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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Misclassification of primary liver cancer in the Life Span Study of atomic bomb survivors

Primary liver cancer is difficult to diagnose accurately at death, due to metastases from nearby organs and to concomitant diseases, such as chronic hepatitis and cirrhosis. Trends in diagnostic accuracy could affect radiation risk estimates for incident liver cancer by altering background rates or by impacting risk modification by sex and age. We quantified the potential impact of death‐certificate inaccuracies on radiation risk estimates for liver cancer in the Life Span Study of atomic bomb survivors. True‐positive and false‐negative rates were obtained from a previous study that compared death‐certificate causes of death with those based on pathological review, from 1958 to 1987. We assumed various scenarios for misclassification rates after 1987. We obtained estimated true positives and estimated false negatives by stratified sampling from binomial distributions with probabilities given by the true‐positive and false‐negative rates, respectively. Poisson regression methods were applied to highly stratified person‐year tables of corrected case counts and accrued person years. During the study period (1958–2009), there were 1,885 cases of liver cancer, which included 383 death‐certificate‐only (DCO) cases; 1,283 cases with chronic liver disease as the underlying cause of death; and 150 DCO cases of pancreatic cancer among 105,444 study participants. Across the range of scenarios considered, radiation risk estimates based on corrected case counts were attenuated, on average, by 13–30%. Our results indicated that radiation risk estimates for liver cancer were potentially sensitive to death‐certificate inaccuracies. Additional data are needed to inform misclassification rates in recent years.

French, Benjamin↗
DOE OSTI2022

Effects of Supplementation in Upper Yakima River Chinook Salmon

Abstract To promote recovery of natural salmonid populations, managers are utilizing hatchery supplementation programs to increase abundance of spawners on the spawning grounds. However, studies have provided evidence that captive breeding can result in domestication, demonstrated by lower fitness of hatchery‐origin compared with natural‐origin fish. Supplementation programs, therefore, typically use natural‐origin broodstock in an effort to minimize long‐term negative fitness impacts. Here we evaluated the upper Yakima River spring supplementation program for Chinook Salmon Oncorhynchus tshawytscha , which has broodstock comprised exclusively of unmarked fish presumed to be of natural‐origin. Using 5 years of spawner data, we tested for effects of hatchery breeding and rearing on total adult returns and their individual reproductive success when spawning naturally. Our study revealed that supplementation increased overall abundance of fish spawning naturally on the spawning grounds. However, on average, compared with natural‐origin spawners, hatchery‐origin fish had reduced reproductive success, which also translated to reduced reproductive success in three out of five return years for natural‐origin fish that spawned with hatchery‐origin fish. As expected, body length and return timing were also significant predictors of reproductive success. However, more generations of data are needed to establish the extent to which reduced reproductive success is passed on to naturally produced progeny.

Koch, Ilana J.↗
DOE OSTI2021

Jet wake from linearized hydrodynamics

We explore how to improve the hybrid model description of the particles originating from the wake that a jet produced in a heavy ion collision leaves in the droplet of quark-gluon plasma (QGP) through which it propagates, using linearized hydrodynamics on a background Bjorken flow. Jet energy and momentum loss described by the hybrid model become currents sourcing linearized hydrodynamics. By solving the linearized hydrodynamic equations numerically, we investigate the development of the wake in the dynamically evolving droplet of QGP, study the effect of viscosity, scrutinize energy-momentum conservation, and check the validity of the linear approximation. We find that linearized hydrodynamics works better in the viscous case because diffusive modes damp the energy-momentum perturbation produced by the jet. We calculate the distribution of particles produced from the jet wake by using the Cooper-Frye prescription and find that both the transverse momentum spectrum and the distribution of particles in azimuthal angle are similar in shape in linearized hydrodynamics and in the hybrid model. Their normalizations are different because the momentum-rapidity distribution in the linearized hydrodynamics analysis is more spread out, due to sound modes. Since the Bjorken flow has no transverse expansion, we explore the effect of transverse flow by using local boosts to add it into the Cooper-Frye formula. After including the effects of transverse flow in this way, the transverse momentum spectrum becomes harder: more particles with transverse momenta bigger than 2 GeV are produced than in the hybrid model. Although we defer implementing this analysis in a jet Monte Carlo, as would be needed to make quantitative comparisons to data, we gain a qualitative sense of how the jet wake may modify jet observables by computing proxies for two example observables: the lost energy recovered in a cone of varying open angle, and the fragmentation function. We find that linearized hydrodynamics with transverse flow effects added improves the description of the jet wake in the hybrid model in just the way that comparison to data indicates is needed. Our study illuminates a path to improving the description of the wake in the hybrid model, highlighting the need to take into account the effects of both transverse flow and the broadening of the energy-momentum perturbation in spacetime rapidity on particle production.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Generation of macro- and microplastic databases by high-throughput FTIR analysis with microplate readers

Abstract FTIR spectral identification is today’s gold standard analytical procedure for plastic pollution material characterization. High-throughput FTIR techniques have been advanced for small microplastics (10–500 µm) but less so for large microplastics (500–5 mm) and macroplastics (> 5 mm). These larger plastics are typically analyzed using ATR, which is highly manual and can sometimes destroy particles of interest. Furthermore, spectral libraries are often inadequate due to the limited variety of reference materials and spectral collection modes, resulting from expensive spectral data collection. We advance a new high-throughput technique to remedy these problems using FTIR microplate readers for measuring large particles (> 500 µm). We created a new reference database of over 6000 spectra for transmission, ATR, and reflection spectral collection modes with over 600 plastic, organic, and mineral reference materials relevant to plastic pollution research. We also streamline future analysis in microplate readers by creating a new particle holder for transmission measurements using off-the-shelf parts and fabricating a nonplastic 96-well microplate for storing particles. We determined that particles should be presented to microplate readers as thin as possible due to thick particles causing poor-quality spectra and identifications. We validated the new database using Open Specy and demonstrated that additional transmission and reflection spectra reference data were needed in spectral libraries. Graphical abstract

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A meta-learning based distribution system load forecasting model selection framework

This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. Furthermore, the framework includes the following processes: feature extraction, candidate model preparation and labeling, offline training, and online model recommendation. Using load forecasting needs and data characteristics as input features, multiple metalearners are used to rank the candidate load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism is proposed to weights recommendations from each meta-leaner and make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

An assessment of the harvesting and fuel performance of a single-pass cut-and-chip harvester in commercial-scale short-rotation poplar crops as influenced by crop and weather conditions

There is a need for data on commercial-scale harvesting operations in hybrid poplar short rotation crops to better understand costs and logistics, especially for modeling large scale biorefineries. An investigation was conducted on the in-field performance of a single-pass cut-and-chip harvester operating in commercial fields for over 370 individual wagon loads representing a range of crop and field conditions. Mean material capacity peaked at 70 Mg h -1 in dry conditions in lower biomass (<60 Mg ha -1 ), but was only 28 Mg h -1 during wet conditions and often higher standing biomass. Wet ground conditions require the harvester to divert additional power to maintaining forward movement, which results in decreased material capacity and increased fuel consumption. Crops with higher standing biomass had taller trees that do not always feed as smoothly into the harvester, slowing forward progress and lowering material capacity. Mean crop specific fuel consumption, L of fuel per Mg of biomass processed, generally decreased as standing biomass increased. When standing biomass was above 40 Mg ha -1 mean crop specific fuel consumption (FCC) was 1.69 L Mg -1 in dry conditions and 3.98 L Mg -1 in wet conditions, but when standing biomass was below 40 Mg ha-1 FCC increased drastically (as high as 5 L Mg -1 ) because the harvester is putting more effort into forward speed instead of material processing. Developing relationships between material capacity and fuel consumption based on standing biomass and ground conditions at representative scales are essential for conducting environmental and economic analyses of these systems.

09 BIOMASS FUELS↗
DOE OSTI2025

Tropical intertidal microbiome response to the 2024 Marine Honour oil spill

Marine fuel oil (MFO) spills in tropical coastal environments are under-characterized despite increasing risk from maritime activities. Microbial and geochemical responses to the June 2024 Marine Honour MFO spill on Singapore's intertidal sediments were analyzed in real time over 185 days. Using metagenomics and hydrocarbon profiling, microbial community shifts and hydrocarbon degradation were quantified across visibly oiled (high-impact) and clean (low-impact) sites. Microbiomes at all sites adapted rapidly to the spill through increased diversity and abundance of genes encoding alkane and aromatic compound degradation, detoxification, and biosurfactant production. The dominant hydrocarbon-degrading bacteria differed markedly from those reported in other crude oil spills and in regions with different climates. Oil deposition intensity strongly influenced microbial succession and hydrocarbon-degrading gene profiles, and this reflected early toxicity constraints in heavily oiled areas. The persistence of hydrocarbon degradation genes beyond hydrocarbon detection in sediments suggested long-term functional priming may occur. The study provides novel genome-resolved insight into the microbial response to MFO pollution, advances understanding of marine environmental biodegradation, and provides urgently needed baseline data for oil spill response strategies in Southeast Asia and beyond.

Coastal pollution↗
DOE OSTI2022

Methods and system for siting advanced nuclear reactors and evaluating energy policy concerns

There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Impact of recent ENDF nuclear data update, high initial enrichment and high burnup fuel on critical experiments applicability determination via the integral index c k for burnup credit validation

In 2012, NUREG/CR-7109 reported on the validation of burnup credit calculations involving major and minor actinides and major fission products which was investigated for pressurized and boiling water reactor (PWR and BWR) fuel enrichments up to 5 wt% 235 U and assembly-average burnups up to 60 GWd/MTU. Recently, there has been interest in increasing the maximum enrichment used in PWR fuel as high as 8 wt% 235 U and correspondingly increasing the maximum assembly-average burnups to approximately 75 GWd/MTU. These proposed increases in enrichment and burnup necessitate reinvestigation of the validation basis for k eff calculations for this expanded application space. Additionally, the 2012 study was performed by using the Evaluated Nuclear Data File (ENDF)/B-VII.0 nuclear data with the SCALE 6 covariance library, and the effects of using the newly released ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data and covariance libraries should be evaluated. In this work, published in NUREG/CR-7309 in 2025, the validation assessment was performed consistently with NUREG/CR-7109: modeling irradiated fuel assemblies in the Generic Burnup Credit (GBC)-32 cask defined in NUREG/CR-6747. The TSUNAMI-3D sequence was used to generate sensitivity data for the application model, and the data were compared with sensitivity data from select benchmark models. The integral parameter c k is the metric of similarity used in this study and is consistent with NUREG/CR-7109, where a c k value in excess of 0.8 indicates sufficient similarity for use in validation. A new set of benchmark experiments with sensitivity data has been assembled for this effort. The number of experiments with available sensitivity data is now 2,104, compared to 474 in NUREG/CR-7109. This increase was facilitated by the efforts of the Nuclear Energy Agency to generate sensitivity data for a majority of the experiments in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook to supplement the data available in the Oak Ridge National Laboratory (ORNL) Verified, Archived Library of Inputs and Data (VALID). The complete set of benchmarks considered here includes experiments for low-enriched uranium (LEU), intermediate enriched uranium (IEU), and a mixture of uranium and plutonium (MIX) from the ICSBEP Handbook and VALID, as well as ORNL models of the Haut Taux de Combustion (HTC) experiments and other potentially relevant models not included in VALID. The updated similarity study shows that none of the extended burnup and higher enrichment combinations considered show a significant decrease in the number of potentially applicable experiments, meaning sufficient critical experiments exist for the validation of BUC criticality safety calculations, with initial enrichments up to 8 wt% 235 U and burnups up to 80 GWd/MTU. Additionally, both the ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data libraries can be used for validation since the number of critical experiments applicable for validation increases for most cases with the most recent nuclear data compared to the previous one. As in previous BUC validation studies, the French HTC experiments are the most similar in a majority of the application cases studied, especially from representative discharge burnups ranging from 40 to 80 GWd/MTU. In conclusion, these results match the conclusions presented in NUREG/CR-7109 regarding validation of the primary actinides in BUC analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Sooting tendencies of terpenes and hydrogenated terpenes as sustainable transportation biofuels

Terpenes are a diverse group of molecules that are synthesized by plants and microorganisms through combining units of isoprene (2-methyl-1,3-butadiene). They typically contain rings and methyl branches, which gives them high energy densities and low freezing points and makes them appealing candidates for sustainable transportation biofuels. Between the original biosynthesis and upgrading options such as hydrogenation, they have a large degree of freedom of structures, e.g., different carbon skeletons, positions of double bonds, and functional groups. Therefore, structure-property data is needed to downselect potential fuel candidates. Here, we measured the sooting tendencies of 17 C10 monoterpenes and 7 of their hydrogenated analogues. The hydrogenated compounds were custom synthesized, so the quantities were too small for conventional smoke point measurements. Thus, the sooting tendencies were quantified with yield sooting index (YSI), which is based on the soot yield in a fuel-doped non-premixed methane flame. Derived smoke points (DSPs) were estimated from a correlation between YSI and smoke point for other hydrocarbons. The YSI of terpenes and their derivatives varies widely from 85.6 to 248.5. The YSI follows the trend: terpenes > dihydroterpenes > tetrahydroterpenes. The DSPs of all the tetrahydroterpenes and some dihydroterpenes are higher than that of a Jet-A fuel sample, suggesting that they offer soot reduction benefits. Further, the YSIs depend strongly on molecular structure; for example, α-pinene and β-pinene have identical carbon skeletons and differ only in the position of one carbon-carbon double bond, but the YSI of α-pinene is 34% higher than that of β-pinene. Detailed decomposition analysis via density functional theory (DFT) suggests that compared with β-pinene, α-pinene requires fewer steps to form the first aromatic ring and the process is more thermodynamically favorable. The YSI difference between the pinenes is mainly affected by the identity of the products from the dominant decomposition pathways.

09 BIOMASS FUELS↗
DOE OSTI2025

Microbial inoculants and invasions: a call to action

Microbial inoculants are increasingly used for beneficial purposes in agriculture, bioremediation, and medicine, but they can carry risks of generating invasive microbes. Here, we present a roadmap for guarding against these invasions, proposing developing (i) coherent mechanistic understandings of how microbial inoculants can effect invasions, (ii) predictive models forecasting microbial invasion risks, and (iii) effective management strategies. To guide mechanistic understandings, we distill 17 guiding hypotheses. For predictive modeling, we highlight data collection needs and qualitative approaches. For management strategies, we stress the importance of accurately weighing the risks against benefits. The unified approach presented here provides a route toward an effective research and management infrastructure for microbial inoculants in order to avoid potentially catastrophic microbial invasions.

invasive species↗
DOE OSTI2023

Linking transportation agent-based model ($\mathrm{ABM}$) outputs with micro-urban social types ($\mathrm{MUSTs}$) via typology transfer for improved community relevance

The human relationship with transportation is shaped by social, economic, demographic, and urban form variables, or socio-spatial factors. The spatial dynamics of these are key to generating and interpreting outputs of transportation models that are most relevant for a community and the diverse mobility needs of its members. Here we present a typology transfer framework, grounded in socio-spatial dynamics shaping people's mobility, to take transportation-themed regional mobility model outcomes, in this case from two agent-based models (ABMs), and extrapolate them to other cities, with less time and resource intensity than new ABM development. The typology transfer process first identifies micro-urban social types (MUSTs) using socio-spatial factors, then defines city types based on spatial patterns of MUSTs to assess across which cities transfer results are likely to best hold. Lastly, a typology transfer multiplier matrix extrapolates a given variable, in our case the Mobility Energy Productivity (MEP) metric, to another city. The full process demonstration uses ABM results from Chicago (POLARIS model) and San Francisco (BEAM model), applying them to New York City. We discuss how MEP or other outputs can be appropriately estimated and used for integrated, human-centered mobility analysis. Key findings include that this MUST framework of user-defined dependent and independent variables allows tailoring ABM results and interpretations to specific community needs and data availability. Findings clarify that positive outcomes can be targeted towards user groups, based on sociospatial characteristics, using a typology approach, such as inclusive access to mobility choices, transportation affordability, and greater efficiency in resource use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

42 ENGINEERING↗
DOE OSTI2022

Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys

More than $270 billion is spent on combatting corrosion annually in the USA alone. As such, we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our focus is on a non-traditional class of alloys called multi-principal element alloys (MPEAs). Given the vast search space due to the variety of compositions and descriptors to be considered, and based upon existing corrosion data for MPEAs, we demonstrate descriptor optimization to predict corrosion resistance of any given MPEA. Our ML model with descriptor optimization predicts the corrosion resistance of a given MPEA in the presence of an aqueous environment by down selecting two environmental descriptors (pH of the medium and halide concentration), one chemical composition descriptor (atomic % of element with minimum reduction potential), and two atomic descriptors (difference in lattice constant (Δa) and average reduction potential). Our findings show that, while it is possible to down select corrosion-resistant MPEAs by using ML from a large search space, a larger dataset and higher quality data are needed to accurately predict the corrosion rate of MPEAs. This study shows both the promise and the perils of ML when applied to a complex chemical phenomenon like corrosion of alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks

Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.

Purohit, Shaurya [Iowa State University Ames Iowa ↗
DOE OSTI2020

Guest Editorial: Advanced Data-Analytics for Power System Operation, Control, and Enhanced Situational Awareness

Along with the smart grid development, modern power systems are entering a ‘data-intensive’ era. A vast volume of data from power grids is being collected through advanced sensing and communication technologies, such as smart metering data, phasor measurement data, as well as meteorological data (e.g., wind speed and solar irradiance) related to renewable power generation. Such data contains comprehensive information about the power system covering equipment's health status, power grid's static and dynamic characteristics, renewable power generation, customers’ electricity usage pattern, etc. Therefore, advanced data-analytics techniques are needed to convert such data to knowledge for practical applications. In line with the trend of widespread data-driven applications in power systems, this Special Issue aims to present state-of-the-art research works on advanced data-analytics for power system's operation, control, and situational awareness. There are in total twenty-six papers accepted for publication in this Special Issue through careful peer reviews and revisions. Under the overarching theme of data-driven applications in power systems, the selected papers are broadly categorised into five topics. The summary of every topic is given below. You are, however, strongly encouraged to read the full paper if interested.

Xu, Yan↗
DOE OSTI2020

Zero-deadtime processing in beta spectroscopy for measurement of the non-zero neutrino mass

The Project 8 collaboration seeks to measure, or more tightly bound, the mass of the electron antineutrino by applying a novel spectroscopy technique to precisely measure the tritium beta-decay spectrum. The current system produces a single analog signal, which is digitized and processed in several stages before being saved to local disk storage. Online processing includes two stages, an FPGA connected to the analog to digital converter reduces the data down to the region of interest before shipping over the local network for further processing and storage. A normal CPU-based processing stage applies triggering logic to only save data at times when a signal is present, further reducing the total volume of data which needs to be written to disk or transferred for long-term storage. The next stage of the project will need to process many input channels and will integrate a necessary aggregation and combination step prior to applying the event search and triggering logic. We present the online processing system which has successfully been deployed for the current, singlechannel, phase. We also present the status and design for a many-channel platform.

Project 8 Collaboration↗
DOE OSTI2023

Machine learning predictions of high-Curie-temperature materials

Technologies that function at room temperature often require magnets with a high Curie temperature, $T$ C , and can be improved with better materials. Discovering magnetic materials with a substantial $T$ C is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid $T$ C predictions solely based on the chemical composition of a material. We train a random-forest model and a k -NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. Further, a random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T$ C materials and under-predict high-$T$ C materials. For exhaustive searches to find new high-$T$ C materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

36 MATERIALS SCIENCE↗