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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probing Condensed-Phase Structure and Dynamics in Hierarchical Zeolites and Nanosheets for Catalytic Upgradation of Biomass (Final Report)

Understanding complex reaction pathways in systems governed by multi-scale collective interactions across time and length scales remains a central scientific challenge. This project was guided by the hypothesis that the interplay among oligomers, solvents, and active sites can be tuned by a suitable choice of solvation environment and pore architecture in solid-acid catalysts to direct chemical transformations relevant to biomass conversion. Zeolites and zeolite nanosheets were used as model platforms, allowing for the interaction of macromolecules with the surface of the zeolite nanosheets and with smaller pores that host catalytically active sites. To investigate these coupled phenomena, we employ a multi-scale computational framework that integrates molecular-level descriptions with advanced sampling approaches to capture key physical and chemical interactions. Our work through this project improved fundamental understanding of how reactants and solid-acid catalysts interact in solvent-rich environments, thereby enabling the rational design of catalytic systems that upgrade biomass with enhanced selectivity and energy efficiency. In addition, the project developed advanced sampling methodologies critical for disentangling complex, reactive processes in multi-component catalytic environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Redshift inference from the combination of galaxy colours and clustering in a hierarchical Bayesian model – Application to realistic N -body simulations

ABSTRACT Photometric galaxy surveys constitute a powerful cosmological probe but rely on the accurate characterization of their redshift distributions using only broad-band imaging, and can be very sensitive to incomplete or biased priors used for redshift calibration. A hierarchical Bayesian model has recently been developed to estimate those from the robust combination of prior information, photometry of single galaxies, and the information contained in the galaxy clustering against a well-characterized tracer population. In this work, we extend the method so that it can be applied to real data, developing some necessary new extensions to it, especially in the treatment of galaxy clustering information, and we test it on realistic simulations. After marginalizing over the mapping between the clustering estimator and the actual density distribution of the sample galaxies, and using prior information from a small patch of the survey, we find the incorporation of clustering information with photo-z’s tightens the redshift posteriors and overcomes biases in the prior that mimic those happening in spectroscopic samples. The method presented here uses all the information at hand to reduce prior biases and incompleteness. Even in cases where we artificially bias the spectroscopic sample to induce a shift in mean redshift of $\Delta \bar{z} \approx 0.05,$ the final biases in the posterior are $\Delta \bar{z} \lesssim 0.003.$ This robustness to flaws in the redshift prior or training samples would constitute a milestone for the control of redshift systematic uncertainties in future weak lensing analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Solution-Based, Anion-Doping of Li 4 Ti 5 O 12 Nanoflowers for Lithium-Ion Battery Applications

Solution–based, anionic doping represents a convenient strategy with which to improve upon the conductivity of candidate anode materials such as Li 4 Ti 5 O 12 (LTO). As such, novel synthetic hydrothermally–inspired protocols have primarily been devised herein, aimed at the large–scale production of unique halogen–doped, micron–scale, three–dimensional, hierarchical LTO flower–like motifs. Although fluorine (F) doping has been explored, the use of chlorine (Cl) dopants is the primary focus here. Several experimental variables, such as dopant amount, lithium hydroxide concentration, and titanium butoxide purity, were probed and perfected. Furthermore, the Cl doping process did not damage the intrinsic LTO morphology. The analysis, based on interpreting a compilation of SEM, XRD, XPS, and TEM–EDS results, was used to determine an optimized dopant concentration of Cl. Electrochemical tests demonstrated an increased capacity via cycling of 12 % for a Cl–doped sample as compared with pristine LTO. Moreover, the Cl–doped LTO sample described in this study exhibited the highest discharge capacity yet reported at an observed rate of 2C for this material at 143mAh g –1 . Overall, these data suggest that the Cl dopant likely enhances not only the ion transport capabilities, but also the overall electrical conductivity of our as–prepared structures. Furthermore, to help explain these favorable findings, theoretical DFT calculations were used to postulate that the electronic conductivity and Li diffusion were likely improved by the presence of increased Ti 3+ ion concentration coupled with widening of the Li migration channel.

25 ENERGY STORAGE↗
DOE OSTI2022

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiscale Mechanical Characterization of Mineral-Reinforced Wood Cell Walls

Studying the multiscale mechanics of bio-based composites offers unique perspectives on underlying structure–property relations. Cellular materials, such as wood, are highly organized, hierarchical assemblies of load-bearing structural elements that respond to mechanical stimuli at the microscopic, mesoscopic and macroscopic scale. In this study, we modified oak wood with nanocrystalline ferrihydrite, a widespread ferric oxyhydroxide mineral, and characterized the resulting mechanical properties of the composite at various levels of organization. Ferrihydrite nanoparticles were deposited inside the wood cell wall by an in situ chemical reaction, resulting in increased stiffness and hardness of the functionalized secondary cell wall, as evidenced by region-specific nanoindentation tests under an electron microscope. Chemically modified and pristine wood samples were characterized by using atomic force microscopy in the bimodal frequency modulation mode, which produced topographical images from the cellular ultrastructure with high lateral resolution and localized nanomechanical information across distinct cell wall layers. In conclusion, despite mineral reinforcement at the cell wall level, the macroscopic fracture behavior examined through three-point flexural testing remained unchanged upon modification, as cell–cell adhesion could be impaired by harsh chemical conditions.

Cells↗
DOE OSTI2020

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 $Fm\bar3m$ face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group $Fm\bar3m$ by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Passive Dust Mitigation Technologies Being Developed for Demonstration Under Patch Plate Materials Compatibility Analysis Task

With the Artemis program, we are planning longer stays on the surface, with more activities that have the potential to put the astronauts and equipment in contact with greater quantities of lunar dust. The success of these missions will depend on our understanding of material interactions with lunar dust and the development of ways to mitigate dust effects in cases where exposure to dust will lead to failure of components, unacceptable loss of power or thermal control, unacceptable loss of visibility, or health issues. Passive dust mitigation by coating or surface alteration is one method that is being developed and demonstrated under the Space Technology Mission Directorate’s Game Changing Technology, Dust Mitigation Program as part of the Patch Plate Materials Compatibility Assessment Task. The goal of the task is to alter the surfaces of materials in order to passively reduce the adhesion of dust, demonstrate their performance in relevant ground-based tests using lunar simulants, and prepare them for demonstration through experiment on the lunar surface. Optically transparent, sputter deposited, work function matching coatings are being developed to reduce adhesion of dust to windows, lenses and display panels by matching the minimum energy to remove an electron from the surface to that of lunar dust in order to reduce adhesion due to charge transfer. Low surface energy coatings and surfaces for thermal control are also being developed to reduce the bonding of dust with the surface enabling it to be removed more easily. Conductive coatings with the ability to shed dust more easily are being developed for use with the active Electrodynamic Dust Shield technology to help reduce the power needed to remove dust from the surface. Passive dust mitigation surfaces for metals such as aluminum, stainless steel, and titanium are being developed that reduce the area of dust contact with the surfaces through topographical modification using laser ablation patterning to impart hierarchical topographies with nanometer to micrometer length scales in a single step. Topographically modified polymeric materials, both those with extensive space heritage and those with lower technology readiness levels, are also being evaluated. Space suit fabric surfaces that can reduce dust penetration into and through the fabric are also being investigated as well as pristine and topographically modified ceramic materials that exhibit high wear resilience. An overview of the passive dust mitigation surfaces and coatings being developed under this task, ground testing being conducted using lunar simulants, characterization techniques, and materials preparation for flight sample delivery for integration into the Alpha Space Regolith Adherence Characterization experiment going to the lunar surface on a Commercial Lunar Payload Services (CLPS) lander in 2023 will be discussed.

Lunar dust, passive mitigation, lunar simulant, co↗
NASA NTRS1988

Hierarchical neural networks for autonomous data analysis and decision making

A neural network based data analysis and decision making system to increase the autonomy of a planetary rover or similar exploratory vehicle is presented. A hierarchical series of neural networks for real time analysis of scientific images is used. The system under development emphasizes analysis of multispectral images by classifier and feature detector neural networks, to provide information on the mineral composition of a scene. A hierarchy of alternating analysis and decision making networks is being developed to allow increasingly fine scale analysis in regions of the image that are potentially important. It is noted that this system will facilitate both the selection of high priorty scientific information for transmission to earth, and the autonomous collection of rocks and soil for sample return.

Eberlein, Susan↗
DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗
NASA NTRS1993

Hierarchical nonlinear behavior of hot composite structures

Hierarchical computational procedures are described to simulate the multiple scale thermal/mechanical behavior of high temperature metal matrix composites (HT-MMC) in the following three broad areas: (1) behavior of HT-MMC's from micromechanics to laminate via METCAN (Metal Matrix Composite Analyzer), (2) tailoring of HT-MMC behavior for optimum specific performance via MMLT (Metal Matrix Laminate Tailoring), and (3) HT-MMC structural response for hot structural components via HITCAN (High Temperature Composite Analyzer). Representative results from each area are presented to illustrate the effectiveness of computational simulation procedures and accompanying computer codes. The sample case results show that METCAN can be used to simulate material behavior such as the entire creep span; MMLT can be used to concurrently tailor the fabrication process and the interphase layer for optimum performance such as minimum residual stresses; and HITCAN can be used to predict the structural behavior such as the deformed shape due to component fabrication. These codes constitute virtual portable desk-top test laboratories for characterizing HT-MMC laminates, tailoring the fabrication process, and qualifying structural components made from them.

Murthy, P. L. N.↗
DOE OSTI2023

Elemental zoning enhances mass transport in zeolite catalysts for methanol to hydrocarbons

Mass transport limitations in zeolite catalysts pose major hurdles for their optimal performance in diverse chemical reactions. Most approaches to reduce these restrictions focus on the synthesis of either hierarchical or nanosized zeolites. Here we demonstrate that the existence of a siliceous, catalytically inactive exterior rim on ZSM-5 particles dramatically reduces the diffusion limitations, which leads to an enhanced catalyst lifetime for the methanol-to-hydrocarbon reaction. In this study, our findings reveal that binary inorganic and organic structure-directing agents enable a one-pot synthesis of Si-zoned ZSM-5 catalysts with diffusion properties that are characteristic of particles with a much smaller size. Operando ultraviolet–visible light diffuse reflectance spectroscopy reveals a marked reduction in external coking among Si-zoned samples. Molecular dynamics simulations to assess the diffusion of methanol and benzene in siliceous pores and in those with Brønsted acids reveal substantially reduced transport limitations in zoned regions, consistent with the improved catalyst activity of Si-zoned zeolites relative to that of ZSM-5 with a homogeneous acid-site distribution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

Academy Sharing Knowledge (ASK). The NASA Source for Project Management Magazine

How big is your project world? Is it big enough to contain other cultures, headquarters, hierarchies, and weird harpoon-like guns? Sure it is. The great American poet Walt Whitman said it best, 'I am large/I contain multitudes.' And so must you, Mr. and Ms. Project Manager. In this issue of ASK, we look outside the project box. See how several talented project managers have expanded their definition of project scope to include managing environments outside the systems and subsystems under their care. Here's a sampling of what we've put together for you this issue: In 'Three Screws Missing,' Mike Skidmore tells about his adventures at the Plesetek Cosmodrome in northern Russia. Ray Morgan in his story, 'Our Man in Kauai,' suggests we take a broader view of what's meant by 'the team.' Jenny Baer-Riedhart, the NASA program manager on the same Pathfinder solar-powered airplane, schools us in how to sell a program to Headquarters in 'Know Thyself--But Don't Forget to Learn About the Customer Too.' Scott Cameron of Proctor and Gamble talks about sharpening your hierarchical IQ in 'The Project Manager and the Hour Glass.' Mike Jansen in 'The Lawn Dart' describes how he and the 'voodoo crew' on the Space Shuttle Advanced Solid Rocket Motor program borrowed a harpoon-like gun from the Coast Guard to catch particles inside of a plume. These are just some of the stories you'll find in ASK this issue. We hope they cause you to stop and reflect on your own project's relationship to the world outside. We are also launching a new section this issue, 'There are No Mistakes, Only Lessons.' No stranger to ASK readers, Terry Little inaugurates this new section with his article 'The Don Quixote Complex.'

Post, Todd↗
DOE OSTI2022

Functional Data Analysis for Extracting the Intrinsic Dimensionality of Spectra: Application to Chemical Homogeneity in the Open Cluster M67

High-resolution spectroscopic surveys of the Milky Way have entered the Big Data regime and have opened avenues for solving outstanding questions in Galactic archeology. However, exploiting their full potential is limited by complex systematics, whose characterization has not received much attention in modern spectroscopic analyses. In this work, we present a novel method to disentangle the component of spectral data space intrinsic to the stars from that due to systematics. Using functional principal component analysis on a sample of 18,933 giant spectra from APOGEE, we find that the intrinsic structure above the level of observational uncertainties requires ≈10 functional principal components (FPCs). Our FPCs can reduce the dimensionality of spectra, remove systematics, and impute masked wavelengths, thereby enabling accurate studies of stellar populations. To demonstrate the applicability of our FPCs, we use them to infer stellar parameters and abundances of 28 giants in the open cluster M67. We employ Sequential Neural Likelihood, a simulation-based Bayesian inference method that learns likelihood functions using neural density estimators, to incorporate non-Gaussian effects in spectral likelihoods. By hierarchically combining the inferred abundances, we limit the spread of the following elements in M67: Fe ≲ 0.02 dex; C ≲ 0.03 dex; O, Mg, Si, Ni ≲ 0.04 dex; Ca ≲ 0.05 dex; N, Al ≲ 0.07 dex (at 68% confidence). Our constraints suggest a lack of self-pollution by core-collapse supernovae in M67, which has promising implications for the future of chemical tagging to understand the star formation history and dynamical evolution of the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

Variation of Florida scrub vegetation along gradients of soil pH and landscape age on a barrier island complex

Florida scrub is a fire-maintained shrub vegetation of well-drained, sandy soils associated with ridge systems that originated as coastal dunes. It is unique to Florida and supports many rare plants and animals. Between 1992 and 2005, we sampled 30 stands of long-unburned scrub with 196 line-intercept transects (15 m length) across the Merritt Island-Cape Canaveral barrier island complex where dune ridges range from relatively recent to > 30,000 years old with a range of soil leaching and reaction. These data allow us to determine the relationships of landscape age and soil reaction on community composition. We recorded community composition in < 0.5 m and > 0.5 m height strata. We determined mapped soil type for all transects; for 151 transects we determined soil pH of the 0–15 cm and 15–30 cm layers. Hierarchical cluster analysis of stands (N=30) and transects (N=196) using 41 species (of 53) present in > 2 transects gave two groups: coastal scrub with Quercus virginiana (shrub form) and Serenoa repens as dominant species on the most alkaline soils, and oak-saw palmetto scrub with Quercus chapmanii, Quercus geminata, Quercus myrtifolia, and S. repens on the strongly to somewhat acidic soils. Direct gradient analysis indicated that dominant species except S. repens varied from acidic to alkaline soils. Indicator species analysis identified seven species that indicated acidic soils and five that indicated alkaline soils (P < 0.01). Nonmetric multidimensional scaling (NMS) ordination at the stand level separated the two groups along the first axis, and NMS ordination of the transect data showed the gradient of coastal to oak-saw palmetto scrub. Position of transects on the first axis was related to soil pH class, and to measured pH of the 0–15 cm and 15–30 cm layers. Soils show a progressive leaching of shell material from the surface horizons followed by podsolization; this process takes > 4,000 years. Our results indicate substantial differences between the community composition of scrub vegetation on recent alkaline soils compared to leached acidic soils.

Barrier island↗
DOE OSTI2022

The Hubble Constant from Strongly Lensed Supernovae with Standardizable Magnifications

The dominant uncertainty in the current measurement of the Hubble constant (H 0 ) with strong gravitational lensing time delays is attributed to uncertainties in the mass profiles of the main deflector galaxies. Strongly lensed supernovae (glSNe) can provide, in addition to measurable time delays, lensing magnification constraints when knowledge about the unlensed apparent brightness of the explosion is imposed. We present a hierarchical Bayesian framework to combine a data set of SNe that are not strongly lensed and a data set of strongly lensed SNe with measured time delays. We jointly constrain (i) H 0 using the time delays as an absolute distance indicator, (ii) the lens model profiles using the magnification ratio of lensed and unlensed fluxes on the population level, and (iii) the unlensed apparent magnitude distribution of the SN population and the redshift–luminosity relation of the relative expansion history of the universe. We apply our joint inference framework on a future expected data set of glSNe and forecast that a sample of 144 glSNe of Type Ia with well-measured time series and imaging data will measure H 0 to 1.5%. We discuss strategies to mitigate systematics associated with using absolute flux measurements of glSNe to constrain the mass density profiles. Using the magnification of SN images is a promising and complementary alternative to using stellar kinematics. Future surveys, such as the Rubin and Roman observatories, will be able to discover the necessary number of glSNe, and with additional follow-up observations, this methodology will provide precise constraints on mass profiles and H 0 .

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Central Atlantic regional ecological test site: A prototype regional environmental information system

The author has identified the following significant results. A comparison of photomorphic regions from an uncontrolled ERTS-1 mosaic of CARETS to land use areas on a map published in the National Atlas revealed close correlations in non-urban regions. Such regional scale analysis of ERTS-1 data has the potential for providing an economical sampling strategy for selecting sites for more detailed field measurements if other environmental variables can be correlated with patterns on ERTS-1 imagery. ERTS-1 imagery has also revealed for the first time the appearance of CARETS during the winter months. Investigators have identified extensive areas of conifers, which have previously been indistinguishable from deciduous vegetation. Imagery has also shown very clearly the extent of snow cover at a particular time over the region. The evaluation of ERTS-1 imagery used for the land use mapping of the shore zone of CARETS, has shown that the presence or absence of elements of an hierarchal system of shoreline landforms can help identify areas of potential rapid change. Changes in land use class distributions on the Barrier Islands signify the environmental response to natural and man-caused processes. Both environmental vulnerability and sensitivity can be estimated from the repetitive ERTS-1 coverage of long reaches of the CARETS coast. Results indicate potential applications to land use planning, management, and regional environmental quality analysis.

Alexander, R. H.↗
DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗