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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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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Grid-connected heat pump water heater benefits for low-income households in the Southeastern United States

Energy efficient heat pump water heaters (HPWHs) reduce customer energy costs; grid-connected controls further increase HPWH value by enabling energy storage and shifting demand to cheaper, off-peak periods. However, a HPWH’s first cost premium can put it out of reach for low-income customers. This paper will present results of an ongoing study exploring the benefits of load shifting HPWHs for 24 low-income households, aiming to support increased product deployment in this demographic. The study uses EcoPort modules to shift HPWH load for a study sample consisting primarily of low-income adults and seniors in North Carolina. Building upon prior studies, the study’s HPWH operating schedules are designed to maximize shifted energy and minimize participant electricity costs based on local time-of-use rates. The study documents load profiles of grid-connected HPWHs in a less-studied demographic group and explores how certain groups may have particularly flexible loads due to their unique usage profiles. The results are relevant for HPWH product performance in the Southeast. The study will quantify HPWH load shifting performance during North Carolina’s hot summer, temperate shoulder, and occasionally sub-freezing winter seasons, and the study includes installations in conditioned, semi-conditioned, and unconditioned spaces. Finally, study results will also explore how controlled HPWHs can provide value to the regional grid by reducing seasonal peaks via demand response. Lessons learned include HPWH acceptance for low-income and senior users, best practices for maintaining HPWH connectivity among users without prior product experience, and strategies to leverage remote monitoring to ensure optimal operation and enhance HPWH reliability.

Urigwe, Daniela↗
DOE OSTI2024

Verification and Validation of Spectral Element Code for Supercritical CO2 Flow in Vertical Heated Tubes

The investigation of heat transfer in supercritical CO2 (sCO2) has garnered considerable attention in recent decades, given sCO2's potential as a promising working fluid for advanced power conversion cycles. Despite previous research efforts, there are still gaps in our understanding of sCO2 heat transfer, particularly in conditions associated with heat transfer deterioration. To delve into sCO2 heat transfer more comprehensively, we propose employing the high-fidelity computational fluid dynamics code NekRS to simulate sCO2 flow using the large eddy simulation technique. Through graphics processing unit acceleration, NekRS achieves a higher computational speed than traditional CPU-based systems. However, before using NekRS in practical applications involving sCO2, it is imperative to perform verification and validation. Here, this paper presents our efforts to verify and validate the NekRS code's capability for simulating sCO2 using heated vertical tubes, where heat transfer deterioration usually happens. To accommodate the unique properties of sCO2, we have modified the NekRS code by integrating third-party property modules, such as REFPROP and PROPATH. Our simulations are compared with experimental and numerical data from the literature, instilling confidence in leveraging NekRS for future engineering applications. Our simulations also reveal that the accuracy of the property module significantly impacts the results, with REFPROP outperforming PROPATH for sCO2 properties. Additionally, we observed that, depending on the flow direction, buoyancy can either enhance or suppress turbulence in sCO2 flow. In upward flow, under certain conditions, the suppressed turbulence leads to heat transfer deterioration, resulting in elevated wall temperatures.

NekRS↗
DOE OSTI2026

Magnus method for electronic structure calculations at extreme conditions

We present the application of Magnus based methods to the solution of first order coupled ordinary differential equations in High Energy Density (HED) physics applications. Our focus is on the application to quantum mechanical methods, specifically on the solution of the radial Dirac equation for real and complex energies. HED applications require accurate solutions across a wide range of spatial and energy domains, including regimes where the solutions exhibit pronounced oscillatory behavior. Such cases pose significant computational challenges. We demonstrate that Magnus-based integrators can efficiently and accurately address these challenges. We discuss the implementation of the Magnus method for the solution of the radial Dirac equation, including practical considerations such as the evaluation of matrix exponentials, numerical integration, error estimation, and adaptive step size control. We also discuss the application of these methods to complex energy Green’s function techniques and the efficient approximation of integrals of the solutions relevant to HED electronic structure calculations. Here, we demonstrate the accuracy and robustness of the resulting method in applications to the free-particle case, for which analytic solutions are available for comparison, as well as the challenging case of gold at HED conditions.

general physics↗
DOE OSTI2021

The Eco-Bio-Social Factors That Modulate Aedes aegypti Abundance in South Texas Border Communities

Aedes aegypti control requires dedicated resources that are usually scarce, limiting the reach and sustainability of vector control programs. This generates a need to focus on areas at risk of disease transmission and also understand the factors that might modulate local mosquito abundance. We evaluated the eco-bio-social factors that modulate indoor and outdoor relative abundance of female Ae. aegypti in communities of South Texas. We conducted housing quality and Knowledge Attitudes and Practices surveys in households that were part of a weekly mosquito surveillance program in November of 2017 and 2018. Our results showed widespread knowledge of mosquitoes and Zika virus by our participants. However, less than 35% considered them as serious problems in this region. The presence of window-mounted air conditioning units increased the risk of female mosquito relative abundance indoors. An increase in outdoor relative abundance was associated with larger properties and a higher number of children between 6 to 17 years of age. Interestingly, we observed that an increasing number of children <5 years of age modulated both indoor and outdoor relative abundance, with a 52% increase indoors and 30% decrease outdoors. The low perception of mosquito and disease risk highlights engagement needs for vector-borne disease prevention in this region. The identified risk factors can help guide public health officials in their efforts to reduce human and vector contact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

From Theory to Practice: Feasibility Study of a Thermal Microgrid at a DoD Installation: Preprint

Advanced district thermal energy systems, also known as thermal microgrids, can electrify and decarbonize urban districts in a highly efficient way, but adoption of such systems, especially in the US, has been slow. Thermal microgrids use a network circulating water at near-ambient (60-80 degree F) (15- 25 degree C) temperatures, and water-source heat pumps at connected buildings to boost or lower the temperature for space conditioning and domestic hot water. Addressing challenges related to retrofits of existing buildings for integration with such systems, as well as quantifying the benefits of different configurations, will be key to unlocking the full potential of thermal microgrids for energy savings, decarbonization, and resiliency. This paper will do that, by reporting on preliminary results from a feasibility study of a thermal microgrid, and illustrating the process of conceptual design to seek the best possible performance, within the many constraints of existing buildings. The feasibility study focuses on a cluster of existing buildings, including a grocery store, big box style retail store, motel, and clinic, at a US Department of Defense installation in Germany. The study will assess the performance of a pilot-scale thermal microgrid serving those buildings, in terms of energy, emissions, and life cycle cost, and assess practical considerations (such as building retrofits and scalability) for future implementation. A significant opportunity exists for waste heat recovery from refrigeration at the grocery store (more than offsetting the entire building s space heating load in terms of waste heat availability), and modeling of the proposed thermal microgrid will assess the benefits of the waste heat integration. The moderate operating temperatures of thermal microgrids facilitate the integration of waste heat from a variety of sources, and the networks allow waste heat to be shared amongst buildings in proximity. Along with results on expected energy and emissions performance, as well as waste heat integration, the paper will assess implications of load diversity on system performance. The paper will also discuss the process of evaluating existing building HVAC systems for compatibility with a thermal microgrid, including the feasibility of lower heating hot water temperatures compatible with currently-available water-to-water heat pumps. The cluster of buildings under consideration in this study has much in common with commercial buildings in the United States. The process of and results from this feasibility study can inform assessment of the potential for thermal microgrids in other locations and help unlock the benefits of such systems for decarbonization and energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Life‐Cycle Assessment Considerations for Batteries and Battery Materials

Abstract Rechargeable batteries are necessary for the decarbonization of the energy systems, but life‐cycle environmental impact assessments have not achieved consensus on the environmental impacts of producing these batteries. Nonetheless, life cycle assessment (LCA) is a powerful tool to inform the development of better‐performing batteries with reduced environmental burden. This review explores common practices in lithium‐ion battery LCAs and makes recommendations for how future studies can be more interpretable, representative, and impactful. First, LCAs should focus analyses of resource depletion on long‐term trends toward more energy and resource‐intensive material extraction and processing rather than treating known reserves as a fixed quantity being depleted. Second, future studies should account for extraction and processing operations that deviate from industry best‐practices and may be responsible for an outsized share of sector‐wide impacts, such as artisanal cobalt mining. Third, LCAs should explore at least 2–3 battery manufacturing facility scales to capture size‐ and throughput‐dependent impacts such as dry room conditioning and solvent recovery. Finally, future LCAs must transition away from kg of battery mass as a functional unit and instead make use of kWh of storage capacity and kWh of lifetime energy throughput.

25 ENERGY STORAGE↗
DOE OSTI2020

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices, including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Finally, future research directions are discussed, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, improved metrics of measuring building performance, and improved industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic-Boreal Region

During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20-year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q 10 , is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Poe, Jeralyn [Northern Arizona University, Flagsta↗
DOE OSTI2025

Scalable mechanochemical synthesis of high-quality Prussian blue analogues for high-energy and durable potassium-ion batteries

Prussian blue analogues (PBAs) are recognized as promising cathode materials for potassium-ion batteries (PIBs), particularly the low-cost and high-energy K 2 Mn[Fe(CN) 6 ](KMnF). However, conventional solution-based synthesis inevitably introduces [Fe(CN) 6 ] 4− defects and lattice water while suffering low synthesis efficiency, unfavorable to the improvement of electrochemical performance and scalability. Here, in this work, we report a simple solvent-free mechanochemical strategy for the synthesis of a wide variety of K 2 M[Fe(CN) 6 ] (M = Mn, Mg, Ca, etc.) with negligible defects and water, and it is unprecedented to achieve kilogram-level products of high-quality KMnF within just 10 minutes. The as-prepared KMnF delivers a high energy density of 590 Wh kg −1 at 0.2 C and exhibits an astonishing stability over 10 000 cycles and rate ability up to 50 C in a potassium metal half-cell. Encouragingly, a high-areal-capacity pouch cell with 2.2 mAh cm −2 (16.5 mg cm −2 ) exhibits a capacity retention of 80.7% after 500 cycles. Furthermore, systematic in situ characterization reveals underlying mechanism insights into structure–performance relationships. Specifically, the fully coordinated Mn–N 6 octahedral configuration effectively suppresses Mn 3+ Jahn–Teller distortion, enabling reversible phase transitions under both high-voltage and long-term cycling conditions. In addition, minimal defects provide sufficient redox centers, while the continuous three-dimensional framework facilitates rapid K + diffusion kinetics. This work provides a new opportunity for the ultrafast, universal and scalable synthesis of high-quality PBAs, facilitating the practical application of PIBs while enabling precise structural and compositional design of novel PBAs.

Mechanochemical method↗
DOE OSTI2022

Land management explains major trends in forest structure and composition over the last millennium in California’s Klamath Mountains

Significance We provide the first assessment of aboveground live tree biomass in a mixed conifer forest over the late Holocene. The biomass record, coupled with local Native oral history and fire scar records, shows that Native burning practices, along with a natural lightning-based fire regime, promoted long-term stability of the forest structure and composition for at least 1 millennium in a California forest. This record demonstrates that climate alone cannot account for observed forest conditions. Instead, forests were also shaped by a regime of frequent fire, including intentional ignitions by Native people. This work suggests a large-scale intervention could be required to achieve the historical conditions that supported forest resiliency and reflected Indigenous influence.

Knight, Clarke A.↗
DOE OSTI2020

Progress Toward Simulating Departure from Nucleate Boiling at High-Pressure Applications with Selected Wall Boiling Closures

Recently, a Eulerian-based two-fluid computational fluid dynamics (CFD) framework with a wall heat flux partitioning approach has been intensively investigated for departure from nucleate boiling (DNB) simulation under the U.S. Department of Energy–funded Consortium for Advanced Simulation of Light Water Reactors (CASL) program. Understanding of the DNB characteristics over a range of pressurized water reactor–like operating conditions and accurate prediction of boiling crisis in the nuclear power system have been grand challenges because of the large impact of DNB on reactor safety and operational economics. The ultimate goal of this task in the CASL program is to introduce a robust multiphase CFD–based DNB modeling framework that is capable of characterizing an entire boiling history in which the wall boiling mode experiences the following through multiple stages of heat transfer mode: (1) single-phase convective heat transfer, (2) nucleate boiling heat transfer, and (3) identification of the departure of nucleate boiling. To validate the CASL boiling model, we have benchmarked simulated DNB over three different flow channel configurations (pipe flow, 5 × 5 fuel bundle with mixing vane tests, and 5 × 5 fuel bundle without mixing vane tests) against experimental measurements, and the validation result with open literature is reported. The DNB detection criteria in the simulation are checked by monitoring the peak wall temperature, wall dryout factor, and net energy balance. In addition to the DNB performance test, some preliminary sensitivity results on closure model selection are reported to address the prediction capability of local void profile against measurements. The boiling simulation tested in this study exhibits a maximum deviation of 24% from the measured DNB value in a high-pressure (i.e., 138 bars) subcooled pipe flow test. The ranges of operating conditions are as follows: 1650 to 2650 kg/m 2 ·s for mass flux and 8.5 to 96 K for subcooled inlet temperature. The deviation is even reduced to 7% when the subcooled temperature is less than 40 K. Besides accuracy, base practice guidelines for DNB detection criteria are tested by monitoring three simulation variables: (1) maximum wall temperature, (2) wall dryout factor (i.e., K-value), and (3) energy balance. Numerical robustness of DNB simulation is largely achieved in most of the validation test except for a few high subcooled test cases.

42 ENGINEERING↗
DOE OSTI2025

Fluidized Bed Gasification For Conversion of Biomass and Waste Materials to Renewable Hydrogen

This project was undertaken in order to study the potential for hydrogen production, at low cost, from mixtures of biomass and municipal solid waste (MSW). This approach allows for the production of hydrogen with a very low fossil carbon burden, while taking advantage of tipping fees (associated with MSW) to improved process economics. The team sourced three primary feedstocks (wood, MSW, and waste plastics) and characterized them comprehensively using established techniques with a long track record in the field of gasification. All three primary feedstocks were highly reactive and lost most of their mass during initial devolatilization. The production of tars, including heavy tars, was quite high, and was most problematic in the case of the MSW and Waste Plastics feedstocks. Little practical difference was identified between the MSW and Waste Plastics materials, and the addition of bed-forming materials (dolomite and brown alumina) to the feedstocks was found to reduce production of tars during devolatilization under thermogravimetric analysis and/or Fischer Assay conditions. A series of four tests in a lab-scale bubbling-fluidized-bed gasifier, at 50 psig of pressure and about 825 C, confirmed these findings. Pellet feedstocks, broken into fragments, were used for these tests, and pellets comprised of 50% MSW and 50% biomass were found to be the best option in terms of fossil carbon burden, economic potential, and gasification characteristics. Tests were then undertaken in a pilot-scale gasifier facility based on the GTI U-Gas fluidized-bed gasification technology. The feedstock handling train of the 20 TPD U-Gas pilot-scale gasifier located in Des Plaines, IL, was operated under simulated gasification conditions, and the 50/50 pellets were found to be very robust and unproblematic. An improved design for the forward end of the feedstock injection screw of the gasifier was developed and installed. The design approach was based on improved passive cooling of the front-most shroud at the end of the screw, since this approach was found in comprehensive modeling studies to be more than sufficient to accomplish the project objectives associated with this phase of the work, while also avoiding thermal gradients that could have caused heat-stress-induced damage to the refractory around the feedstock inlet if an active cooling approach had been applied. Careful technoeconomic analysis (TEA) of two possible 1000 TPD facilities was carried out. The TEA of conversion of MSW with corn stover in one case, and MSW with woody feedstock in the other case, showed that both had the potential to provide hydrogen at about $1/kg (minimum selling price, 2018 dollar basis). Of the two TEA cases, the one that was based on the conversion of wood in the southeastern USA was found to have slightly better economic potential. The other TEA case was based on a real location in Nebraska and called for corn stover feedstock conversion along with MSW. An underserved communities outreach program plan was developed in cooperation with personnel from the Nebraska Public Power District.

08 HYDROGEN↗
DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

The sensitive surface chemistry of Co-free, Ni-rich layered oxides: identifying experimental conditions that influence characterization results

Recent studies have suggested that Co-free, Ni-rich layered cathodes (e.g., doped LiNiO2) can provide promising battery performance for practical applications. However, these layered cathodes suffer from significant surface instability during various stages of the sample history, which generates inherent challenges for achieving stable battery performance and obtaining statistically representative characterization results. To reliably report the surface chemistry of these materials, delicate controls of stepwise sample preparation are required. In this study, we aim to reveal how the surface chemistry of LiNiO2 based materials changes with various environments, including human exhalation, sample storage, sample preparation, electrochemistry cycling, and surface doping. Our results demonstrate that the surface of these materials is highly reactive and prone to alter at various stages of sample handling and characterization. The sensitive surface could impact the interpretation of the surface chemical and structural information, including surface carbonate formation, transition metal reduction and dissolution, and surface reconstruction. Importantly, the heterogeneity of the surface degradation calls for a consolidation of nanoscale, high-resolution characterization, and ensemble-averaged methods in order to improve statistical representation. Furthermore, the doping chemistry can effectively mitigate the surface degradation and improve overall battery performance due to the enhanced surface oxygen retention. Our study highlights the necessity of strict measurements through complementary characterizations at multiple length scales to eliminate unintentional biased conclusions.

surface chemistry, Co-free Ni-rich cathodes, Istab↗
DOE OSTI2025

Enhanced Weathering May Benefit From Co‐Application With Organic Amendments

Abstract Enhanced weathering has emerged as a promising natural climate solution that has the potential to remove billions of tons of carbon from the atmosphere if widely adopted in agricultural settings. Despite this potential, few field trials have been published that verify the carbon dioxide removal (CDR) potential of enhanced weathering in croplands and, until now, none had been published in grazing lands. Anthony et al. (2025, https://doi.org/10.1029/2024AV001480 ) conducted the first trial of enhanced weathering in a California rangeland and showed weathering of ground silicate rocks despite drought conditions in an already dry climate. Co‐application of inorganic (silicate rocks) with organic (biochar and compost) amendments revealed not just additive, but synergistic effects whereby organic amendments increased rates of weathering. This is important because field CDR rates were <10% of the theoretical maximum (i.e., the rate if basalt was completely weathered); thus, methods to improve weathering rates will be necessary for this practice to scale in a meaningful way. Multi‐carbon pool measurements revealed not only how co‐application of soil amendments heightened net carbon benefits, but also how soil amendments complemented each other to produce net benefits for soil carbon, biomass growth, and greenhouse gas emission reductions. Anthony et al. (2025, https://doi.org/10.1029/2024AV001480 ) produce new insights toward our understanding of enhanced weathering as well as introduce paths for future research concerning combined amendment applications, synergistic mechanisms for carbon storage, and deployment in various agricultural contexts. While questions remain about the fate of weathering products in arid regions, Anthony et al. (2025, https://doi.org/10.1029/2024AV001480 ) present novel findings on the potential for significant weathering to occur even under suboptimal conditions.

Almaraz, Maya [Yale Center for Natural Carbon Capt↗
DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗
DOE OSTI2021

Revisiting the ODE Method for Recursive Algorithms: Fast Convergence Using Quasi Stochastic Approximation

Several decades ago, Profs. Sean Meyn and Lei Guo were postdoctoral fellows at ANU, where they shared interest in recursive algorithms. It seems fitting to celebrate Lei Guo's 60th birthday with a review of the ODE Method and its recent evolution. The method has been regarded as a technique for algorithm analysis. It is argued that this viewpoint is backwards: The original stochastic approximation method was surely motivated by an ODE, and tools for analysis came much later (based on establishing robustness of Euler approximations). The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time, following by careful approximation to obtain a practical recursive algorithm. While these methods are usually presented in a stochastic setting, this is not a prerequisite. In fact, recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo. Subject to conditions, the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert. The conditions are not universal, but theory suggests alternatives to achieve acceleration. The theory is illustrated with applications to gradient-free optimization, and policy gradient algorithms for reinforcement learning.

learning and adaptive systems in artificial intell↗