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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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Objectives: Provide experimental data to feed back in to models for high-throughput predictions of potential new scintillator materials and identify promising scintillator compounds to pursue further.
Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.
The experimental techniques employed by NASA researchers to obtain a high volume throughput of data in a study of high-lift multielement airfoils using a laser velocimeter (LV) are detailed. The two-dimensional LV system recorded mean velocity, turbulence intensities, Re shear stresses, skewness and kurtosis of the flow. A computer-controlled positioning mount for the LV allowed automated surveys of the flows with a 0.001 in. positioning accuracy. The flow was seeded with oil droplets and additional refinement of the wind tunnel controls permitted rapid speedup at selected areas of the flowfield over specific regions of the airfoil. Surface pressure taps furnished data for selecting regions for more detailed investigation. The data processing capabilities processed 30-40 samples every 10 min in an automated mode. Profiles of the data were computed automatically to furnish graphical displays on demand. Numerous samples of the data displays generated are provided.
Abstract The Escherichia coli genome‐scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision–recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene‐protein‐reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high‐throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.
Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.
Abstract Drought is a major abiotic stress limiting agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere. How these changes ultimately affect plant health remains elusive. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key root-associated microbes that robustly affect drought-stressed plant phenotypes. Fifty-three Arabidopsis-associated bacteria were applied to sorghum seeds and their effect on root growth was monitored. Two Arthrobacter strains caused root growth inhibition (RGI) in Arabidopsis and sorghum. In the context of synthetic communities, Variovorax strains were able to protect plants from Arthrobacter-caused RGI. As a transitional system, high-throughput phenotyping was used to test the synthetic communities. During drought stress, plants colonized by Arthrobacter had reduced growth and leaf water content. Plants colonized by both Arthrobacter and Variovorax performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across drought conditions. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a novel method and were able to observe the negative correlation between the abundance of Arthrobacter and plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of bacteria with high confidence that positively associated with plant growth under drought stress. In conclusion, a three-tiered experimental system successfully spanned the lab-to-field gap and identified beneficial and deleterious bacterial strains for sorghum under drought.
The experimental NASA satellite, Advanced Communications Technology Satellite (ACTS), introduces new technology for high throughput 30 to 20 GHz satellite services. Contained in a single communication payload is both a regenerative TDMA system and multiple 800 MHz 'bent pipe' channels routed to spot beams by a switch matrix. While only one mode of operation is typical during any experiment, both modes can operate simultaneously with reduced capability due to sharing of the transponder. NASA-Lewis instituted a ground terminal development program in anticipation of the satellite launch to verify the performance of the switch matrix mode of operations. Specific functions are built into the ground terminal to evaluate rain fade compensation with uplink power control and to monitor satellite transponder performance with bit error rate measurements. These functions were the genesis of the ground terminal's name, Link Evaluation Terminal, often referred to as LET. Connectors are included in LET that allow independent experimenters to run unique modulation or network experiments through ACTS using only the RF transmit and receive portions of LET. Test data indicate that LET will be able to verify important parts of ACTS technology and provide independent experimenters with a useful ground terminal. Lab measurements of major subsystems integrated into LET are presented. Bit error rate is measured with LET in an internal loopback mode.
The experimental NASA satellite, Advanced Communications Technology Satellite (ACTS), introduces new technology for high throughput 30 to 20 GHz satellite services. Contained in a single communication payload is both a regenerative TDMA system and multiple 800 MHz 'bent pipe' channels routed to spot beams by a switch matrix. While only one mode of operation is typical during any experiment, both modes can operate simultaneously with reduced capability due to sharing of the transponder. NASA-Lewis instituted a ground terminal development program in anticipation of the satellite launch to verify the performance of the switch matrix mode of operations. Specific functions are built into the ground terminal to evaluate rain fade compensation with uplink power control and to monitor satellite transponder performance with bit error rate measurements. These functions were the genesis of the ground terminal's name, Link Evaluation Terminal, often referred to as LET. Connectors are included in LET that allow independent experimenters to run unique modulation or network experiments through ACTS using only the RF transmit and receive portions of LET. Test data indicate that LET will be able to verify important parts of ACTS technology and provide independent experimenters with a useful ground terminal. Lab measurements of major subsystems integrated into LET are presented. Bit error rate is measured with LET in an internal loopback mode.
In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.
The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure fromindirectmeasures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.
Molecular hydrogen has the potential to significantly reduce the use of carbon dioxide emitting energy processes. However, hydrogen gas storage is a major bottleneck for its large-scale use as current storage methods are energy intensive. Among different storage methods, physisorbing molecular hydrogen at ambient pressure and temperatures is a promising alternative—particularly in light of the advancements in tunable lightweight nanomaterials and high throughput screening methods. Nonetheless, understanding hydrogen adsorption in well-defined nanomaterials remains experimentally challenging and reference information is scarce despite the proliferation of works predicting hydrogen adsorption. We focus on Li, Na, Ca, and K, decorated graphene sheets as substrates for molecular hydrogen adsorption, and compute the most accurate adsorption energies available to date using quantum diffusion Monte Carlo (DMC). Building on our previous insights at the density functional theory (DFT) level, we find that a weak covalent chemisorption of molecular hydrogen, known as Kubas interaction, is feasible on Ca decorated graphene according to DMC, in agreement with DFT. This finding is in contrast to previous DMC predictions of the 4H2/Ca+ gas cluster (without graphene) where chemisorption is not favored. However, we find that the adsorption energy of hydrogen on metal decorated graphene according to a widely used DFT method is not fully consistent with DMC. The reference adsorption energies reported herein can be used to find better work-horse methods for application in large-scale modeling of hydrogen adsorption. Furthermore, the implications of this work affect strategies for finding suitable hydrogen storage materials and high-throughput methods.
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 .
Encapsulated inorganic particles with high melting points (>300 °C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric & thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.
Cation-disordered ZnGeN 2 shows promise for application as a blue-green emitter in light-emitting devices, but more foundational work is necessary to understand structure–property relationships. In this work, we present a combinatorial exploration of the experimental phase space of wurtzite (cation-disordered) ZnGeN 2 using high-throughput co-sputtering. Structure, morphology and optical properties are explored as a function of cation composition and synthesis temperature. ZnGeN 2 is found to crystallize in the wurtzite structure ranging from Zn-rich to Ge-rich compositions. X-ray diffraction refinements reveal a continuous shift in cell volume with off-stoichiometry, indicating alloy-like structural behavior. Furthermore, the optical absorption of all films examined is lower in energy than the value predicted for cation-ordered ZnGeN 2 , suggesting that cation disorder is decreasing the bandgap. Additionally, the absorption threshold shifts continuously to higher energy for Ge-rich samples, consistent with bandgap shifts due to alloy-like structural behavior. Defect formation energy diagrams are calculated to help guide understanding of off-stoichiometry from a defect complex perspective. This work paves the way toward use of ZnGeN 2 as a bandgap-tunable optoelectronic semiconductor.
Nitride perovskites ABN3 are an emerging and highly underexplored class of materials that are of interest due to their intriguing calculated ferroelectric, optoelectronic, and other functional properties. Incorporating novel A-site cations is one strategy to tune and expand such properties; for example, Gd 3+ is compelling due to its large magnetic moment, potentially leading to multiferroic behavior. However, the theoretically predicted ground state of GdWN 3 was a non-perovskite monoclinic structure. Here, we experimentally show that GdWN 3-y crystallizes in a perovskite structure. High-throughput combinatorial sputtering with activated nitrogen is employed to synthesize thin films of Gd 2-x W x N 3-y O y with oxygen content y < 0.05. Ex situ annealing crystallizes a polycrystalline perovskite phase in a narrow composition window near x = 1. LeBail fits of synchrotron grazing incidence wide angle x-ray scattering data are consistent with a perovskite ground-state structure. Refined density functional theory calculations that included antiferromagnetic configurations confirm that the ground-state structure of GdWN 3 is a distorted Pnma perovskite with antiferromagnetic ordering, in contrast to prior predictions. Initial property measurements find that GdWN 3-y is paramagnetic down to T = 2 K with antiferromagnetic correlations and that the absorption onset depends on cation stoichiometry. This work provides an important path toward both the rapid expansion of the emerging family of nitride perovskites and understanding their potential multiferroic properties.
A high-throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd-containing perovskite oxide compositions Gd 2 BB'O 6 , GdA'B 2 O 6 , and GdA'BB'O 6 that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH TR and ΔS TR . This scheme uses the machine-learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO 6 octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic- and defect-sampling to predict STCH-relevant properties. Eighty-three promising STCH candidate perovskite oxides down-selected from 4392 Gd-containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. Our results demonstrate that the high-throughput computational scheme described herein—which is used to evaluate Gd-containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.
Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.