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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

A general approach to develop reduced order models for simulation of solid oxide fuel cell stacks

A reduced order modeling approach based on response surface techniques was developed for solid oxide fuel cell stacks. This approach creates a numerical model that can quickly compute desired performance variables of interest for a stack based on its input parameter set. The approach carefully samples the multidimensional design space based on the input parameter ranges, evaluates a detailed stack model at each of the sampled points, and performs regression for selected performance variables of interest to determine the responsive surfaces. After error analysis to ensure that sufficient accuracy is established for the response surfaces, they are then implemented in a calculator module for system-level studies. The benefit of this modeling approach is that it is sufficiently fast for integration with system modeling software and simulation of fuel cell-based power systems while still providing high fidelity information about the internal distributions of key variables. This paper describes the sampling, regression, sensitivity, error, and principal component analyses to identify the applicable methods for simulating a planar fuel cell stack.

Pan, Wenxiao↗
DOE OSTI2024

Source term analysis of FeCrAl accident tolerant fuel using MELCOR

It has been established that incremental improvements in beyond design basis accident performance can be achieved through accident tolerant fuel (ATF). However, they have the potential to recover margin with respect to conventional fuel and therefore enhance plant economics through uprate or cycle length increase. To realize this potential, it is necessary to quantify the reduction in source term due to use of ATF, and correspondingly how this is affected by increasing the cycle length and/or burnup. This requires development of a risk informed analysis methodology for ATF under high burnup conditions, which is being developed within LWRS. To this end, a MELCOR simulation of FeCrAl ATF in a recovered Large Break LOCA (LBLOCA) scenario has been developed, using a model based on the Zion Pressurized Water Reactor (PWR). The new user defined material capability, along with the inclusion of detailed neutronics- and depletion-derived parameters such as core power distribution, decay heat behavior, and fission product inventories, allows a more detailed simulation of FeCrAl clad material properties and behavior than has previously been possible using MELCOR. These detailed FeCrAl results were compared with a zircaloy clad model to investigate the differences between the two clad types and quantify the benefits of FeCrAl ATF with an 18-month cycle. Next, the fuel cycle with ATF was extended to 24 months, to determine whether any additional safety margin provided by FeCrAl ATF could be leveraged to implement high burnup FeCrAl-clad fuel while retaining the same operating and safety limits as current zircaloy-clad fuel. For the particular scenario analyzed, the delay to fuel failure from using ATF was of the same order as the LOCA recovery time, and hence significant in reducing fission product release, with the 24-month FeCrAl core performing better than the 18-month zircaloy core. It is noted that for other transients, the reduction in release due to using FeCrAl may be less significant. Furthermore, the material model developed here can be used in such further studies in support of determining the overall source term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2009

Biophysics Representation of the Two-Hit Model of Alzheimer's Disease for the Exploration of Late CNS Risks from Space Radiation

A concern for long-term space travel outside the Earth s magnetic field is the late effects to the central nervous system (CNS) from galactic cosmic ray (GCR) or solar particle events (SPE). Human epidemiology data is severely limited for making CNS risk estimates and it is not clear such effects occur following low LET exposures. We are developing systems biology models based on biological information on specific diseases, and experimental data for proton and heavy ion radiation. A two-hit model of Alzheimer s disease (AD) has been proposed by Zhu et al.(1), which is the framework of our model. Of importance is that over 50% of the US population over the age of 75-y have mild to severe forms of AD. Therefore we recommend that risk assessment for a potential AD risk from space radiation should focus on the projection of an earlier age of onset of AD and the prevention of this possible acceleration through countermeasures. In the two-hit model, oxidative stress and aberrant cell cycle-related abnormalities leading to amyloid-beta plaques and neurofibrillary tangles are necessary and invariant steps in AD. We have formulated a stochastic cell kinetics model of the two-hit AD model. In our model a population of neuronal cells is allowed to undergo renewal through neurogenesis and is susceptible to oxidative stress or cell cycle abnormalities with age-specific accumulation of damage. Baseline rates are fitted to AD population data for specific ages, gender, and for persons with an apolipoprotein 4 allele. We then explore how low LET or heavy ions may increase either of the two-hits or neurogenesis either through persistent oxidative stress, direct mutation, or through changes to the micro-environment, and suggest possible ways to develop accurate quantitative estimates of these processes for predicting AD risks following long-term space travel.

Cucinotta, Francis A.↗
DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO 2 – Reservoir oil minimum miscibility pressure prediction

Carbon dioxide (CO 2 ) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO 2 -EOR process is highly dependent on the minimum miscibility pressure (MMP), estimating this parameter is critical to design of the CO 2 injection process. Traditional empirical methods to test the CO 2 -oil MMP are time consuming and expensive; derived correlations are fast but not accurate. Therefore, an efficient and reliable method to determine MMP is beneficial. Here in this study, a mixed kernels function (MKF) based support vector regression (SVR) model was developed and used to predict the MMP for both pure and impure CO 2 injection cases. Four parameters were chosen as input parameters: (1) reservoir temperature; (2) average critical temperature; (3) molecular weight of pentane plus (C 5+ ) fraction of crude oil, and; (4) the ratio of volatile components to intermediate components in crude oil. MMP was selected as the desired output parameter to train and test this newly developed model. The performance of basic kernels function based SVR model is compared with that of this newly developed MKF-SVR model. The well-trained MFK-SVR was compared with three well-established published correlations, demonstrated the highest correlation coefficient (R of 0.9381), lowest root mean square error (RMSE of 1.9151), smallest average absolute error (AAE of 1.1406) and maximum absolute error (MAE of 4.6291). We believe that the proposed MFK-SVM model is a more reliable and stable regression model to predict MMP. In addition, a sensitivity analysis was conducted to evaluate the physical correctness. It indicates that the predicted results from the newly developed model are in excellent agreement with previous empirical work.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Developing a nonparametric functional calibration framework for physics-based models

Due to the cost, complexity, or difficulty in solely carrying out experiments in the effort to better understand physical phenomenon, researchers and engineers often work towards the development of computer models to represent the systems under analysis. The importance of these models in accurately representing the systems that they are designed to emulate has become a focus in numerous fields, including medicine, energy, defense, and manufacturing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

Hypovolemia Induced Orthostatic Hypotension in Presyncopal Astronauts and Normal Subjects Relates to Hypo-Sympathetic Responsiveness

Circulating blood volume is reduced during spaceflight, leaving astronauts hemodynamically compromised after landing. Because of this hypovolemia, crew members are able to withstand a postflight 10 minute upright tilt test only if they are able to mount a hyper-sympathetic response. Previous work from this laboratory has shown that about 30% of astronauts, primarily female, have postflight sympathetic responses to tilt that are equal to or less than their preflight responses and thus, they become presyncopal. Part of the mission of the cardiovascular lab at the Johnson Space Center is to identify susceptible crewmembers before flight so that individualized countermeasures can be prescribed. The goal of this study was to develop a ground based model of hypovolemia that could be used for this purpose We tested the hypothesis that hypovolemia alone, in the absence of spaceflight, would reproduce the landing day rate of presyncope during upright tilt in normal volunteers. Further, we hypothesized that, during hypovolemia, subjects who had sympathetic responses that were equal to or less than their normovolemic responses would become presyncopal during upright tilt tests. We studied 20 subjects, 13 male and 7 female, on two separate occasions: during normovolemia and hypovolemia. We induced hypovolemia with intravenous furosemide 40 hours prior to the experiment day, followed by a 10MEq Na diet. On the normovolemia and hypovolemia test days, plasma volume, tilt tolerance and supine and standing arterial pressure, heart rate and plasma norepinephrine levels were measured. A two factor, repeated measures analysis of variance was performed to examine the differences between group (presyncopal vs. non-presyncopal) and day (normovolemia vs. hypovolemia) effects. There were no differences in baseline arterial pressure between normovolemia and hypovolemia or between presyncopal and non-presyncopal groups, but heart rates were higher with hypovolemia in both groups (presyncopal: 70 5 bpm vs. 63 3 bpm, P = 0.003, non-presyncopal: 59 2 bpm vs. 52 2 bpm, P = 0.003). Similar to patterns reported after flight, non-presyncopal subjects had greater norepinephrine responses to tilt during hypovolemia compared to normovolemia (580 79 vs. 298 37 pg/ml, P < 0.05), but presyncopal subjects did not (180 44 vs. 145 32 pg/ml, P = NS). This new model has the potential to accelerate the development of countermeasures and save flight resources. It can be used to identify astronauts who will become presyncopal on landing day, so that prospective, individualized countermeasures can be developed. In addition, it can also be used to screen candidate countermeasures prior to requests for bed rest or inflight resources.

Meck, Janice V.↗
DOE OSTI2016

Nanostructured Fe-Cr Alloys for Advanced Nuclear Energy Applications

We have completed research on the grain-size stabilization of model nanostructured Fe14Cr base alloys at high temperatures by the addition of non-equilibrium solutes. Fe14Cr base alloys are representative for nuclear reactor applications. The neutron flux in a nuclear reactor will generate He atoms that coalesce to form He bubbles. These can lead to premature failure of the reactor components, limiting their lifetime and increasing the cost and capacity for power generation. In order to mitigate such failures, Fe14Cr base alloys have been processed to contain very small nano-size oxide particles (less than 10 nm in size) that trap He atoms and reduce bubble formation. Theoretical and experimental results indicate that the grain boundaries can also be very effective traps for He atoms and bubble formation. An optimum grain size will be less than 100 nm, ie., nanocrystalline alloys must be used. Powder metallurgy methods based on high-energy ball milling can produce Fe-Cr base nanocrystalline alloys that are suitable for nuclear energy applications. The problem with nanocrystalline alloys is that excess grain-boundary energy will cause grains to grow at higher temperatures and their propensity for He trapping will be lost. The nano-size oxide particles in current generation nuclear alloys provide some grain size stabilization by reducing grain-boundary mobility (Zener pinning – a kinetic effect). However the current mitigation strategy minimizing bubble formation is based primarily on He trapping by nano-size oxide particles. An alternate approach to nanoscale grain size stabilization has been proposed. This is based on the addition of small amounts of atoms that are large compared to the base alloy. At higher temperatures these will diffuse to the grain boundaries and will produce an equilibrium state for the grain size at higher temperatures (thermodynamic stabilization – an equilibrium effect). This would be preferred compared to a kinetic effect, which is not based on an equilibrium state. The PI and coworkers have developed thermodynamic-based models that can be used to select appropriate solute additions to Fe14Cr base alloys to achieve a contribution to grain-size stabilization and He bubble mitigation by the thermodynamic effect. All such models require approximations and the proposed research was aimed at alloy selection, processing and detailed atomic-level microstructure evaluations to establish the efficacy of the thermodynamic effect. The outcome of this research shows that appropriate alloy additions can produce a contribution from the thermodynamic stabilization effect. Furthermore, due to the oxygen typically present in nominally high purity elemental powders used for powder metallurgy processing, the optimum results obtained appeared as a synergistic combination of nano-size oxide particle pinning kinetic effect and the grain-boundary segregation thermodynamic effect.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Reducing Reaction Temperature, Steam Requirements, and Coke Formation During Methane Steam Reforming Using Electric Fields: A Microkinetic Modeling and Experimental Study

In this study, we approach several common problems with the Ni-catalyzed methane steam reformation reaction (MSR) using a two-pronged approach combining density functional theory (DFT) calculations with experimental work. Specifically, we look at the deactivation of a Ni catalyst due to coke formation, its high operating temperature requirements, and the high steam-to-methane (H 2 O/CH 4 ) ratio needed for proper MSR operation. A DFT-based microkinetic model was developed in the presence and absence of electric fields and the results were compared with experimental results. The microkinetic model shows that under various electric fields, the most favorable MSR mechanism changed slightly. It also shows that the presence of a positive electric field decreases the surface coverage of carbon, increases the water coverage, accelerates the rate-limiting step of the C-H bond cleavage in methane, and increases the desorption rates of the syngas product (CO + H 2 ) during MSR. Consequently, for a given methane conversion, a positive electric field allows for significantly lower H 2 O/CH 4 ratio and operating temperatures as compared to systems without an electric field. These findings correspond well with experimental tests under a variety of operating conditions. In addition, improvement in the catalytic activity due to the presence of a positive electric field remained significant even at industrially relevant applied pressures improving the hydrogen yield greatly. Overall, we find that an applied electric field can play a significant role in improving the catalytic activity of heterogeneous reactions. Here, this information can guide the design of heterogeneous reactions in the presence of an electric field. By utilizing the electric field generated by various renewable energy sources, electric field assisted heterogeneous reactions can open up a paradigm in future energy research.

03 NATURAL GAS↗
DOE OSTI2011

CRADA Final Report for CRADA Number ORNL00-0605: Advanced Engine/Aftertreatment System R&D

Navistar and ORNL established this CRADA to develop diesel engine aftertreatment configurations and control strategies that could meet emissions regulations while maintaining or improving vehicle efficiency. The early years of the project focused on reducing the fuel penalty associated with lean NOx trap (LNT), also known as NOx adsorber catalyst regeneration and desulfation. While Navistar pursued engine-based (in-cylinder) approaches to LNT regeneration, complementary experiments at ORNL focused on in-exhaust fuel injection. ORNL developed a PC-based controller for transient electronic control of EGR valve position, intake throttle position, and actuation of fuel injectors in the exhaust system of a Navistar engine installed at Oak Ridge. Aftertreatment systems consisting of different diesel oxidation catalysts (DOCs) in conjunction with a diesel particle filter and LNT were evaluated under quasi-steady-state conditions. Hydrocarbon (HC) species were measured at multiple locations in the exhaust system with Gas chromatograph mass spectrometry (GC-MS) and Fourier transform infrared (FTIR) spectroscopy. Under full-load, rated speed conditions, injection of fuel upstream of the DOC reduced the fuel penalty for a given level of NOx reduction by 10-20%. GC-MS showed that fuel compounds were 'cracked' into smaller hydrocarbon species over the DOC, particularly light alkenes. GC-MS analysis of HC species entering and exiting the LNT showed high utilization of light alkenes, followed by mono-aromatics; branched alkanes passed through the LNT largely unreacted. Follow-on experiments at a 'road load' condition were conducted, revealing that the NOx reduction was better without the DOC at lower temperatures. The improved performance was attributed to the large swings in the NOx adsorber core temperature. Split-injection experiments were conducted with ultra-low sulfur diesel fuel and three pure HC compounds: 1-pentene, toluene, and iso-octane. The pure compound experiments confirmed the previous results regarding hydrocarbon reactivity: 1-pentene was the most efficient LNT reductant, followed by toluene. Injection location had minimal impact on the reactivity of these two compounds. Iso-octane was an ineffective LNT reductant, requiring high doses (resulting in high HC emissions) to achieve reasonable NOx conversions. Diesel fuel reactivity was sensitive to injection location, with the best performance achieved through fuel injection downstream of the DOC. This configuration generated large LNT temperature excursions, which probably improved the efficiency of the NOx storage/reduction process, but also resulted in very high HC emissions. The ORNL team demonstrated an LNT desulfation under 'road load' conditions using throttling, EGR, and in-pipe injection of diesel fuel. Flow reactor characterization of core samples cut from the front and rear of the engine-aged LNT revealed complex spatially dependent degradation mechanisms. The front of the catalyst contained residual sulfates, which impacted NOx storage and conversion efficiencies at high temperatures. The rear of the catalyst showed significant sintering of the washcoat and precious metal particles, resulting in lower NOx conversion efficiencies at low temperatures. Further flow reactor characterization of engine-aged LNT core samples established that low temperature performance was limited by slow release and reduction of stored NOx during regeneration. Carbon monoxide was only effective at regenerating the LNT at temperatures above 200 C; propene was unreactive even at 250 C. Low temperature operation also resulted in unselective NOx reduction, resulting in high emissions of both N{sub 2}O and NH{sub 3}. During the latter years of the CRADA, the focus was shifted from LNTs to other aftertreatment devices. Two years of the CRADA were spent developing detailed ammonia SCR device models with sufficient accuracy and computational efficiency to be used in development of model-based ammonia injection control algorithms.ORNL, working closely with partners at Navistar and Mi

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Scalable ab initio fragmentation methods based on a truncated expansion of the non-orthogonal molecular orbital model

An alternative formulation of the non-orthogonal molecular orbital model of electronic structure theory is developed based on the expansion of the inverse molecular orbital overlap matrix. From this model, a hierarchy of ab initio fragment-based quantum chemistry methods, referred to as the nth-order expanded non-orthogonal molecular orbital methods, are developed using a minimal number of approximations, each of which is frequently employed in intermolecular interaction theory. These novel methods are compared to existing fragment-based quantum chemistry methods, and the implications of those significant differences, where they exist, between the methods developed herein and those already existing methods are examined in detail. Computational complexities and theoretical scaling are also analyzed and discussed. Future extensions for the hierarchy of methods, to account for additional intrafragment and interfragment interactions, are outlined.

Westheimer, Bryce M. (ORCID:0000000267645714)↗
DOE OSTI2016

SU-F-I-49: Vendor-Independent, Model-Based Iterative Reconstruction On a Rotating Grid with Coordinate-Descent Optimization for CT Imaging Investigations

Purpose: Iterative reconstruction methods show promise for improving image quality and lowering the dose in helical CT. We aim to develop a novel model-based reconstruction method that offers potential for dose reduction with reasonable computation speed and storage requirements for vendor-independent reconstruction from clinical data on a normal desktop computer. Methods: In 2012, Xu proposed reconstructing on rotating slices to exploit helical symmetry and reduce the storage requirements for the CT system matrix. Inspired by this concept, we have developed a novel reconstruction method incorporating the stored-system-matrix approach together with iterative coordinate-descent (ICD) optimization. A penalized-least-squares objective function with a quadratic penalty term is solved analytically voxel-by-voxel, sequentially iterating along the axial direction first, followed by the transaxial direction. 8 in-plane (transaxial) neighbors are used for the ICD algorithm. The forward problem is modeled via a unique approach that combines the principle of Joseph’s method with trilinear B-spline interpolation to enable accurate reconstruction with low storage requirements. Iterations are accelerated with multi-CPU OpenMP libraries. For preliminary evaluations, we reconstructed (1) a simulated 3D ellipse phantom and (2) an ACR accreditation phantom dataset exported from a clinical scanner (Definition AS, Siemens Healthcare). Image quality was evaluated in the resolution module. Results: Image quality was excellent for the ellipse phantom. For the ACR phantom, image quality was comparable to clinical reconstructions and reconstructions using open-source FreeCT-wFBP software. Also, we did not observe any deleterious impact associated with the utilization of rotating slices. The system matrix storage requirement was only 4.5GB, and reconstruction time was 50 seconds per iteration. Conclusion: Our reconstruction method shows potential for furthering research in low-dose helical CT, in particular as part of our ongoing development of an acquisition/reconstruction pipeline for generating images under a wide range of conditions. Our algorithm will be made available open-source as “FreeCT-ICD”. NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Advanced Turbulence Models for Large-Scale Atmospheric Boundary Layer Flows

We present high-fidelity large-eddy-simulation (LES) modeling approaches for the turbulent atmospheric boundary layer (ABL) flows. Wind energy is a prime example of an application driven by ABL. Generation of electrical energy from farms of wind turbines at night in the stable ABL is a particularly interesting situation. In this report, we consider the well-known GEWEX (Global Energy and Water Cycle Experiment) Atmospheric Boundary Layer Study (GABLS) stably stratified benchmark LES case. We use a high-order spectral element code Nek5000/RS, which is supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms. In our earlier ANL report, we demonstrated our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. In this report, we extend the range of our SGS modeling approaches in the context of the mean-field eddy viscosity (MFEV), to include the solution of an SGS turbulent kinetic energy equation (TKE). We demonstrate the model fidelity of Nek5000/RS in comparison to that of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, with which we studied scaling performance for both codes in comparison on DOE's leadership computing platforms.

17 WIND ENERGY↗
NASA NTRS1979

Development of a model and computer code to describe solar grade silicon production processes

Mathematical models, and computer codes based on these models were developed which allow prediction of the product distribution in chemical reactors in which gaseous silicon compounds are converted to condensed phase silicon. The reactors to be modeled are flow reactors in which silane or one of the halogenated silanes is thermally decomposed or reacted with an alkali metal, H2 or H atoms. Because the product of interest is particulate silicon, processes which must be modeled, in addition to mixing and reaction of gas-phase reactants, include the nucleation and growth of condensed Si via coagulation, condensation, and heterogeneous reaction.

Srivastava, R.↗
NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.

Machine Learning↗
DOE OSTI2024

Rationalizing Acidic Oxygen Evolution Reaction over IrO 2 : Essential Role of Hydronium Cation

The development of active, stable, and more affordable electrocatalysts for acidic oxygen evolution reaction (OER) is of great importance for the practical application of electrolyzers and the advancement of renewable energy conversion technologies. Currently, IrO 2 is the only catalyst with high stability and activity, but a high cost. Further optimization of the catalyst is limited by the lack of understanding of catalytic behaviors at the acid-IrO 2 interface. Here, in strong interaction with the experiment, we develop an explicit model based on grand-canonical density function theory (GC-DFT) calculations to describe acidic OER over IrO 2 . Compared to the explicit models reported previously, hydronium cations (H 3 O + ) are introduced at the electrochemical interface in the current model. As a result, a variation in stable IrO 2 surface configuration under the OER operating condition from previously proposed complete *O-coverage to a mixture coverage of *OH and *O is revealed, which is well supported by in situ Raman measurements. In addition, the accuracy of predicted overpotential is increased in comparison with the experimentally measured. More importantly, in this study, an alteration of the potential limiting step from previously identified *O→*OOH to *OH→*O is observed, which opens new opportunities to advance the IrO 2 -based catalysts for acidic OER.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. Here we introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off-stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application to assist field scientists in the discovery of novel thermoelectric materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗