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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Expanding the role of reactive transport models in critical zone processes

We report that models test our understanding of processes and can reach beyond the spatial and temporal scales of measurements. Multi-component Reactive Transport Models (RTMs), initially developed more than three decades ago, have been used extensively to explore the interactions of geothermal, hydrologic, geochemical, and geobiological processes in subsurface systems. Driven by extensive data sets now available from intensive measurement efforts, there is a pressing need to couple RTMs with other community models to explore non-linear interactions among the atmosphere, hydrosphere, biosphere, and geosphere. Here we briefly review the history of RTM development, summarize the current state of RTM approaches, and identify new research directions, opportunities, and infrastructure needs to broaden the use of RTMs. In particular, we envision the expanded use of RTMs in advancing process understanding in the Critical Zone, the veneer of the Earth that extends from the top of vegetation to the bottom of groundwater. We argue that, although parsimonious models are essential at larger scales, process-based models offer tools to explore the highly nonlinear coupling that characterizes natural systems. We present seven testable hypotheses that emphasize the unique capabilities of process-based RTMs for (1) elucidating chemical weathering and its physical and biogeochemical drivers; (2) understanding the interactions among roots, micro-organisms, carbon, water, and minerals in the rhizosphere; (3) assessing the effects of heterogeneity across spatial and temporal scales; and (4) integrating the vast quantity of novel data, including “omics” data (genomics, transcriptomics, proteomics, metabolomics), elemental concentration and speciation data, and isotope data into our understanding of complex earth surface systems. In conclusion, with strong support from data-driven sciences, we are now in an exciting era where integration of RTM framework into other community models will facilitate process understanding across disciplines and across scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A method to generate heating and cooling schedules based on data from connected thermostats

Internet-connected thermostats are a promising new source of temperature and operational data in homes because they record inside temperatures, setpoints, and HVAC runtimes every five minutes. Over 20 million Internet-connected thermostats have already been installed in American homes. Data from about 20,000 connected thermostats were collected and organized by climate zone, number of occupants, floor area, and day type. A method was developed to create up to 40 representative temperature schedules which, together, can more accurately capture the diversity of heating and cooling behaviors. These results are suitable for input into schedules for building energy simulation models. This information enables more realistic simulations of American heating and cooling behavior, leading to more accurate estimates of energy consumption and savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Metal hydride composition-derived parameters as machine learning features for material design and H 2 storage

Though hydrogen is a promising energy carrier for a green future, many challenges persist. One is the difficulty in engineering storage solutions, with metal hydrides being a leading contender among solid-state strategies. To facilitate efficient searching of candidate materials, ridge regression, simple decision trees, random forest ensembles, and gradient boosting ensembles were employed to predict the energy of formation, with the random forest ensemble resulting in the lowest test set error. First, two public databases, Materials Project and HydPark, were searched for metal hydrides. Feature engineering was performed before the models were developed, resulting in electronegativity, density, atomic density, d-character, f-character, band gap, hydrogen weight fraction, magnetization, temperature, and pressure being retained. The models were then benchmarked by the lowest test error before a random forest ensemble was used to populate entries missing energy of formation. Furthermore, all were then scored by hydrogen storage capacity and energy of formation suitability. Readily available features including several derived from only the chemical formula which were found to be highly predictive. and so are promising for high-throughput screening of arbitrary novel hydride formulations and blends for thermodynamic feasibility.

25 ENERGY STORAGE↗
DOE OSTI2024

A novel methodology for gamma-ray spectra dataset procurement over varying standoff distances and source activities

The adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including expected pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ratio (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) model are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing a problem space, ranging from 5 cm to 100 cm standoff distances and 5 μCi–100 μCi of 137 Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy.

data science↗
DOE OSTI2022

Expanded analysis of machine learning models for nuclear transient identification using TPOT

Industries around the world are becoming more and more data driven. The nuclear field is no exception with several different applications being proposed. One popular area of research is the use of machine learning in transient detection. This paper seeks to build upon a previous study which made use of the AutoML package TPOT to train traditional machine learning models to classify transient events occurring with a reactor. Synthetic data was once again collected using a GPWR reactor simulator. Data on 12 different events was collected using 15 different initial conditions. Here, a dataset consisting of over 100,000 data points was compiled and used to train 7 different machine learning models using a pre-defined TPOT dictionary with 12 different preprocessing techniques. Three of the trained models were able to produce validation results in the 90s with the expanded dataset. Once the models were trained, it was possible to look into where during the simulation, misclassifications occurred. Using these three models, analysis was done to determine if TPOT could be used to train models that were effective if important features were missing. The results from this were positive with the newly trained models scoring close to the original models. Finally, to conclude this study, the three high performing models were retrained using different random states to see if there was any major variation when different states were used.

42 ENGINEERING↗
DOE OSTI2018

A non-destructive method for crack quantification in photovoltaic backsheets under accelerated and real-world exposures

The long-term durability of photovoltaic modules is paramount for the continued growth of the industry. Polymer backsheets are of particular concern since they provide electrical insulation and an environmental barrier. In this paper, 23 freestanding, multilayer backsheets with nine unique material combinations underwent four different weathering exposures under accelerated and real-world conditions. Besides changes in color and gloss, the induced degradation included parallel or mudflat cracks on 11 backsheets, sometimes in combination with delamination or blistering. Similar degradation has been observed in previous studies and is concerning since cracks compromise the mechanical integrity and electrical safety of backsheets. Quantitative parameters are desirable to reliably classify categories of cracks and supply unbiased features for statistical analysis in predictive lifetime models. We developed an analysis technique that utilizes surface profilometry data to quantify the depth, width, area, spacing, and number of cracks. Parameters are automatically extracted from the raw data by an algorithm running on a high performance distributed computing cluster. Our algorithm excelled at characterizing parallel cracks with minimal de-adhesion, and only an estimated 4% of crack detections were false positives. The addition of humidity and temperature variation formed up to three times as many cracks on a photodose basis compared to dry, constant temperature exposures. Cracks in real-world and accelerated exposures propagated to similar depths with equivalent photodoses; however, the number of cracks formed in accelerated exposures was far greater on a photodose basis. Of samples that cracked, the best performing backsheet configuration was polyvinyl fluoride/poly (ethylene-terephthalate)/polyethylene (PVF/PET/PE) while the least durable was PET/PET/ethylene-vinyl acetate. Finally, none of the six PVF/PET/PVF backsheets cracked in any of the exposures.

14 SOLAR ENERGY↗
DOE OSTI2017

Investigation of a Quantum Monte Carlo Protocol To Achieve High Accuracy and High-Throughput Materials Formation Energies

High-throughput calculations based on density functional theory (DFT) methods have been widely implemented in the scientific community. However, depending on both the properties of interest as well as particular chemical/structural phase space, accuracy even for correct trends remains a key challenge for DFT. In this work, we evaluate the use of quantum Monte Carlo (QMC) to calculate material formation energies in a high-throughput environment. We test the performance of automated QMC calculations on 21 compounds with high quality reference data from the Committee on Data for Science and Technology (CODATA) thermodynamic database. We compare our approach to different DFT methods as well as different pseudopotentials, showing that errors in QMC calculations can be progressively improved especially when correct pseudopotentials are used. We determine a set of accurate pseudopotentials in QMC via a systematic investigation of multiple available pseudopotential libraries. We show that using this simple automated recipe, QMC calculations can outperform DFT calculations over a wide set of materials. Out of 21 compounds tested, chemical accuracy has been obtained in formation energies of 11 structures using our QMC recipe, compared to none using DFT calculations.

Chemistry↗
DOE OSTI2018

Extracting Knowledge from Data through Catalysis Informatics

Catalysis informatics is a distinct sub-field that lies at the intersection of cheminformatics and materials informatics, but with unique challenges arising from the dynamic, surface-sensitive, multi-scale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data-information-knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and micro-kinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced micro-kinetic models, and the development of open-source software tools. As a result, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Impacts of Observational Constraints Related to Sea Level on Estimates of Climate Sensitivity

Reduced complexity climate models are necessary tools for quantifying decision-relevant uncertainties, given their flexibility, computational efficiency, and suitability for large-ensemble frameworks necessary for statistical estimation using resampling techniques (e.g. Markov chain Monte Carlo—MCMC). Here we document a new version of the simple, open-source, global climate model Hector, coupled with a one-dimensional diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model; DOECLIM) and a sea-level change module (Building blocks for Relevant Ice and Climate Knowledge; BRICK) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. We apply a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters with prescribed radiative forcing, using observational information from global surface temperature, ocean heat uptake, and sea-level change. Furthermore, we find sea-level change as an observational constraint sharpens inference for the upper tail of posterior climate sensitivity estimates (the 97.5 percentile is tightened from 6.5 K to 5.3 K). The addition of sea-level change as an observational constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5 2100 temperature decreases 0.3 K) and sea-level rise (the 97.5 percentile for RCP8.5 2100 sea level decreases 0.5 m).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

Zeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure. To date, 213 framework types have been synthesized and >330000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a large-scale, multi-step computational screening process, promising zeolite structures for two energy-related applications: the purification of ethanol from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. Finally, these results demonstrate that predictive modeling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CRISPRi-ART enables functional genomics of diverse bacteriophages using RNA-binding dCas13d

Bacteriophages constitute one of the largest reservoirs of genes of unknown function in the biosphere. Even in well-characterized phages, the functions of most genes remain unknown. Experimental approaches to study phage gene fitness and function at genome scale are lacking, partly because phages subvert many modern functional genomics tools. Here we leverage RNA-targeting dCas13d to selectively interfere with protein translation and to measure phage gene fitness at a transcriptome-wide scale. We find CRISPR Interference through Antisense RNA-Targeting (CRISPRi-ART) to be effective across phage phylogeny, from model ssRNA, ssDNA and dsDNA phages to nucleus-forming jumbo phages. Using CRISPRi-ART, we determine a conserved role of diverse rII homologues in subverting phage Lambda RexAB-mediated immunity to superinfection and identify genes critical for phage fitness. CRISPRi-ART establishes a broad-spectrum phage functional genomics platform, revealing more than 90 previously unknown genes important for phage fitness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes

In the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Learning representations of microbe–metabolite interactions

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We illustrate with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Improvement of eukaryotic protein predictions from soil metagenomes

During the last decades, metagenomics has highlighted the diversity of microorganisms from environmental or host-associated samples. Most metagenomics public repositories use annotation pipelines tailored for prokaryotes regardless of the taxonomic origin of contigs. Consequently, eukaryotic contigs with intrinsically different gene features, are not optimally annotated. Using a bioinformatics pipeline, we have filtered 7.9 billion contigs from 6,872 soil metagenomes in the JGI’s IMG/M database to identify eukaryotic contigs. We have re-annotated genes using eukaryote-tailored methods, yielding 8 million eukaryotic proteins and over 300,000 orphan proteins lacking homology in public databases. Comparing the gene predictions we made with initial JGI ones on the same contigs, we confirmed our pipeline improves eukaryotic proteins completeness and contiguity in soil metagenomes. The improved quality of eukaryotic proteins combined with a more comprehensive assignment method yielded more reliable taxonomic annotation. This dataset of eukaryotic soil proteins with improved completeness, quality and taxonomic annotation reliability is of interest for any scientist aiming at studying the composition, biological functions and gene flux in soil communities involving eukaryotes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Informing selection of drugs for COVID-19 treatment through adverse events analysis

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and there is an urgent need for safe and effective drugs for COVID-19 treatment. Since developing a new drug is time consuming, many approved or investigational drugs have been repurposed for COVID-19 treatment in clinical trials. Therefore, selection of safe drugs for COVID-19 patients is vital for combating this pandemic. Our goal was to evaluate the safety concerns of drugs by analyzing adverse events reported in post-market surveillance. We collected 296 drugs that have been evaluated in clinical trials for COVID-19 and identified 28,597,464 associated adverse events at the system organ classes (SOCs) level in the FDA adverse events report systems (FAERS). We calculated Z-scores of SOCs that statistically quantify the relative frequency of adverse events of drugs in FAERS to quantitatively measure safety concerns for the drugs. Analyzing the Z-scores revealed that these drugs are associated with different significantly frequent adverse events. Our results suggest that this safety concern metric may serve as a tool to inform selection of drugs with favorable safety profiles for COVID-19 patients in clinical practices. Caution is advised when administering drugs with high Z-scores to patients who are vulnerable to associated adverse events.

59 BASIC BIOLOGICAL SCIENCES↗