxides: one currently in use in commercial nuclear reactors, uranium dioxide (UO 2 ), and one advanced fuel candidate material, thorium dioxide (ThO 2 ). In both materials, heat is carried by lattice waves or phonons. Crystalline defects caused by fission events effectively scatter phonons and lead to a degradation in fuel performance over time. Bolstered by new computational and experimental tools, researchers are now developing the foundational work necessary to accurately model and ultimately control thermal transport in advanced nuclear fuels. We begin by reviewing research aimed at understanding thermal transport in perfect single crystals. The absence of defects enables studies that focus on the fundamental aspects of phonon transport. Next, we review research that targets defect generation and evolution. Here the focus is on ion irradiation studies used as surrogates for damage caused by fission products. We end this review with a discussion of modeling and experimental efforts directed at predicting and validating mesoscale thermal transport in the presence of irradiation defects. While efforts in these research areas have been robust, challenging work remains in developing holistic tools to capture and predict thermal energy transport across widely varying environmental conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Data Evaluation of Actinide Cross Sections: 230 Th and 231 Th

A new evaluation of the ENDL cross section set for Thorium (Z=90) is developed using the TALYS statistical model cross section code. The primary goal of this effort is to produce an evaluation that attempts to match as closely as possible fission cross sections developed through surrogate reaction techniques on actinide targets 230 Th and 231 Th. This evaluation effort and the processing needed to render its results into data libraries is a necessary step in making the efforts of nuclear experimentalists useful to the broad community of researchers engaged in simulations of nuclear fusion for basic and applied science. Another aspect, verification and validation against various AGEX experiments, is also presented. The end-product is an updated library that includes the latest measurements of fission cross sections for comparison against those measured via traditional techniques. All the steps in the evaluation, processing, validation and verification, and library release are described in the following sections. For completeness, the appendix contains all the parameters used in the TALYS cross section evaluation for neutrons incident on 230 Th and 231 Th.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

ASCR Workshop Position Paper: Challenges and Opportunities in High Energy Physics

High energy particle physics and cosmology concern themselves with estimating fundamental parameters of nature, such as the masses and interactions of fundamental particles like the Higgs boson and the rate of expansion of the universe. In doing so, they analyze exabyte-scale datasets, some of the largest in all of science, and face many challenges in subsequent data analysis. These challenges are shared between the two disciplines, but we focus on particle physics to highlight one specific domain. In particle physics, the standard method for estimating parameters involves performing Monte Carlo (MC) integration as a function of both parameters of interest and nuisance parameters using an expensive simulator, counting the number of observed collision events (i.i.d. samples) from an experiment in the corresponding integration domains, and forming a Poisson likelihood function. This likelihood function is then used in a Frequentist manner to construct a maximum likelihood point estimate (MLE) and confidence set for the parameters. To sufficiently populate the high-dimensional integration domains, simulators consume billions of CPU-hours annually and produce hundreds of petabytes of intermediate output data. Several techniques have been developed to: optimize definitions of the integration domains so as to be maximally sensitive to a particular subset of parameters, efficiently estimate the integrals, and build robust surrogate models by interpolating between integral evaluations at different parameter points. One can view this whole endeavor as classical Simulation-Based Inference (SBI).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves

The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.

42 ENGINEERING↗

DOE OSTI2020

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

25 ENERGY STORAGE↗