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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Interfacial negative capacitance in planar perovskite solar cells: An interpretation based on band theory

Highlights: • PSCs with different ETL strategies were characterized by EIS measurement under various conditions. • The negative capacitance solely occurred in the moderate frequency region when special conditions are met. • A model based on semiconductor band theory was proposed to interpret the corresponding mechanism. • This achievement provide important insight into the physical mechanism behind the negative capacitance of PSCs. - Abstract: The negative capacitance is a peculiar phenomenon frequently observed in electrochemical impedance spectroscopy (EIS) measurement for perovskite solar cells (PSCs). However, the origin and mechanism of the negative capacitance in PSCs remains ambiguous. Here, planar PSCs with different electron transport layer (ETL) strategies (SnO{sub 2}, TiO{sub 2}, or without ETL) were characterized by EIS in a wide range of frequencies under various conditions. The negative capacitance solely occurred in the moderate frequency region as the perovskite layer directly contacted with the conductive SnO{sub 2}: F (FTO) substrate under illumination and open-circuit conditions. Based on semiconductor band theory, the origin of negative capacitance to the formation of an inversion layer in the perovskite/FTO interface induced by the interfacial energy band over-bent under illumination and open-circuit conditions. Our study provides important insight into the physical mechanism behind the negative capacitance of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optical spectroscopy of molten fluorides: Methods, electronic and vibrational data, structural interpretation, and relevance to radiative heat transfer

In this study, to help address the need for predicting radiative heat transfer (RHT) behavior of molten salts, we conducted a comprehensive review of methods and data from optical spectroscopic measurements on molten fluoride salts. Transmittance, reflectance, and trans-reflectance experimental methods are discussed, along with the corresponding data reduction methodology and the limitations of each technique. Optical spectroscopy is a convenient indirect probe for changes in structural parameters with temperature and composition. Electronic and vibrational absorption data for transition-metal, lanthanide, and actinide solutes and vibrational absorption data for alkali and alkaline earth fluoride solvents are compiled, and the corresponding structural interpretation is discussed and compared with other experimental and theoretical work. We find that solvent and solute vibrational absorption can be significant in the mid-infrared, resulting in near-infrared edges of significance to RHT. Extrapolation and averaging of existing edge data leads to estimated gray absorption coefficient values at 700 °C of 546 m —1 for FLiBe and 276 m —1 for FLiNaK, both within the range of 1 – 6000 m —1 identified to be of engineering relevance for radiative heat transfer analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2015

2700 years of Mediterranean environmental change in central Italy: a synthesis of sedimentary and cultural records to interpret past impacts of climate on society

We report abrupt climate change in the past is thought to have disrupted societies by accelerating environmental degradation, potentially leading to cultural collapse. Linking climate change directly to societal disruption is challenging because socioeconomic factors also play a large role, with climate being secondary or sometimes inconsequential. Combining paleolimnologic, historical, and archaeological methods provides for a more secure basis for interpreting the past impacts of climate on society. We present pollen, non-pollen palynomorph, geochemical, paleomagnetic and sedimentary data from a high-resolution 2700 yr lake sediment core from central Italy and compare these data with local historical documents and archeological surveys to reconstruct a record of environmental change in relation to socioeconomic history and climatic fluctuations. Here we document cases in which environmental change is strongly linked to changes in local land management practices in the absence of clear climatic change, as well as examples when climate change appears to have been a strong catalyst that resulted in significant environmental change that impacted local communities. During the Imperial Roman period, despite a long period of stable, mild climate, and a large urban population in nearby Rome, our site shows only limited evidence for environmental degradation. Warm and mild climate during the Medieval Warm period, on the other hand, led to widespread deforestation and erosion. The ability of the Romans to utilize imported resources through an extensive trade network may have allowed for preservation of the environment near the Roman capital, whereas during medieval time, the need to rely on local resources led to environmental degradation. Cool wet climate during the Little Ice Age led to a breakdown in local land use practices, widespread land abandonment and rapid reforestation. Our results present a high-resolution regional case study that explores the effect of climate change on society for an under-documented region of Europe.

58 GEOSCIENCES↗
DOE OSTI2021

Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease

The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. Finally, we review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The importance of temporal and spatial incoherence in quantitative interpretation of 4D-STEM

Recent developments in pixelated detectors, when combined with aberration correction of probe forming optics have greatly enhanced the field of scanning electron diffraction. Differential phase contrast is now routine and deep learning has been proposed as a method to extract maximum information from diffraction patterns. This study examines the effects of temporal and spatial incoherence on convergent beam electron diffraction patterns and demonstrates that simple center of mass measurements cannot be naively interpreted. The inclusion of incoherence in deep learning data sets is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interpreting accelerated tests on perovskite modules using photooxidation of MAPbI 3 as an example

Solar panels (modules) based on metal halide perovskites are following a fast track to commercialization. Unlike more established solar cell materials, there are not yet decades-long field observations to increase consumer confidence. The "physics and chemistry of failure" approach is used in other industries and estimates product degradation based on laboratory accelerated tests integrated with an understanding of degradation mechanisms. This work uses that approach to quantify the relationship between accelerated tests and projected product behavior for metal halide perovskite modules. Degradation involving photooxidation of methylammonium lead iodide is used to illustrate the method. Acceleration factors in common accelerated tests are found to be low. Conclusions emphasize that the accelerated tests on photovoltaics should not be interpreted as equivalent across module types or as a green light for commercialization unless supported by the appropriate field data or physics and chemistry of failure analysis.

14 SOLAR ENERGY↗
DOE OSTI2025

PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data

Finding relevant chemicals in the vast (known) chemical space is a major challenge for environmental and exposomics studies leveraging nontarget high resolution mass spectrometry (NT-HRMS) methods. Chemical databases now contain hundreds of millions of chemicals, yet many are not relevant. This article details an extensive collaborative, open science effort to provide a dynamic collection of chemicals for environmental, metabolomics, and exposomics research, along with supporting information about their relevance to assist researchers in the interpretation of candidate hits. The PubChemLite for Exposomics collection is compiled from ten annotation categories within PubChem, enhanced with patent, literature and annotation counts, predicted partition coefficient (logP) values, as well as predicted collision cross section (CCS) values using CCSbase. Monthly versions are archived on Zenodo under a CC-BY license, supporting reproducible research, and a new interface has been developed, including historical trends of patent and literature data, for researchers to browse the collection. This article details how PubChemLite can support researchers in environmental and exposomics studies, describes efforts to increase the availability of experimental CCS values, and explores known limitations and potential for future developments. The data and code behind these efforts are openly available.

PubChem↗
DOE OSTI2016

Ab Initio Molecular Dynamics of Uranium Incorporated in Goethite (α-FeOOH): Interpretation of X-ray Absorption Spectroscopy of Trace Polyvalent Metals

Incorporation of economically or environmentally consequential polyvalent metals into iron (oxyhydr)oxides has applications in environmental chemistry, remediation, and materials science. A primary tool for characterizing the local coordination environment of such metals, and therefore building models to predict their behavior, is extended X-ray absorption fine structure spectroscopy (EXAFS). Accurate structural information can be lacking, yet is required to constrain and inform data interpretation. In this regard, ab initio molecular dynamics (AIMD) was used to calculate the local coordination environment of minor amounts of U incorporated in the structure of goethite (α-FeOOH). U oxidation state (VI, V, and IV) and charge compensation scheme (CCS) were varied. Simulated trajectories were used to calculate the U LIII-edge EXAFS function and fit experimental EXAFS data for U incorporated into goethite under reducing conditions. Calculations that closely matched the U EXAFS of the well-characterized mineral uraninite (UO2), and constrained the S02 parameter to be 0.909, validated the approach. The results for the U-goethite system indicated that U(V) substituted for structural Fe(III) in octahedral uranate coordination. Charge balance was achieved by the loss of one structural proton coupled to injection of one electron into the solid (–1 H+, + 1 e-). The ability of AIMD to model higher-energy states thermally accessible at room temperature is particularly relevant for protonated systems such as goethite, where proton transfers between adjacent octahedra had a dramatic effect on the calculated EXAFS. Vibrational effects as a function of temperature were also estimated using AIMD, allowing separate quantification of thermal and configurational disorder. In summary, coupling AIMD structural modeling and EXAFS experiments enables modeling of the redox behavior of polyvalent metals that are incorporated in conductive materials such as iron (oxyhydr)oxides, with applications over a broad swath of chemistry and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2022

PDFDataExtractor: A Tool for Reading Scientific Text and Interpreting Metadata from the Typeset Literature in the Portable Document Format

The layout of portable document format (PDF) files is constant to any screen, and the metadata therein are latent, compared to mark-up languages such as HTML and XML. No semantic tags are usually provided, and a PDF file is not designed to be edited or its data interpreted by software. However, data held in PDF files need to be extracted in order to comply with opensource data requirements that are now government-regulated. In the chemical domain, related chemical and property data also need to be found, and their correlations need to be exploited to enable data science in areas such as data-driven materials discovery. Such relationships may be realized using text-mining software such as the “chemistry-aware” natural-language-processing tool, ChemDataExtractor; however, this tool has limited data-extraction capabilities from PDF files. This study presents the PDFDataExtractor tool, which can act as a plug-in to ChemDataExtractor. It outperforms other PDF-extraction tools for the chemical literature by coupling its functionalities to the chemical-named entityrecognition capabilities of ChemDataExtractor. The intrinsic PDF-reading abilities of ChemDataExtractor are much improved. The system features a template-based architecture. This enables semantic information to be extracted from the PDF files of scientific articles in order to reconstruct the logical structure of articles. While other existing PDF-extracting tools focus on quantity mining, this template-based system is more focused on quality mining on different layouts. PDFDataExtractor outputs information in JSON and plain text, including the metadata of a PDF file, such as paper title, authors, affiliation, email, abstract, keywords, journal, year, document object identifier (DOI), reference, and issue number. With a self-created evaluation article set, PDFDataExtractor achieved promising precision for all key assessed metadata areas of the document text.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive ab initio calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy-comparable or better than existing state-of-the-art machine learning models-with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. In conclusion, the accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Machine Learning a Simple Interpretable Short-Range Potential for Silica

A wide array of models, spanning from computationally expensive ab initio methods to a spectrum of force-field approaches, have been developed and employed to probe silica polymorphs and understand growth processes and atomic-level dynamical transitions in silica. However, the quest for a model capable of making accurate predictions with high computational efficiency for various silica polymorphs is still ongoing. Recent developments in short-range machine-learned models, such as GAP and NNPScan, have shown promise in providing reasonable descriptions of silica, but their computational cost remains high compared to force fields such as BKS which are based on simple interpretable functional forms. Here, in this study, we build on the recent success of our reinforcement learning (RL) workflow to derive a new set of optimal parameters for a promising short-range BKS-based model proposed by Soules. We use RL to navigate the eight-dimensional parameter space of the Soules potential using an experimental training data set that includes both local and global structural features from approximately 21 experimentally realized silica polymorphs, including high density phases and porous zeolites. We compare the performance of our machine-learned ML-Soules model with other high quality models including our recent machine-learned parametrization of BKS (ML-BKS), a machine-learned potential (GAP), as well as predictions of ab initio calculations with the highly fidelity SCAN functional. The ML-Soules accurately captures the relative energetic ordering of various polymorphs as well as their structural features at a significantly reduced computational expense. The ML-Soules model also reasonably captures the structure, density, and elastic constants of quartz, as well as metastable silica polymorphs. We further discuss the limitations of the Soules functional form and propose potential enhancements, including the incorporation of additional three-body terms and/or the utilization of different short-ranged functional forms to achieve greater accuracy for both global and local features in the modeling of silica while retaining low computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Oxygen K-Edge X-ray Absorption Spectra of ThO 2 and CeO 2 : Experiment, Interpretation, and Structural Effects

Experimental oxygen K-edge spectra of ThO 2 and CeO 2 are presented and interpreted based on density functional theory (DFT). The contribution of d and f orbitals to the O K-edge spectrum is identified as well-distinguished peaks, the presence of which evidences the strong hybridization of Th and Ce metal centers with O orbitals. The sensitivity of the O K-edge to both f- and d-states in the absence of a core–hole on the metal ion results in an insightful overview of the electronic structure involved in the chemical bond. In particular, the large bandwidth of the Th 5f band as compared to the Ce 4f band is observed as a set of wider and more substantial set of peaks in the O K-edge, confirming the stronger hybridization of the former with O orbitals. The peak ascribed to the 5f band of ThO 2 is found at higher energy than the 6d band, as predicted from DFT calculations on actinide dioxides. Here, to highlight the sensitivity and the potential use of the O K-edge for the characterization of ThO 2 -based systems, the sensitivity of the spectrum to structural changes such as lattice expansion and size reduction are calculated and discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Catalytic Resonance Theory: Experimental and Kinetic Interpretation of Programmable Catalysis

The complexity of programmable catalysts that change in the physical or electronic state on the time scale of a catalytic turnover results in uncertainty in the kinetic outcome of common measurements with dynamic catalyst experiments. To provide an interpretation of utility to experimentalists, a general programmable catalytic reaction exhibiting high turnover efficiency was simulated to understand the kinetic behavior of catalysts with varying frequency, binding energy, oscillation amplitude, and minimum oscillation binding energy at different temperatures and reactant gas pressures. The time-averaged catalytic rate for varying temperature in the form of an Arrhenius plot exhibited three distinct kinetic regimes, with the intermediate temperatures or applied frequencies exhibiting zero slope, indicative of barrier-free kinetic control. Transitions in experimentally measurable kinetic regimes with both temperature and applied catalyst oscillation frequency were associated with transitions in the sensitivity of reaction rate constants and degrees of rate control. Furthermore, these distinct kinetic macroscopic features specific to programmable catalysts were identified for observation in experimental dynamic kinetic measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Robust Interpretation of Electrochemical Impedance Spectra Using Numerical Complex Analysis

Electrochemical Impedance Spectroscopy (EIS) is a noninvasive technique widely used for understanding charge transfer and charge transport processes in electrochemical systems and devices. Standard approaches for the interpretation of EIS data involve starting with a hypothetical circuit model for the physical processes in the device based on experience/intuition and then fitting the EIS data to this circuit model. This work explores a mathematical approach for extracting key characteristic features from EIS data by relying on fundamental principles of complex analysis. These characteristic features can suggest the presence of inductors and constant phase elements (nonideal capacitors) from impedance data and enable us to answer questions about the identifiability and nonuniqueness of equivalent circuit models. In certain scenarios such as models with only resistors and capacitors, we are able to enumerate all possible families of circuit models. Finally, we apply the mathematical framework presented here to real-world electrochemical systems and highlight results using impedance measurements from a lithium-ion battery coin cell.

charge transfer↗
DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Viscoplastic Interpretation of Localized Compaction Creep in Porous Rock

Recent laboratory evidence shows that compaction creep in porous rocks may develop through stages of acceleration, especially if the material is susceptible to strain localization. This paper provides a mechanical interpretation of compaction creep based on viscoplasticity and nonlinear dynamics. For this purpose, a constitutive operator describing the evolution of compaction creep is defined to evaluate the spontaneous accumulation of pore collapse within an active compaction band. This strategy enables the determination of eigenvalues associated with the stability of the response which is able to differentiate decelerating from accelerating strain. This mathematical formalism was linked to a constitutive law able to simulate compaction localization. Material point simulations were then used to identify the region of the stress space where unstable compaction creep is expected, showing that accelerating strains correspond to pulses of inelastic strain rate. Such pulses were also found in full-field numerical analyses of delayed compaction, revealing that they correspond to stages of inception and propagation of new bands across the volume of the simulated sample. These results illustrate the intimate relation between the spatial patterns of compaction and their temporal dynamics, showing that while homogeneous compaction develops with decaying rates of accumulation, localized compaction occurs through stages of accelerating deformation caused by the loss of strength taking place during the formation of a band. In addition, they provide a predictive modeling framework to simulate and explain the spatio-temporal dynamics of compaction in porous sedimentary formations.

58 GEOSCIENCES↗
DOE OSTI2021

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Abstract Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

54 ENVIRONMENTAL SCIENCES↗