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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

Abstract Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO $$_2$$ 2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO $$_2$$ 2 fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model’s accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400 000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

The circularity of current and future polymeric materials is a major focus of fundamental and applied research, as undesirable end-of-life outcomes and waste accumulation are global problems that impact our society. The recycling or repurposing of thermoplastics and thermosets is an attractive solution to these issues, yet both options are encumbered by poor property retention upon reuse, along with heterogeneities in common waste streams that limit property optimization. Dynamic covalent chemistry, when applied to polymeric materials, enables the targeted design of reversible bonds that can be tailored to specific reprocessing conditions to help address conventional recycling challenges. In this review, we highlight the key features of several dynamic covalent chemistries that can promote closed-loop recyclability and we discuss recent synthetic progress towards incorporating these chemistries into new polymers and existing commodity plastics. Next, we outline how dynamic covalent bonds and polymer network structure influence thermomechanical properties related to application and recyclability, with a focus on predictive physical models that describe network rearrangement. Finally, we examine the potential economic and environmental impacts of dynamic covalent polymeric materials in closed-loop processing using elements derived from techno-economic analysis and life-cycle assessment, including minimum selling prices and greenhouse gas emissions. Throughout each section, we discuss interdisciplinary obstacles that hinder the widespread adoption of dynamic polymers and present opportunities and new directions toward the realization of circularity in polymeric materials.

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

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗
NASA NTRS2002

Internet Data Delivery for Future Space Missions

Ongoing work at National Aeronautics and Space Administration Goddard Space Flight Center (NASA/GSFC), seeks to apply standard Internet applications and protocols to meet the technology challenge of future satellite missions. Internet protocols and technologies are under study as a future means to provide seamless dynamic communication among heterogeneous instruments, spacecraft, ground stations, constellations of spacecraft, and science investigators. The primary objective is to design and demonstrate in the laboratory the automated end-to-end transport of files in a simulated dynamic space environment using off-the-shelf, low-cost, commodity-level standard applications and protocols. The demonstrated functions and capabilities will become increasingly significant in the years to come as both earth and space science missions fly more sensors and as the need increases for more network-oriented mission operations. Another element of increasing significance will be the increased cost effectiveness of designing, building, integrating, and operating instruments and spacecraft that will come to the fore as more missions take up the approach of using commodity-level standard communications technologies. This paper describes how an IP (Internet Protocol)-based communication architecture can support all existing operations concepts and how it will enable some new and complex communication and science concepts. The authors identify specific end-to-end data flows from the instruments to the control centers and scientists, and then describe how each data flow can be supported using standard Internet protocols and applications. The scenarios include normal data downlink and command uplink as well as recovery scenarios for both onboard and ground failures. The scenarios are based on an Earth orbiting spacecraft with downlink data rates from 300 Kbps to 4 Mbps. Included examples are based on designs currently being investigated for potential use by the Global Precipitation Measurement (GPM) mission.

Rash, James↗
DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗
DOE OSTI2022

Spatial scales of competition and a growth–motility trade-off interact to determine bacterial coexistence

The coexistence of competing species is a long-lasting puzzle in evolutionary ecology research. Despite abundant experimental evidence showing that the opportunity for coexistence decreases as niche overlap increases between species, bacterial species and strains competing for the same resources are commonly found across diverse spatially heterogeneous habitats. We thus hypothesized that the spatial scale of competition may play a key role in determining bacterial coexistence, and interact with other mechanisms that promote coexistence, including a growth–motility trade-off. To test this hypothesis, we let two Pseudomonas putida strains compete at local and regional scales by inoculating them either in a mixed droplet or in separate droplets in the same Petri dish, respectively. We also created conditions that allow the bacterial strains to disperse across abiotic or fungal hyphae networks. We found that competition at the local scale led to competitive exclusion while regional competition promoted coexistence. When competing in the presence of dispersal networks, the growth–motility trade-off promoted coexistence only when the strains were inoculated in separate droplets. Our results provide a mechanism by which existing laboratory data suggesting competitive exclusion at a local scale is reconciled with the widespread coexistence of competing bacterial strains in complex natural environments with dispersal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis

We show that "single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO 2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.

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

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) Projects in Germany and the Netherlands—Review and Lessons Learned

Aquifer thermal energy storage (ATES) is a concept that can help to address heating and cooling needs through the use of the subsurface as a seasonal thermal energy storage (STES) system. Over 2800 ATES systems have been deployed with storage temperatures typically below 25 °C and only a few with higher temperatures (>40 °C), which would increase the energy density and utility of the stored thermal fluids. Until now, only a few high-temperature aquifer thermal energy storage (HT-ATES) projects have been initiated and are still in operation. These HT-ATES projects have encountered a range of technical and non-technical challenges. This study reviews ten such projects: four in Germany and six in the Netherlands. The non-technical issues include public acceptance, a lack of regulatory framework for these systems, managing overlapping uses of the subsurface, managing changes with the providers and off-takers of thermal energy, and obtaining financing to implement these projects. Common technical issues include geological factors such as incomplete characterization of the subsurface and reservoir heterogeneity; geochemical issues such as mineral scaling, corrosion, and biofouling; lower than expected thermal recovery; and issues with system design and reliability. This review highlights benefits and challenges faced by HT-ATES projects with the goal to use the lessons learned to improve the siting, design, development, and operation of such systems. Recommendations include improved initial subsurface site characterization, use of coupled process models to optimize system design and predict system performance, cascaded uses of stored thermal energy to better utilize the stored heat, monitoring networks to provide feedback on system performance, and expanded system scale to allow for continued operation even when maintenance of some system components is required. Techno-economic modeling and risk analysis could be used to optimize such HT-ATES project design and identify key factors that will affect sustained economic viability. In addition, design flexibility is important for these systems to allow for changing conditions regarding the supply and demand of thermal energy. Adopting these findings should improve the performance and reduce the risks for future HT-ATES projects worldwide.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2023

Urbanization and edge effects interact to drive mutualism breakdown and the rise of unstable pathogenic communities in forest soil

Temperate forests are threatened by urbanization and fragmentation, with over 20% (118,300 km 2 ) of U.S. forest land projected to be subsumed by urban land development. We leveraged a unique, well-characterized urban-to-rural and forest edge-to-interior gradient to identify the combined impact of these two land use changes—urbanization and forest edge creation—on the soil microbial community in native remnant forests. We found evidence of mutualism breakdown between trees and their fungal root mutualists [ectomycorrhizal (ECM) fungi] with urbanization, where ECM fungi colonized fewer tree roots and had less connectivity in soil microbiome networks in urban forests compared to rural forests. However, urbanization did not reduce the relative abundance of ECM fungi in forest soils; instead, forest edges alone led to strong reductions in ECM fungal abundance. At forest edges, ECM fungi were replaced by plant and animal pathogens, as well as copiotrophic, xenobiotic-degrading, and nitrogen-cycling bacteria, including nitrifiers and denitrifiers. Urbanization and forest edges interacted to generate new “suites” of microbes, with urban interior forests harboring highly homogenized microbiomes, while edge forest microbiomes were more heterogeneous and less stable, showing increased vulnerability to low soil moisture. When scaled to the regional level, we found that forest soils are projected to harbor high abundances of fungal pathogens and denitrifying bacteria, even in rural areas, due to the widespread existence of forest edges. Our results highlight the potential for soil microbiome dysfunction—including increased greenhouse gas production—in temperate forest regions that are subsumed by urban expansion, both now and in the future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

An analysis of nearfield normal mode amplitude anomalies of the Landers earthquake

The 1992 Landers earthquake (M(sub w) = 7.3) occurred in the middle of the TERRAscope network. Long-period Rayleigh waves recorded at the TERRAscope stations (delta less than or = 3 deg) after traveling around the Earth show large amplitude anomalies, one order of magnitude larger than spherical Earth predictions up to a period of about 600 s. The ground motions over the epicentral region at and after the arrival of R4-5 are in phase at all stations. These observations are inconsistent with the nearly vertical strike slip mechanism of the Landers earthquake. Synthetic seismograms for a rotating, elliptic, and laterally heterogeneous Earth model calculated by the variational method agree well with the observed waveforms. Calculations for various 3D Earth models demonstrate that the amplitudes are very sensitive to the large scale aspherical structure in the crust and the mantle. The anomalies for modes shorter than 300 s period can be explained by lateral heterogeneity shallower than the upper mantle. Rotation of the Earth and lower mantle heterogeneity are required to explain mode amplitudes at longer periods. Current whole mantle seismic tomographic models can fully explain the observed amplitudes longer than 300 s. To assess the effect of the high order lateral heterogeneity in the mantle more precise estimate of the crustal correction is required.

Watada, Shingo↗
NASA NTRS2015

Towards an Open, Distributed Software Architecture for UxS Operations

To address the growing need to evaluate, test, and certify an ever expanding ecosystem of UxS platforms in preparation of cultural integration, NASA Langley Research Center's Autonomy Incubator (AI) has taken on the challenge of developing a software framework in which UxS platforms developed by third parties can be integrated into a single system which provides evaluation and testing, mission planning and operation, and out-of-the-box autonomy and data fusion capabilities. This software framework, named AEON (Autonomous Entity Operations Network), has two main goals. The first goal is the development of a cross-platform, extensible, onboard software system that provides autonomy at the mission execution and course-planning level, a highly configurable data fusion framework sensitive to the platform's available sensor hardware, and plug-and-play compatibility with a wide array of computer systems, sensors, software, and controls hardware. The second goal is the development of a ground control system that acts as a test-bed for integration of the proposed heterogeneous fleet, and allows for complex mission planning, tracking, and debugging capabilities. The ground control system should also be highly extensible and allow plug-and-play interoperability with third party software systems. In order to achieve these goals, this paper proposes an open, distributed software architecture which utilizes at its core the Data Distribution Service (DDS) standards, established by the Object Management Group (OMG), for inter-process communication and data flow. The design decisions proposed herein leverage the advantages of existing robotics software architectures and the DDS standards to develop software that is scalable, high-performance, fault tolerant, modular, and readily interoperable with external platforms and software.

Cross, Charles D.↗
NASA NTRSDate not supplied

Tin-Essako 001: A Metal-Rich Ureilite?

Introduction: Metal-rich achondrites include a variety of types, and likely have a variety of origins. Models range from gravitational mixing at the core-mantle boundaries of differentatiated asteroids, to complex impact mixing scenarios. We describe a new type of metal-rich achondrite that might be the first metal-rich ureilite. Sample: Tin-Essako (TE) 001 (~4.3 g) was found in Mali in 2020 and purchased by Jay Piatek in 2021. It was classified as a metal-rich ungrouped achondrite, with olivine and oxygen isotope (d18O=8.2810‰, d17O=3.718‰, avg. 3) compositions suggesting affinity to ureilites [1]. We studied one polished section (~187.7 mm2) of TE 001. Petrography: TE 001 consists of ~60% metal and 40% silicates, heterogeneously distributed. The metal is largely fresh, but iron oxides (presumably terrestrial) occur along one edge and in some patches and veins in the interior. The silicates are dominantly olivine (≥90%), with melt-textured areas of plagioclase + Si-rich glass. Minor phases include chromite and carbon. Olivine occurs as rounded grains (up to ~2.5 mm) in metal, commonly with rims of melt-textured plagioclase + glass. Olivine also occurs in more massive areas having a “honeycomb” texture, with rounded olivine “cells” surrounded by an interstitial network of reduced olivine riddled with tiny metal grains, plus melt-textured plagioclase + Si-rich glass. Chromite occurs as subhedral to rounded grains; smaller grains (30-250 mm) are included in olivine and a few larger grains (400-500 mm) are isolated within metal. A carbon phase occurs as lacy-textured rims around olivine grains in metal, or small patches within metal. Mineral Compositions: The olivine (excluding interstitial areas) is Fo 73.9±0.5, with 0.25±0.02 wt.% CaO, 0.31±0.01 wt% Cr2O3, 0.01 wt.% NiO, and molar Fe/Mn=49.8±2.2 (53 analyses). Olivine in interstitial areas has Fo up to at least 91. Smaller chromite grains have Fe# (molar Fe/[Fe+Mg]) = 0.54±0.01, Cr# (molar Cr/[Cr+Al]) = 0.52±0.01, 0.52±0.02 wt.% V2O3 and 0.21±0.04 wt% ZnO (28 analyses). One larger grain is zoned from Fe# = 0.45, Cr# =0.52 to Fe# = 0.40, Cr# =0.57, and contains thin Al-rich lamellae not resolved by EMPA. One irregularly shaped patch of chromite included in olivine has Fe# =0.26 and contains no ZnO. Plagioclase laths are An ~53-60, with ≤0.01 wt.% K2O. Glass contains 75-76 wt% SiO2 and ~16 wt% Al2O3. The metal contains 5.2±0.2 wt% Ni, 0.46±0.02 wt.% Co, and 0.01±0.01 wt.% Cr, with Si and P below detection (168 analyses). Discussion: The olivine + chromite assemblage in TE 001 is similar to the most ferroan ureilites (Fo ~75-79 [2]), as are the oxygen isotopes [1]. The presence of a carbon phase supports this, although the identity of this phase (graphite as in ureilites?) remains to be determined. The “honeycomb” textured areas, in particular, the presence of olivine “reduction rims,” resemble shock-smelted olivine areas in ureilites [3], but interstitial melt-textured plagioclase laths + glass like those in TE 001 have not been reported in such (or any) ureilites. Olivine in TE 001 is marginally more ferroan than in the most FeO-rich ureilite, with Fe-Mg-Mn composition offset from the trend of olivine + low-Ca pyroxene ureilites similar to augite-bearing ureilites [4]. CaO and Cr2O3 contents are in the range of those in ureilite olivine [ ] , though Cr2O3 is at the extreme low end of the range [5]. Chromites (except the unusual one) have similar Fe# to the most ferroan primary chromites in ureilites [2], but distinctly lower Cr# (0.52 vs. 0.71). Metal compositions are with the range for metal in ureilites [7]. The absence of pyroxene and sulfide, and the high abundance of metal in TE 001, are unlike ureilites. One possibility is that a ferroan, chromite-bearing, pyroxene-poor ureilite was invaded (possibly due to impact) by a metallic liquid (low S content suggests very high temperature), resulting in complete melting of pyroxene and smelting of olivine, with rapid recrystallization of melted silicate as plagioclase + glass. Alternatively, a pre-existing metal-rich ureilite assemblage may have been impact melted. Additional types of data will be obtained to evaluate these hypotheses (i.e., is the metal indigenous?) and assess affinity to ureilites.

olivine↗
DOE OSTI2025

Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning

Predicting lithium-ion battery performance is hindered by microscale electrode heterogeneities invisible to conventional diagnostics. Here, we combine electrochemical fluorescence microscopy (EFM), which maps electronic connectivity by visualizing an electrofluorophore reaction distribution, with a multitask ElasticNet regression to forecast discharge capacity from spatial heterogeneity. Analyzing 196 images from six pilot-scale LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathodes with varying carbon loadings, we extract 62 descriptors that capture morphology and texture. A compact five-feature model predicts capacity across eight discharge rates, achieving a per-target R 2 of up to 0.63 and an overall R 2 of 0.92, with a mean absolute percentage error of less than 2%. This performance rivals impedance-based approaches while avoiding their reliance on postformation data and incomplete electronic network information. Our facile and rapid, image-driven method may enable electrode quality control upstream of costly cell assembly to offer a transformative tool for data-driven battery research and manufacturing.

battery electrodes↗
NASA NTRS2001

A Latency-Tolerant Partitioner for Distributed Computing on the Information Power Grid

NASA's Information Power Grid (IPG) is an infrastructure designed to harness the power of graphically distributed computers, databases, and human expertise, in order to solve large-scale realistic computational problems. This type of a meta-computing environment is necessary to present a unified virtual machine to application developers that hides the intricacies of a highly heterogeneous environment and yet maintains adequate security. In this paper, we present a novel partitioning scheme. called MinEX, that dynamically balances processor workloads while minimizing data movement and runtime communication, for applications that are executed in a parallel distributed fashion on the IPG. We also analyze the conditions that are required for the IPG to be an effective tool for such distributed computations. Our results show that MinEX is a viable load balancer provided the nodes of the IPG are connected by a high-speed asynchronous interconnection network.

Das, Sajal K.↗
DOE OSTI2020

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Single-cell and spatial omics in plants: from cellular atlases to regulatory mechanisms

Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomic studies by enabling gene expression profiling at the resolution of individual cells within and across a broad range of tissue types, revealing cellular heterogeneity that is obscured in bulk tissue transcriptomes. Over the past decade, improvements in microfluidics and library preparation have drastically increased throughput, allowing tens of thousands of cells to be assayed in a single experiment. Although initially developed in animal systems, scRNA-seq has rapidly emerged as a powerful and widely adopted approach in plant biology. Beyond transcriptomics, the integration of single-cell data with chromatin accessibility, proteomics, metabolomics, and spatial omics is enabling a system-level understanding of plant gene regulation and cellular organization. Network-based analytical frameworks further support the reconstruction of gene regulatory networks and the interpretation of complex single-cell data. In this review, we summarize the current technological landscape of plant single-cell studies, discuss key experimental and analytical challenges, and review emerging strategies for validating single-cell discoveries. We also discuss future directions in applying single-cell technologies to woody perennials plants and bioenergy-relevant crops, emphasizing their potential to accelerate the discovery of cell type-specific regulatory mechanisms underlying growth, stress resilience, and biomass production.

Li, Miaomiao [ORNL] (ORCID:0000000321326168)↗
DOE OSTI2024

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗