An algorithm for the synthesis of binary sequence detectors
Algorithm for synthesis of binary sequence detectors
ALGORITHM↗
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Results for “Data synthesis”
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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An autonomous laboratory for the accelerated synthesis of novel materials
To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.
36 MATERIALS SCIENCE↗
International Arctic Systems for Observing the Atmosphere (IASOA) Metadata Harvest for Oliktok Point Observations Field Campaign Report
The International Arctic Systems for Observing the Atmosphere (IASOA) was initiated as an International Polar Year (IPY) project (Uttal et al. 2015, on-line release) to address key atmospheric science questions through coordinating the considerable atmospheric observing assets at 10 pan-arctic observatories (http://iasoa.org). The mission of IASOA is to advance cross-site research objectives from independent arctic atmospheric observatories through: (1) strategically developing comprehensive observational capacity; (2) facilitating data access and usability through a single gateway; and (3) mobilizing contributions to synergistic science and socially relevant services derived from IASOA assets and expertise. In April 2015 the IASOA science steering committee invited the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s Oliktok Point observatory to contribute to consortium activities. The IASOA steering committee identified enhanced, cross-site data sharing through an IASOA-specific data access portal as a focus for ongoing consortium activities. The design intent of the portal has been to display the data inventories of all IASOA observatories in a succinct summary (Data-at-a-Glance) and provide the most proximal access links available to those data sets. The IASOA data access portal is not intended to replace the functions of existing archives. This succinct summary of data availability supports cross-site and interdisciplinary synthesis science activities. Efforts to enhance the legacy IASOA data access portal functionality have focused on two areas: developing a comprehensive and coherent data vocabulary for IASOA data and implementing an interoperable metadata standard for all IASOA data. The technical goal of this proposal is to work with ARM data managers to operationalize the export of metadata for observational data sets for Oliktok Point from the ARM metadata database. IASOA harvests data in the commonly used International Organization for Standardization (ISO) format. ARM has successfully generated metatdata matching this format in the past to provide metadata from its North Slope of Alaska (NSA) Barrow observatory.
54 ENVIRONMENTAL SCIENCES↗
The AutoBayes Program Synthesis System: System Description
AUTOBAYES is a fully automatic program synthesis system for the statistical data analysis domain. Its input is a concise description of a data analysis problem in the form of a statistical model; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. AUTOBAYES synthesizes code by a schema-guided deductive process. Schemas (i.e., code templates with associated semantic constraints) are applied to the original problem and recursively to emerging subproblems. AUTOBAYES complements this approach by symbolic computation to derive closed-form solutions whenever possible. In this paper, we concentrate on the interaction between the symbolic computations and the deductive synthesis process. A statistical model specifies for each problem variable (i.e., data or parameter) its properties and dependencies in the form of a probability distribution, A typical data analysis task is to estimate the best possible parameter values from the given observations or measurements. The following example models normal-distributed data but takes prior information (e.g., from previous experiments) on the data's mean value and variance into account.
Fischer, Bernd↗
Data for Purple Acid Phosphatase2 Stimulates a Futile Cycle of Lipid Synthesis and Degradation, and Mitigates the Negative Growth Effects of Triacylglycerol Accumulation in Vegetative Tissues
Storage lipids (mostly triacylglycerols, TAGs) serve as an important energy and carbon reserve in plants, and hyperaccumulation of TAG in vegetative tissues can have negative effects on plant growth. Purple acid phosphatase2 (PAP2) was previously shown to affect carbon metabolism and boost plant growth. However, the effects of PAP2 on lipid metabolism remain unknown. Here, we demonstrated that PAP2 can stimulate a futile cycle of fatty acid (FA) synthesis and degradation, and mitigate negative growth effects associated with high accumulation of TAG in vegetative tissues. Constitutive expression of PAP2 in Arabidopsis thaliana enhanced both lipid synthesis and degradation in leaves and led to a substantial increase in seed oil yield. Suppressing lipid degradation in a PAP2-overexpressing line by disrupting sugar-dependent1 (SDP1), a predominant TAG lipase, significantly elevated vegetative TAG content and improved plant growth. Diverting FAs from membrane lipids to TAGs in PAP2-overexpressing plants by constitutively expressing phospholipid:diacylglycerol acyltransferase1 (PDAT1) greatly increased TAG content in vegetative tissues without compromising biomass yield. These results highlight the potential of combining PAP2 with TAG-promoting factors to enhance carbon assimilation, FA synthesis and allocation to TAGs for optimized plant growth and storage lipid accumulation in vegetative tissues.
Biomass Analytics↗
Representative Phenomena of Cyclic Turbulent Combustion in High-Pressure Fuel Sprays
Abstract Cyclic variations in conventional diesel combustion engines can lead to large differences in engine out emissions even at steady operation. This study uses an optically accessible constant-pressure flow chamber to acquire fuel injections in quick succession to analyze mixing, auto-ignition, and combustion of diesel-surrogate n-heptane using multiple high-speed optical diagnostics. Prior studies have utilized fewer injections and/or they rely on analysis of ensemble average behavior. These approaches do not yield information on injection-to-injection variation or provide confidence in utilizing individual injection measurements for high-fidelity computational fluid dynamics(CFD) model validation. In this study, a large set of 500 injections is used to obtain global parameters including liquid length, vapor penetration length, ignition delay time, and lift-off length. Results for multiple injections are presented to illustrate large injection to injection variations. Potential sources for these variations are analyzed to conclude localized, small scale turbulence and rate of injection variations as the likely sources. Then, a statistical method based on z-scores is proposed and implemented to identify instantaneous injections that best represent the bulk data-set of jet boundaries measured independently by three different diagnostics. This synthesis of statistics-guided screening of data set and ensemble-average analysis offers higher confidence for CFD model validation relying upon both a representative single and average injection results.
42 ENGINEERING↗
Atomically Dispersed Supported Metal Catalysts: Understanding Fundamentals and Extending to New Catalyst Classes
We report synthesis, characterization, and catalyst performance data for supported metal catalysts, primarily those consisting of noble metals on metal oxide and zeolite supports.
36 MATERIALS SCIENCE↗
Design and Synthesis of Novel Block Copolymers for Efficient Opto-Electronic Applications
It has been predicted that nano-phase separated block copolymer systems containing electron rich donor blocks and electron deficient acceptor blocks may facilitate the charge carrier separation and migration in organic photovoltaic devices due to improved morphology in comparison to polymer blend system. This paper presents preliminary data describing the design and synthesis of a novel Donor-Bridge-Acceptor (D-B-A) block copolymer system for potential high efficient organic optoelectronic applications. Specifically, the donor block contains an electron donating alkyloxy derivatized polyphenylenevinylene (PPV), the acceptor block contains an electron withdrawing alkyl-sulfone derivatized polyphenylenevinylene (PPV), and the bridge block contains an electronically neutral non-conjugated aliphatic hydrocarbon chain. The key synthetic strategy includes the synthesis of each individual block first, then couple the blocks together. While the donor block stabilizes and facilitates the transport of the holes, the acceptor block stabilizes and facilitates the transport of the electrons, the bridge block is designed to hinder the probability of electron-hole recombination. Thus, improved charge separation and stability are expected with this system. In addition, charge migration toward electrodes may also be facilitated due to the potential nano-phase separated and highly ordered block copolymer ultra-structure.
Sun, Sam-Shajing↗
AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere
Materials Acceleration Platforms (MAPs) – also known as self-driving laboratories– present a new paradigm for materials science and promise an order of magnitude accelerated materials discovery compared to the traditional trial-and-error approach. Metal halide perovskites (MHPs) are an emerging class of materials for optoelectronic applications but are plagued by irreproducible optoelectronic quality, particularly for films fabricated in a humid atmosphere. Here, in this work, a machine learning (ML)-guided closed-loop platform is developed with a multimodal data fusion approach to predict synthesis–property relations for the optical quality of MHP thin films in relative humidities (RHs) ranging from 5–55%. The efficiency of this approach is confirmed by the fast-dropping learning rate to 2% after experimentally sampling less than 1% of the possible 5,000+ combinations. The prediction of synthesis–property relations is done by optical and imaging characterizations. In situ photoluminescence characterization revealed the origin of thin film quality variation at different RH. These insights provide an avenue for controlling the MHP crystallization by fine-tuning the synthesis parameters and RH for a given chemistry, thus lifting the need for stringent atmosphere control. The MAP enables an accelerated screening and understanding of the synthesis design space, facilitating rational synthesis recipe choice for a wide range of materials.
AI-driven robot↗
Modeling Terrestrial Dissolved Organic Carbon Loading to Western Arctic Rivers
The mobilization and land-to-ocean transfer of dissolved organic carbon (DOC) in Arctic watersheds is intricately linked with the region's climate and water cycle, and furthermore at risk of changes from climate warming and associated impacts. This study quantifies model-simulated estimates of runoff, surface and active layer leachate DOC concentrations and loadings to western Arctic rivers, specifically for basins that drain into coastal waters between and including the Yukon and Mackenzie Rivers. Model validation leverages data from other field measurements, synthesis studies, and modeling efforts. The simulations effectively quantify DOC leaching in surface and subsurface runoff and broadly capture the seasonal cycle in DOC concentration and mass loadings reported from other studies that use river-based measurements. A marked east-west gradient in simulated spring and summer DOC concentrations of 24 drainage basins on the North Slope of Alaska is captured by the modeling, consistent with independent data derived from river sampling. Simulated loadings for the Mackenzie and Yukon show reasonable agreement with estimates of DOC export for annual totals and four of the six seasonal comparisons. Nearly equivalent loading occurs to rivers which drain north to the Beaufort Sea and west to the Bering and Chukchi Seas. The modeling framework provides a basis for understanding carbon export to coastal waters and for assessing impacts of hydrological cycle intensification and permafrost thaw with ongoing warming in the Arctic.
54 ENVIRONMENTAL SCIENCES↗
Perceived Costs and Benefits of ICON Science and Foundational Documents associated with “Integrated, Coordinated, Open, and Networked (ICON) Science to Advance the Geosciences: Introduction and Synthesis of a Special Collection of Commentary Articles"
This data package is associated with the publication "Integrated, Coordinated, Open, and Networked (ICON) Science to Advance the Geosciences: Introduction and Synthesis of a Special Collection of Commentary Articles" in Earth and Space Science (Goldman et al. 2022; https://doi.org/10.1029/2021EA002099). The manuscript is an introductory article for a special collection of commentary articles across 19 geoscience disciplines that explore the challenges and opportunities associated with the use of ICON science principles. These principles focus on research intentionally designed to be Integrated, Coordinated, Open, and Networked (ICON) with the goal of maximizing mutual benefit (among stakeholders) and cross-system transferability of science outcomes. This data package contains data, figures, and R scripts associated with the cost/benefit analysis presented in the manuscript. The writing teams involved in the special collection placed each letter of ICON on a plot with perceived cost on one axis and perceived benefit on the other to summarize their perceptions of pursuing each principle of ICON science. These data were subsequently quantified and analyzed. Files are saved as .csv, .R, and .pdf. This data package also contains (1) the public foundational and instructional documents that enabled the crowdsourced creation of the special collection; (2) file-level metadata (flmd) that lists each file in the data package with a description; (3) data dictionary (dd) that defines column headers that appear in csv files. Files are saved as .pdf and .csv.
54 ENVIRONMENTAL SCIENCES↗
Remote Sensing techniques used to characterize soil erosion in southwestern Sao Paulo state
Within randomly sampled squares of a 1 km x 1 km grid, rill/gullies frequency, land cover/land use type and shape of the slopes were extracted from aerial photographs of the Ribeirao Anhumas drainage basin. Mean slope gradient, stream frequency and slope length were calculated on topographic maps. Ground truth data on fine sand/coarse sand ratio and vegetation cover densities were obtained. The MSS-LANDSAT-2 data (CCTs) were analyzed using single-cell, cluster synthesis and slicer algorithms. Graphical and statistical analyses of the data indicate that different slope gradients and land cover/land use types are the most significant factors related to the soil erosion process. The digital analysis of MSS data allowed the association among gray level classes and vegetation cover classes, which defined seven classes. These gray level classes and slope gradient classes were used to rank erosion risk.
Parada, N. D. J.↗
Experimental discovery of novel ammonia synthesis catalysts via active learning
Active learning based on literature and experimental data enabled the discovery of highly active novel catalysts for ammonia synthesis. Pathway analysis implies that these activities have been achieved by both structural and electronic promotion.
Chemistry↗
Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Prototype Physical Database for Passive Microwave Retrievals of Precipitation over the US Southern Great Plains
An accurate understanding of the instantaneous, dynamic land surface emissivity is necessary for a physically based, multi-channel passive microwave precipitation retrieval scheme over land. In an effort to assess the feasibility of the physical approach for land surfaces, a semi-empirical emissivity model is applied for calculation of the surface component in a test area of the US Southern Great Plains. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM PR) are utilized along with coincident brightness temperatures (Tbs) from the TRMM Microwave Imager (TMI), and cloud-resolving model profiles. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled top of the atmosphere Tbs show correlations to observations of 0.9, biases of 1K or less, root-mean-square errors on the order of 5K, and improved agreement over the use of climatological emissivity values. The synthesis of these models and data sets leads to the creation of a simple prototype Tb database that includes both dynamic surface and atmospheric information physically consistent with the land surface model, emissivity model, and atmospheric information.
microwave↗
Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis
Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.
25 ENERGY STORAGE↗
Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)
This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Information transfer satellite concept study. Volume 3: Appendices
Briefly reviewed are the various documents which supply background information for the information transfer satellite study. The sixteen papers reviewed are evaluated in terms of: (1) the category of service or demand being treated; (2) the extent to which information transfer predictions are quantified; (3) the degree to which the data supplied is adequate for the purposes of system synthesis; (4) some assessment as to the overall utility of the data presented in the paper.
Bergin, P.↗