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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Consideration of Ecosystem for ICME

As the Integrated Computational Materials Engineering (ICME) emerges as a hot topic, computation, experimentation, and digital database are identified as its three major components. Efforts are being actively made from various aspects to bring ICME to reality. However, many factors that would affect ICEM development still remain vague. This paper is an attempt to discuss the needs for establishing a database centered ecosystem to facilitate ICEM development.

cyber infrastructure↗
DOE OSTI2014

Regulatory and Permitting Information Desktop (RAPID) Toolkit (Poster)

The Regulatory and Permitting Information Desktop (RAPID) Toolkit combines the former Geothermal Regulatory Roadmap, National Environmental Policy Act (NEPA) Database, and other resources into a Web-based tool that gives the regulatory and utility-scale geothermal developer communities rapid and easy access to permitting information. RAPID currently comprises five tools - Permitting Atlas, Regulatory Roadmap, Resource Library, NEPA Database, and Best Practices. A beta release of an additional tool, the Permitting Wizard, is scheduled for late 2014. Because of the huge amount of information involved, RAPID was developed in a wiki platform to allow industry and regulatory agencies to maintain the content in the future so that it continues to provide relevant and accurate information to users. In 2014, the content was expanded to include regulatory requirements for utility-scale solar and bulk transmission development projects. Going forward, development of the RAPID Toolkit will focus on expanding the capabilities of current tools, developing additional tools, including additional technologies, and continuing to increase stakeholder involvement.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Monitoring Wildlife Interactions with Their Environment: An Interdisciplinary Approach

In a rapidly changing world, wildlife ecologists strive to correctly model and predict complex relationships between animals and their environment, which facilitates management decisions impacting public policy to conserve and protect delicate ecosystems. Recent advances in monitoring systems span scientific domains, including animal and weather monitoring devices and landscape classification mapping techniques. The current challenge is how to combine and use detailed output from various sources to address questions spanning multiple disciplines. WolfScout wildlife and weather tracking system is a software tool capable of filling this niche. WolfScout automates integration of the latest technological advances in wildlife GPS collars, weather stations, drought conditions, and severe weather reports, and animal demographic information. The WolfScout database stores a variety of classified landscape maps including natural and manmade features. Additionally, WolfScout’s spatial database management system allows users to calculate distances between animals’ location and landscape characteristics, which are linked to the best approximation of environmental conditions at the animal’s location during the interaction. Through a secure website, data are exported in formats compatible with multiple software programs including R and ArcGIS. The WolfScout design promotes interoperability in data, between researchers, and software applications while standardizing analyses of animal interactions with their environment.

Spatial database management system, Wildlife, Weat↗
DOE OSTI2018

An Open-Access Nuclear Knowledge and Innovation Toolkit to Make Use of Nuclear World and Increase Collaboration

Nuclear engineering began as an urgent weapons development program in the early 1940s, involving work in enrichment, reactor design, shielding design, and isotope production. This work led to investigations of peaceful uses of nuclear technology, including nuclear power production and applications of radiation. Such peaceful technologies, often derived from experience gained through the weapons program, often maintained some level or attitude of secrecy. As the field continued to develop, that secrecy led to different specializations developing with different sets of terminology and methods of computation. For example, radiation safety used buildup and shielding factors while reactor designers use diffusivity and cross sections. Impressive amounts of work went into theory and application development in each sub-field over the next few decades, with much of the work left without adequate documentation or with important documentation made only within the organization. This situation has led to a loss of knowledge and experience, a lack of collaboration across nuclear sub-fields, and immense amounts of time and effort spent on redundant work. This paper proposes an open-access system that combines preserving and passing on the knowledge in all nuclear sub-fields with tools to make that knowledge useful, referred to as the Nuclear Knowledge and Innovation Toolkit (NuKIT). Knowledge would be captured in user-entered and expert-verified databases of reactors, applications of radiation, isotope production methods, and other pertinent nuclear information complete with technical information and tags that can be used to organize and investigate the data. Tools include community tools like forums and the ability to create collaborative projects; visualization and presentation tools; and an online programming system that allows code sharing, collaborative editing, database manipulation, and integration with all other tools. Finally, NuKIT is envisioned to include commercialization tools to allow freelance work and to compensate data entry/moderation work to help encourage participation. Eventually, integration with industry-standard tools like MCNP, SCALE, etc. is expected. This paper will present some proposed work in the context of its use for isotopes and radiation application, as well as possible work to be done in the context of the Combined Materials Experimental Toolkit (CoMET) being developed by the Nuclear Science User Facilities (NSUF) program. It should be noted that any work done towards this system, by any group, will advance the full system. It should also be noted that similar work previously done can be integrated into NuKIT.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

BETO 2021 Peer Review - Electrocatalytic CO2 Utilization

The goal of the ChemCatBio DataHub project is to accelerate the catalyst and process development cycle by developing transformational tools for prediction and collaboration in catalyst R&D. The project is currently focused on the development of the Catalyst Property Database (CPD), a free and public resource released in September 2020. The CPD was designed to advance the state of the art for application of computational data. When computational data, such as computed reaction energetics, is used in catalyst design, it is almost always generated by the researchers seeking to use it, even if similar data has been published previously. One barrier to data reuse that results in this duplication of effort is the difficult process of finding and applying published data, which can be slow, error-prone, and manual. The CPD seeks to overcome these challenges by creating a centralized, searchable database of quality catalyst property data. At present, the CPD contains computed adsorption energies for intermediates along catalytic pathways. During FY21 and FY22, development of the CPD continues with a focus on external users and meeting their requirements. A batch upload capability, training and curation procedures, user interviews, and a demonstration of the CPD's utility in accelerating catalyst research are planned. Overall, the Data Hub project and CPD aim to reduce the time and cost of catalyst research by harnessing the power of data in catalyst discovery.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

BETO 2021 Peer Review - ChemCatBio Data Hub 2.6.2.500

The goal of the ChemCatBio DataHub project is to accelerate the catalyst and process development cycle by developing transformational tools for prediction and collaboration in catalyst R&D. The project is currently focused on the development of the Catalyst Property Database (CPD), a free and public resource released in September 2020. The CPD was designed in response to the observation that when data, such as computed reaction energetics, is used in catalyst design, it is almost always generated by the researchers seeking to use it, even if similar data has been published previously. One barrier to data reuse that results in this duplication of effort is the difficult process of finding and applying published data, which can be slow, error-prone, and manual. The CPD seeks to overcome these challenges by creating a centralized, searchable database of quality catalyst property data. At present, the CPD contains computed adsorption energies for intermediates along catalytic pathways. During FY21 and FY22, development of the CPD continues with a focus on external users and meeting their requirements. A batch upload capability, training and curation procedures, user interviews, and a demonstration of the CPD's utility in accelerating catalyst research are planned. Overall, the Data Hub project and CPD aim to reduce the time and cost of catalyst research by harnessing the power of data in catalyst discovery.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Assessing the Performance of a Circular Economy for Wind Energy Technologies: A Summary of Three Analytical Tools

A circular economy emphasizes the efficient use of all resources and presents opportunities for addressing series of economic and environmental objectives at local, regional, and national levels. Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. As a result, it is important to evaluate the performance and tradeoffs associated with circular economy transitions. This poster summaries three previously published analytical tools that were used to assess the performance of developing a circular economy for wind energy technologies: the Renewable Energy Materials Properties Database (REMPD), a circular economy agent-based model for wind blades (CE Wind ABM), and the Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework. The REMPD is a comprehensive database of materials used in wind and solar plants, including material quantities and physical materials availability. The CE Wind ABM allows us to understand how wind stakeholders' end-of-life behaviors influence wind blade circularity and evaluate the impact of regional variables (e.g., logistics and transportation). And, the CELAVI framework is a modular framework that can be used to evaluate the impacts associated with circular economy transitions. These three analytical tools have been applied to evaluate circular economy transitions for wind energy technologies and they could be expanded to other technologies and products.

agent-based modeling↗
DOE OSTI2025

SEAFORML (Smart Exploration and Analysis For Optimal and Robust Machine Learning)

The poster discusses data analysis of the WAVgraph database and applied machine learning methods for it. The database is a long-term project that seeks to be a comprehensive repository of information on cyber threats and is updated regularly. It was previously unanalyzed and unexplored. The goal was to learn more about it and its contents in order to have a better understanding and enable better use. The data analysis and discovery enabled further exploration through natural language processing, similarity, and clustering methods. The poster shows some of the insights from the analysis and explains the methods used for the machine learning applications.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Russian anthropogenic black carbon: Emission reconstruction and Arctic black carbon simulation

Development of reliable source emission inventories is needed to advance the understanding of the origin of Arctic haze using chemical transport modeling. This paper develops a regional anthropogenic black carbon (BC) emission inventory for the Russian Federation, the largest country by land area in the Arctic Council. Activity data from combination of local Russia information and international resources, emission factors based on either Russian documents or adjusted values for local conditions, and other emission source data are used to approximate the BC emissions. Emissions are gridded at a resolution of 0.1° × 0.1° and developed into a monthly temporal profile. Total anthropogenic BC emission of Russia in 2010 is estimated to be around 224 Gg. Gas flaring, a commonly ignored black carbon source, contributes a significant fraction of 36.2% to Russia's total anthropogenic BC emissions. Other sectors, i.e., residential, transportation, industry, and power plants, contribute 25.0%, 20.3%, 13.1%, and 5.4%, respectively. Three major BC hot spot regions are identified: the European part of Russia, the southern central part of Russia where human population densities are relatively high, and the Urals Federal District where Russia's major oil and gas fields are located but with sparse human population. BC simulations are conducted using the hemispheric version of Community Multi-scale Air Quality Model with emission inputs from a global emission database EDGAR (Emissions Database for Global Atmospheric Research)-HTAPv2 (Hemispheric Transport of Air Pollution) and EDGAR-HTAPv2 with its Russian part replaced by the newly developed Russian BC emissions, respectively. The simulation using the new Russian BC emission inventory could improve 30–65% of absorption aerosol optical depth measured at the AERONET sites in Russia throughout the whole year as compared to that using the default HTAPv2 emissions. At the four ground monitoring sites (Zeppelin, Barrow, Alert, and Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out-of-date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. Finally, this study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Alkali trace elements in Gale crater, Mars, with ChemCam: Calibration update and geological implications

The Chemistry Camera (ChemCam) instrument onboard Curiosity can detect minor and trace elements such as lithium, strontium, rubidium, and barium. Their abundances can provide some insights about Mars' magmatic history and sedimentary processes. We focus on developing new quantitative models for these elements by using a new laboratory database (more than 400 samples) that displays diverse compositions that are more relevant for Gale crater than the previous ChemCam database. These models are based on univariate calibration curves. For each element, the best model is selected depending on the results obtained by using the ChemCam calibration targets onboard Curiosity. New quantifications of Li, Sr, Rb, and Ba in Gale samples have been obtained for the first 1000 Martian days. Comparing these data in alkaline and magnesian rocks with the felsic and mafic clasts from the Martian meteorite NWA7533—from approximately the same geologic period—we observe a similar behavior: Sr, Rb, and Ba are more concentrated in soluble- and incompatible-element-rich mineral phases (Si, Al, and alkali-rich). Correlations between these trace elements and potassium in materials analyzed by ChemCam reveal a strong affinity with K-bearing phases such as feldspars, K-phyllosilicates, and potentially micas in igneous and sedimentary rocks. However, lithium is found in comparable abundances in alkali-rich and magnesium-rich Gale rocks. This very soluble element can be associated with both alkali and Mg-Fe phases such as pyroxene and feldspar. Here, these observations of Li, Sr, Rb, and Ba mineralogical associations highlight their substitution with potassium and their incompatibility in magmatic melts.

58 GEOSCIENCES↗
DOE OSTI2017

Modeling Global Soil Carbon and Soil Microbial Carbon by Integrating Microbial Processes into the Ecosystem Process Model TRIPLEX-GHG

Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process-based model TRIPLEX-GHG was developed by coupling it with the new MEND (Microbial-ENzyme-mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX-MICROBE) shows considerable improvement over the previous version (TRIPLEX-GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well-regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated. We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC) and mineral-associated organic carbon (MOC). Furthermore, our work represents the first step towards a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

Abstract The work function is the key surface property that determines the energy required to extract an electron from the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. This work presents a high‐throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that are created from 3,716 bulk materials. The number of calculated surface properties surpasses the previously largest database by a factor of ≈27. Several surfaces with an ultra‐low (<2 eV) and ultra‐high (>7 eV) work function are identified. Specifically, the (100)‐Ba‐O surface of BaMoO 3 and the (001)‐F surface of Ag 2 F have record‐low (1.25 eV) and record‐high (9.06 eV) steady‐state work functions. Based on this database a physics‐based approach to featurize surfaces is utilized to predict the work function. The random forest model achieves a test mean absolute error (MAE) of 0.09 eV, comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function (≈ 10 5 faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic device applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Accelerated Discovery of CH 4 Uptake Capacity Metal–Organic Frameworks Using Bayesian Optimization

Abstract High‐throughput computational studies for discovery of metal–organic frameworks (MOFs) for separations and storage applications are often limited by the costs of computing thermodynamic quantities. Recent such studies at the time of writing may use ab initio results for a narrow selection of MOFs or empirical force‐field methods for larger selections. Here, a proof‐of‐concept study is conducted using Bayesian optimization on CH 4 uptake capacity of hypothetical MOFs for an existing dataset (Wilmer et al., Nature Chem. 2012, 4 , 83). It is shown that less than 0.1% of the database needs to be screened with the Bayesian optimization approach to recover the top candidate MOFs. This opens the possibility for efficient screening of MOF databases using accurate ab initio calculations for future adsorption studies on a minimal subset of MOFs. Furthermore, Bayesian optimization and the surrogate model presented here can offer interpretable material design insights and the framework will be applicable in the context of other target properties.

Taw, Eric↗
DOE OSTI2025

MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature

Abstract Efficiently extracting data from tables in the scientific literature is pivotal for building large‐scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule‐based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT‐based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow‐up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero‐shot, few‐shot, and fine‐tuning, the study presents a Pareto‐front mapping where the few‐shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score >95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.

Yi, Gyeong Hoon [Computational Science Research Ce↗
DOE OSTI2018

A framework for the identification of promising bio-based chemicals

Recent progress in metabolic engineering and synthetic biology enables the use of microorganisms for the production of chemicals (“bio-based chemicals”). Yet, it is still unclear which chemicals have the highest economic prospect. Because of this, we develop a framework for the identification of such promising ones. Specifically, we first develop a genome-scale constraintbased metabolic modeling approach, which is used to identify a candidate pool of 209 chemicals (together with the estimated yield, productivity and residence time for each) from the intersection of the High-Production-Volume (HPV) chemicals and the KEGG and MetaCyc databases. Second, we design three screening criteria based on a chemical’s profit margin, market volume and market size. The total process cost, including the downstream separation cost, is systematically incorporated into the evaluation. Third, given the three aforementioned criteria, we identify 32 products as economically promising if the maximum yields can be achieved, and 22 products if the maximum productivities can be achieved. The breakeven titer that renders zero profit margin for each product is also presented. Comparisons between extracellular and intracellular production, as well as E. coli and S. cerevisiae systems are also discussed. The proposed framework provides important guidance for future studies in the production of bio-based chemicals. It is also flexible in that the databases, yield estimations, and criteria can be modified to customize the screening.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

User applications driven by the community contribution framework MPContribs in the Materials Project

This paper discusses how the MPContribs framework in the Materials Project (MP) allows user-contributed data to be shown and analyzed alongside the core MP database. The MP is a searchable database of electronic structure properties of over 65,000 bulk solid materials, which is accessible through a web-based science-gateway. We describe the motivation for enabling user contributions to the materials data and present the framework's features and challenges in the context of two real applications. These use cases illustrate how scientific collaborations can build applications with their own 'user-contributed' data using MPContribs. The Nanoporous Materials Explorer application provides a unique search interface to a novel dataset of hundreds of thousands of materials, each with tables of user-contributed values related to material adsorption and density at varying temperature and pressure. The Unified Theoretical and Experimental X-ray Spectroscopy application discusses a full workflow for the association, dissemination, and combined analyses of experimental data from the Advanced Light Source with MP's theoretical core data, using MPContribs tools for data formatting, management, and exploration. The capabilities being developed for these collaborations are serving as the model for how new materials data can be incorporated into the MP website with minimal staff overhead while giving powerful tools for data search and display to the user community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

BrazilClim : The overcoming of limitations of pre‐existing bioclimate data

Abstract Species distribution modelling has become instrumental in assessing the influence of environmental conditions on the occurrence or abundance of taxa. The set of environmental layers used for this purpose is a crucial aspect, for which different climate‐based (bioclimatic) datasets have been recently developed. These bioclimatic variables result from combinations of precipitation and temperatures surfaces. Here, we explored both the performance and possibility of improving some of the currently available bioclimatic databases, through an evaluation of the precipitation and temperatures surfaces used to generate them. For this purpose, we used a combination of statistic and graphic approaches. We focused on Brazil, not only due to its natural megadiversity, but also due to its continental size and orographic heterogeneity: an excellent ground for refining methods replicable elsewhere. We found a better match between the climatic data measured on‐field and Tropical Rainfall Measuring Mission (TRMM 3B43 v7) in the case of precipitation, and the surfaces provided by the National Oceanic and Atmospheric Administration (NOAA) in the case of temperatures, sources uncommonly used for species niche modelling. We gauge‐calibrated the best performing surfaces using machine‐learning algorithms and generated corrected surfaces that allowed us to create BrazilClim: a database of bioclimatic variables, based on improved primary surfaces, which will result in more assertive predicted distributions and more actual pictures of the species' ecological requirements for megadiverse Brazil, an approach replicable elsewhere. All primary and bioclimatic surfaces generated for this study may be freely downloaded.

Ramoni‐Perazzi, Paolo↗
DOE OSTI2021

Machine learning for polymeric materials: an introduction

We report that Polymers are incredibly versatile materials and have become ubiquitous. Increasingly, researchers are using data science and polymer informatics to design new materials and understand their structure-property relationships. Polymer informatics is an emerging field. While there are many useful tools and databases available, many are not widely utilized. Herein, we introduce the field of polymer informatics and discuss some of the available databases and tools. We cover how to share polymer data, approaches for preparing a dataset for machine learning and recent applications of machine learning to polymer property prediction and polymer synthesis.

36 MATERIALS SCIENCE↗