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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: • Global robust models constructed for carcinogenicity prediction of diverse chemicals. • Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. • PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. • Developed interspecies PNN/GRNN models for carcinogenicity prediction. • Proposed models can be used as tool to predict carcinogenicity of new chemicals.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1979

Shock metamorphism of granulated lunar basalt

The paper deals with an extensive series of shock-recovery experiments performed on both nonporous crystalline basalt and its granulated and sieved counterpart to study the role of porosity and grain size in shock motomorphic effects under otherwise identical conditions. Shocked samples are compared with unshocked starting material in terms of textural and mineralogical modifications attributable to shock. A comparative petrographic and chemical characterization is presented of pulverized and sieved lunar basalt 75035 shocked between 6 and 75 GPa in comparison with holocrystalline disks of the same basalts shocked in 10 earlier experiments. Specifically, a petrographic classification of shock features is given, along with an estimation of relative amounts of shock glasses and a chemical characterization of shock glasses in each shocked granular basalt.

Schaal, R. B.↗
DOE OSTI2024

Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement

Accurately identifying primary biological aerosol particles (PBAPs) using analytical techniques poses inherent challenges due to their resemblance to other atmospheric carbonaceous particles. Here, we present a study of an enhanced method for detecting PBAPs by combining single-particle measurement with advanced supervised machine learning (SML) techniques. We analyzed ambient particles from a variety of environments and lab-generated standards, focusing on chemical composition for traditional rule-based and clustering approaches and incorporating morphological features into the SML approaches, neural networks and XGBoost, for improved accuracy. This study demonstrates that SML methods outperform traditional methods in quantifying PBAPs, achieving significant improvements in precision, recall, F1-score, and accuracy, leading to an increased number of detected PBAPs by at least 19%. The adaptability of the proposed XGBoost-based SML model is showcased in comparison to traditional methods in categorizing PBAPs for blind data sets from different geographical locations. Two field case studies were investigated, over agricultural land and Amazonia rain forest, representing relatively low and high concentrations of PBAPs, respectively, where XGBoost consistently detected up to 3.5 times more PBAPs than traditional methods. Precise detection of PBAPs in the atmosphere could significantly improve the prediction of climatic impacts by them.

42 ENGINEERING↗
DOE OSTI2022

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗
NASA NTRS2004

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Hoover, Richard B.↗
NASA NTRS1986

Systematics of the CHON and other light-element particle populations in Comet Halley

Based on chemical signatures measured by the PIA experiment during the Giotto flyby of comet Halley, particle classifications were designated. In addition to silicate-like grains and particles of mixed (cosmic) composition, there appear to be several light-element rich populations, including the CHON, (H,C), (H,C,O), and (H,C,N) particle types. These compositional classes are further distinguished by differences in mass distributions, a density indicator, and variations in relative abundance within the coma. These particle populations are evidence for chemical heterogeneity in the surface of the cometary nucleus. Particles found mainly in the inner coma may be volatile icy grains. Most of the N of the comet may be found in up to three different populations of grains; one or more of these may be responsible for the observation of cyanojets.

Clark, Benton↗
DOE OSTI2024

The Effect of Air Separations on Fast Pyrolysis Products for Forest Residue Feedstocks

This study investigates the intricate relationship between biomass preprocessing and pyrolysis product yields, employing the air classification technique for the treatment of loblolly pine residues with varying moisture content. A comprehensive exploration of the physicochemical properties of air-classified loblolly pine informs a sophisticated pyrolysis simulation model. Given the complex and multifaceted nature of biomass pyrolysis, operating across diverse temporal and spatial scales, a pyrolysis kinetics-based CFD–DEM simulation method is employed to predict product yields. Results showed that the elevated moisture content amplifies particle adhesiveness, necessitating augmented air velocities for effective separation, thereby influencing the efficiency of the separation process. While carbon and hydrogen contents exhibit relative stability across diverse moisture contents and blower frequencies, the oxygen content undergoes noticeable changes. For example, the oxygen contents were measured as 29.2 and 38.6 wt% in the light fraction of 30% moisture content sample at blower frequencies of 10 and 20 Hz, respectively. An intriguing finding emerges from pyrolysis simulation, indicating that a lower blower frequency in air classification moderately enhances bio-oil yield and significantly improves its quality, particularly in terms of water content. For instance, the water content in the bio-oil was about 1.5% and 10% in the heavy and light fractions, respectively from 10% moisture sample under 15 Hz blower frequency. In summary, a detailed understanding and strategic manipulation of critical material attributes in biomass through efficient fractionation techniques are imperative for advancing fast pyrolysis as a sustainable avenue for renewable energy and chemical production.

09 BIOMASS FUELS↗
DOE OSTI2012

Analytical Methods for Measuring Mercury in Water, Sediment and Biota

Mercury (Hg) exists in a large number of physical and chemical forms with a wide range of properties. Conversion between these different forms provides the basis for mercury's complex distribution pattern in local and global cycles and for its biological enrichment and effects. Since the 1960’s, the growing awareness of environmental mercury pollution has stimulated the development of more accurate, precise and efficient methods of determining mercury and its compounds in a wide variety of matrices. During recent years new analytical techniques have become available that have contributed significantly to the understanding of mercury chemistry in natural systems. In particular, these include ultra sensitive and specific analytical equipment and contamination-free methodologies. These improvements allow for the determination of total mercury as well as major species of mercury to be made in water, sediments and soils, and biota. Analytical methods are selected depending on the nature of the sample, the concentration levels of mercury, and what species or fraction is to be quantified. The terms “speciation” and “fractionation” in analytical chemistry were addressed by the International Union for Pure and Applied Chemistry (IUPAC) which published guidelines (Templeton et al., 2000) or recommendations for the definition of speciation analysis. "Speciation analysis is the analytical activity of identifying and/or measuring the quantities of one or more individual chemical species in a sample. The chemical species are specific forms of an element defined as to isotopic composition, electronic or oxidation state, and/or complex or molecular structure. The speciation of an element is the distribution of an element amongst defined chemical species in a system. In case that it is not possible to determine the concentration of the different individual chemical species that sum up the total concentration of an element in a given matrix, meaning it is impossible to determine the speciation, it is a useful practice to do fractionation instead. Fractionation is the process of classification of an analyte or a group of analytes from a certain sample according to physical (e.g. size, solubility) or chemical (e.g. bonding, reactivity) properties."

mercury↗
NASA NTRS2000

Super Capacitor Development At NASA MSFC

A viewgraph presentation outlines super capacitor development at NASA Marshall Space Flight Center. The concept, proof of concept testing and the test set-ups are described. An overview of super capacitor classification is shown and several types of capacitors are detailed: Ni-C chemical double layer (CDL), Ru-Oxide pseudo-cap, and a Ru-Oxide 2 F 30 V capacitor.

Hall, David K.↗
DOE OSTI2025

An energetic link between order and strength in metals: A nanocrystalline strength limit in high-entropy alloys and intermetallic compounds

The metallurgy and materials communities have long understood and exploited fundamental links between chemical and structural ordering in metallic solids to tailor their mechanical properties. We extend these ideas to include prediction of the nanocrystalline strength limit in high-entropy alloys and intermetallic compounds, where a breakdown occurs in the classical Hall-Petch strengthening behavior. The highest reported strength achievable through alloying has rapidly climbed and given rise to new classifications of materials with extraordinary properties, with a notable case being nanocrystalline metals. High-entropy alloys (chemically disordered, concentrated solid solutions) and intermetallic compounds are two boundary cases of how tailored order can be used to manipulate mechanical behavior. Here, we show that the complex electronic-structure mechanisms governing the peak strength of alloys and pure metals can be reduced to a few physically meaningful parameters based on their atomic arrangements and used – with no fitting parameters – to predict the maximum strength of these materials. This includes a generalized energy-based accounting for the degree of structural and chemical ordering that allows for rapid and reasonably accurate prediction of peak strength (validated in the nanocrystalline limit) as a function of temperature. Predictions of maximum strength based on the activation energy (with all materials properties derived from DFT calculations or experiments) for a stress-driven transition to an amorphous state is shown to accurately describe the breakdown in Hall-Petch behavior at the smallest crystallite sizes for pure metals, intermetallic compounds, high-entropy alloys, and metallic glasses. Further, this activation energy is also shown to be directly proportional to interstitial electronic charge density, which is a good predictor of ductility, stiffness (moduli), and phase stability in high-entropy alloys and solid metals generally. The proposed framework suggests the possibility of coupling ordering and intrinsic strength to mechanisms like dislocation nucleation, hydrogen embrittlement, and transport properties, such as through correlations between the activation energies for amorphization with stacking-fault and grain boundary energies. It additionally opens the prospect for greatly accelerated structural materials design and development to address materials challenges limiting more sustainable and efficient use of energy.

36 MATERIALS SCIENCE↗
NASA NTRS2018

In Situ Measurements of Angular-Dependent Light Scattering by Aerosols over the Contiguous United States

This work provides a synopsis of aerosol phase function (F (sub 11)) and polarized phase function (F (sub 12)) measurements made by the Polarized Imaging Nephelometer (PINeph) during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and the Deep Convection Clouds and Chemistry (DC3) field campaigns. In order to more easily explore this extensive dataset, an aerosol classification scheme is developed that identifies the different aerosol types measured during the deployments. This scheme makes use of ancillary data that include trace gases, chemical composition, aerodynamic particle size and geographic location, all independent of PI-Neph measurements. The PI-Neph measurements are then grouped according to their ancillary data classifications and the resulting scattering patterns are examined in detail. These results represent the first published airborne measurements of F (sub 11) and minus F (sub 12) divided by F (sub 11) for many common aerosol types. We then explore whether PI-Neph light-scattering measurements alone are sufficient to reconstruct the results of this ancillary data classification algorithm. Principal component analysis (PCA) is used to reduce the dimensionality of the multi-angle PI-Neph scattering data and the individual measurements are examined as a function of ancillary data classification. Clear clustering is observed in the PCA score space, corresponding to the ancillary classification results, suggesting that, indeed, a strong link exists between the angular scattering measurements and the aerosol type or composition. Two techniques are used to quantify the degree of clustering and it is found that in most cases the results of the ancillary data classification can be predicted from PI-Neph measurements alone with better than 85 percent recall. This result both emphasizes the validity of the ancillary data classification as well as the PI-Neph's ability to distinguish common aerosol types without additional information.

Aerosol phase function↗
NASA NTRS2006

Probabilistic Classification Using Elemental Abundance Distributions and Lossless Image Compression in Apollo 17 Lunar Dust Samples from Mare Serenitatis

We have previously outlined a strategy for the detection of fossils [Storrie-Lombardi and Hoover, 2004] and extant microbial life [Storrie-Lombaudi and Hoover, 20051 during robotic missions to Mars using co-registered structural and chemical signatures. Data inputs included image lossless compression indices to estimate relative textural complexity and elemental abundance distributions. Two exploratory classification algorithms (principal component analysis and hierarchical cluster analysis) provide an initial tentative classification of all targets. Nonlinear stochastic neural networks are then trained to produce a Bayesian estimate of algorithm classification accuracy. The strategy previously has been successful in distinguishing regions of biotic and abiotic alteration of basalt glass from unaltered samples. [Storrie-Lombardi and Fisk, 2004; Storrie-Lombardi and Fisk, 2004] Such investigations of abiotic versus biotic alteration of terrestrial mineralogy on Earth are compromised by .the difficulty finding mineralogy completely unaffected by the ubiquitous presence of microbial life on the planet. The renewed interest in lunar exploration offers an opportunity to investigate geological materials that may exhibit signs of aqueous alteration, but are highly unlikely to contain contaminating biological weathering signatures. We here present an extension of our earlier data set to include lunar dust samples obtained during the Apollo 17 mission. Apollo 17 landed in the Taurus-Littrow Valley in Mare Serenitatis. Most of the rock samples from this region of the lunar highlands are basalts comprised primarily of plagioclase and pyroxene and selected examples of orange and black volcanic glass. SEM images and elemental abundances (C6, N7, O8, Na11, Mg12, Al13, Si14, P15, S16, Cll7, K19, Ca20, Fe26) for a series of targets in the lunar dust samples are compared to the extant cyanobacteria, fossil trilobites, Orgueil meteorite, and terrestrial basalt targets previously discussed. The data set provides a first step in producing a quantitative probabilistic methodology for geobiological analysis of returned lunar samples or in situ exploration.

Storrie-Lombardi, Michael C.↗
DOE OSTI2019

Formation of Plastic Inclusions in U71Mnk High-Speed Heavy-Rail Steel Refined by CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO Slag

To precisely control the characteristics of nonmetallic inclusions with high plasticity in U71Mnk high-speed heavy-rail steel deoxidized by Si and Mn, the chemical composition of CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO slag in the ladle furnace (LF) refining process was optimized by thermodynamic calculations. Relationships among the refining slag, nonmetallic inclusions, and elemental concentrations in the steels were analyzed to obtain the conditions for determining the optimal composition of refining slag. The optimal compositions and the component activities of CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO refining slag were determined and discussed according to calculations using FactSage 7.0. Finally, ten industrial experiments were performed. Variations in the compositions of LF refining slag, inclusions, and steel, as well as the sizes of the inclusions, and metallographic classification assessment of the U71Mnk steel product, were investigated and discussed to verify the feasibility and effectiveness of the optimized LF refining slag. Kinetics of steel–slag and steel-inclusion reactions during LF refining was studied and compared. The results show that to obtain low [O], [Al], and [S] contents and inclusions with high plasticity in molten steel, the optimal composition of the LF refining slag should be CaO: 44.2 to 51.7 wt pct, SiO{sub 2}: 41.4 to 47.0 wt pct, Al{sub 2}O{sub 3}: 0 to 4.7 wt pct and MgO 5.0 to 7.0 wt pct, where the basicity R is above 1.00 and the C/A ratio is above 9.00. After the LF refining process, the contents of total oxygen (T.O.), [Al], and [S] gradually stabilized in the range of 0.0008 to 0.0012 wt pct, 0.002 to 0.0028 wt pct and 0.0026 to 0.0037 wt pct, respectively. The densities of the three kinds of typical inclusions (CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO, MnS, and CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO-MnS) also decreased and reached approximately 0.25/mm{sup 2}, and no inclusion larger than 6.0 μm was found. The composition of the inclusions gradually changed into the optimal range in the CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO slag system during the LF refining process.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Ten-year chemical signatures associated with long-range transport observed in the free troposphere over the central North Atlantic

Ten-year observations of trace gases at Pico Mountain Observatory (PMO), a free troposphere site in the central North Atlantic, were classified by transport patterns using the Lagrangian particle dispersion model, FLEXPART. The classification enabled identifying trace gas mixing ratios associated with background air and long- range transport of continental emissions, which were defined as chemical signatures. Comparison between the chemical signatures revealed the impacts of natural and anthropogenic sources, as well as chemical and physical processes during long transport, on air composition in the remote North Atlantic. Transport of North American anthropogenic emissions (NA-Anthro) and summertime wildfire plumes (Fire) significantly enhanced CO and O 3 at PMO. Summertime CO enhancements caused by NA-Anthro were found to have been decreasing by a rate of 0.67 ± 0.60 ppbv/year in the ten-year period, due possibly to reduction of emissions in North America. Downward mixing from the upper troposphere and stratosphere due to the persistent Azores-Bermuda anticyclone causes enhanced O 3 and nitrogen oxides. The d [O 3 ]/d [CO] value was used to investigate O 3 sources and chemistry in different transport patterns. The transport pattern affected by Fire had the lowest d [O 3 ]/d [CO], which was likely due to intense CO production and depressed O 3 production in wildfire plumes. Slightly enhanced O 3 and d [O 3 ]/d [CO] were found in the background air, suggesting that weak downward mixing from the upper troposphere is common at PMO. Enhancements of both butane isomers were found during upslope flow periods, indicating contributions from local sources. The consistent ratio of butane isomers associated with the background air and NA-anthro implies no clear difference in the oxidation rates of the butane isomers during long transport. Based on observed relationships between non-methane hydrocarbons, the averaged photochemical age of the air masses at PMO was estimated to be 11 ± 4 days.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Structural characterization of sugarcane lignins extracted from different protic ionic liquid pretreatments

Previous studies based on protic ionic liquids have shown them to be effective for extracting lignins from biomass. Moreover, ILs synthesized with amine cations can promote the insertion of nitrogen into lignin, favoring its valorization for industrial applications. Here, lignins extracted from sugarcane bagasse by eight different PILs were analyzed. Several techniques of characterization were employed (2D HSQC NMR, 31P NMR, GPC, Py-GC/MS, TGA, FTIR, and elemental analysis), seeking a deeper understanding of lignin structure post-PILS treatment. In particular, lignin obtained with acetate and lactate anions with monoethanolamonium cations showed similar structural compositions. Lignins obtained from ethylenediamonium cation combined anions acetate and lactate exhibited relatively higher molecular weights compared to the other lignins studied. The elemental analysis of these lignins further revealed the presence of nitrogen in their structures, suggesting that amination occurred. In this work we used PILs not yet reported in the literature for this purpose, and consequently obtained lignins with specific structures. Overall, our results demonstrated that depending on the PILs used, lignins with distinct properties could be obtained which might be used for value-added applications. This work covered new advances in elucidation of lignin chemical structure and opened a new path for lignin valorization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluating Material Design Principles for Calcium-Ion Mobility in Intercalation Cathodes

Multivalent-ion batteries offer an alternative to Li-based technologies, with the potential for greater sustainability, improved safety, and higher energy density, primarily due to their rechargeable system featuring a passivating metal anode. Although a system based on the Ca 2+ /Ca couple is particularly attractive given the low electrochemical plating potential of Ca 2+ , the remaining challenge for a viable rechargeable Ca battery is to identify Ca cathodes with fast ion transport. In this work, a high-throughput computational pipeline is adapted to (1) discover novel Ca cathodes in a largely unexplored space of empty intercalation hosts and (2) develop material design rules for Ca-ion mobility. One candidate from the screening, W 2 O 3 (PO 4 ) 2 , is confirmed to have a low Nudged Elastic Band (NEB) barrier of 168 meV within a one-dimensional (1D) ion percolation topology. This candidate is subsequently synthesized and electrochemically tested, achieving reversible Ca cycling with a capacity of 25 mA h/g. To further accelerate the screening for promising Ca intercalation electrodes, machine learning (ML) Random Forest (RF) and Extreme Gradient Boosting (XGB) classification models are created with local environment descriptors based on a large, structurally and chemically diverse dataset of minimum energy pathways, spanning over 5,000 density functional theory (DFT) site energy calculations. Accuracies of 92% are achieved, material design metrics are quantified, ML force-fields are leveraged in an accelerated iteration of the screening, and a total of 27 novel Ca cathode materials are highlighted for further investigation.

25 ENERGY STORAGE↗