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At least 1,261 records · Page 70

A Single-Point Modeling Approach for the Intercomparison and Evaluation of Ozone Dry Deposition Across Chemical Transport Models (Activity 2 of AQMEII4)

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.

Dry deposition↗

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗

Geostationary satellite position determination for common-view two-way time transfer measurements

In common-view two-way time transfer, each earth station receives an unwanted return signal from its own transmission as well as the desired signal from the other earth station. National Institute of Standards and Technology (NIST), the National Research Council (NRC), and the U.S. Naval Observatory (USNO) have been cooperating in a three-corner common-view two-way time transfer experiment. Some systematic effects are known to depend on the position of the satellite (Sagnac effect and the cross-correlation pulling of the pseudo-random codes). A method is presented for deriving accurate satellite ranges from each of three stations doing common-view two-way satellite time transfer measurements, when one (and only one) station also takes ranging measurements on its 'unwanted return signal' for a brief period. The method is applied to determine the variations in position of the satellite used over the course of the NIST/NRC/USNO SBS-3 experiment, with ranging data taken at NRC, where no additional hardware was required to automate the process. The fit and extrapolation which are employed in this method have an estimated precision of 2 m. If the delays of SBS-3 satellite KU band transponder and earth station equipment were measured accurately as well as the tropospheric refractions were well modeled and corrected, we would expect a ranging accuracy of 2.5 m and satellite positioning accuracy would be 200 m (latitude) 50 m (longitude) and 20 m (height above ellipsoid).

Zhuang, Qixiang↗

Fan Noise Control Using Herschel-quincke Resonators

The research effort proposed for this NASA NRA is mainly experimental. In addition, Virginia Tech is working in partnership with Goodrich Aerospace, Aerostructures Group for the analytical development needed to support the experimental endeavor, i.e. model development, design, and system studies. In this project, Herschel-Quincke (HQ)liner technology experiments will be performed at the NASA Glenn Active Noise Control Fan (ANCF) facility. A schematic of both inlet and aft HQ-liner systems installed in the ANCF rig as well as a picture of the Glenn facility is shown. The main goal is to simultaneously test in both the inlet and bypass duct sections. The by-pass duct will have HQ-systems in both the inner and outer duct walls. The main advantages of performing tests at the ANCF facility are that the effect of the inlet HQ-system on the by-pass HQ-system and vice versa, can be accurately determined from the in-duct modal data. Another significant advantage is that it offers the opportunity to assess (on a common basis) the proposed noise reduction concept on the ANCF rig which in the past has been used for assessing other active and passive noise reduction strategies.

Burdisso, Ricardo A.↗

Analysis of Electro-static Assisted Air Dehumidification

The removal of water vapor from the air to reduce relative humidity is a well-known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. Also, the findings from the literature focused mainly on small flow rates on the order of one to two cfm. Electrically-enhanced condensation consists of the use of highly charged particles, preferably highly charged water droplets, that attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. An effect of the electric charge is the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation. Consequently, the equilibrium between evaporation and condensation is shifted towards condensation. Following the application of the modified Kelvin-Thomson theory, we developed a preliminary physics-based model to predict an effective size range of the charged droplets for optimal dehumidification. The range resulted in about 2 to 4 μm in diameter, under few simplifying assumptions. The effect of the size and the charge of the electrosprayed droplets on the overall dehumidification rate was briefly discussed. The use of electrosprays to produce small but highly charged droplets was broadly discussed in this paper. The cone-jet mode was identified as the most suitable electrospray operational mode, and it generated droplets of small size and high electrical charge. The cone-jet stability was also analyzed in detail. The preliminary data of the present work and the model results indicated that several electrospray heads were required to achieve a 5% dehumidification rate for airflow rates of about 5 cfm.

Morcelli, Stefano↗

Analysis of New Data of Electro-static Assisted Air Dehumidification Processes

The removal of water vapor from the air to reduce relative humidity is a well-known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. Also, the findings from the literature focused mainly on small flow rates on the order of one to two cfm. Electrically-enhanced condensation consists of the use of highly charged particles, preferably highly charged water droplets, that attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. An effect of the electric charge is the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation. Consequently, the equilibrium between evaporation and condensation is shifted towards condensation. Following the application of the modified Kelvin-Thomson theory, we developed a preliminary physics-based model to predict an effective size range of the charged droplets for optimal dehumidification. The range resulted in about 2 to 4 m in diameter, under few simplifying assumptions. The effect of the size and the charge of the electrosprayed droplets on the overall dehumidification rate was briefly discussed. The use of electrosprays to produce small but highly charged droplets was broadly discussed in this paper. The cone-jet mode was identified as the most suitable electrospray operational mode, and it generated droplets of small size and high electrical charge. The cone-jet stability was also analyzed in detail. The preliminary data of the present work and the model results indicated that several electrospray heads were required to achieve a 5% dehumidification rate for airflow rates of about 5 cfm.

Morcelli, Stefano↗

Salts in Hot Water: Developing a Scientific Basis for Supercritical Desalination and Strategic Metal Recovery

Given the steady growth in world population and the ever-increasing fresh water demand, the only sustainable water supply option is desalination. Conceptually, desalination is very simple - just separate dissolved salts from water. While desalination research programs have existed since the 1960s and have resulted in a multitude of approaches, realizing affordable desalination has proven to be a challenge. Our goal is to meet this challenge using low cost heat sources to drive a liquid-discharge-free desalination process that can be co-mingled with the extraction of economically valuable co-products. High temperatures and pressures will be used to manipulate water’s properties, such as the dielectric constant, and hence its ability to solvate ions. Selective precipitation/recovery of valuable metal co-products creates new revenue streams (i.e., cost offsets). We will develop the scientific understanding necessary to control salt precipitation from supercritical seawater, inland brines, and water commonly co-produced with oil and gas. Atomistic simulations will be used to understand at a molecular level the changes in hydrogen bonding and ion solvation that occur as we increase the temperature and/or pressure and change the density and ion concentration. Our simulation results will also aid in the interpretation of experimental data and guide the development of applied thermodynamic models for engineering design calculations.

58 GEOSCIENCES↗

Pulse Detonation Engine Air Induction System Analysis

A preliminary mixed-compression inlet design concept for potential pulse-detonation engine (PDE) powered supersonic aircraft was defined and analyzed. The objectives of this research were to conceptually design and integrate an inlet/PDE propulsion system into a supersonic aircraft, perform time-dependent CFD analysis of the inlet flowfield, and to estimate the installed PDE cycle performance. The study was baselined to a NASA Mach 5 Waverider study vehicle in which the baseline over/under turboramjet engines were replaced with a single flowpath PDE propulsion system. As much commonality as possible was maintained with the baseline configuration, including the engine location and forebody lines. Modifications were made to the inlet system's external ramp angles and a rotating cowl lip was incorporated to improve off-design inlet operability and performance. Engines were sized to match the baseline vehicle study's ascent trajectory thrust requirement at Mach 1.2. The majority of this study was focused on a flight Mach number of 3.0. The time-dependent Navier Stokes CFD analyses of a two-dimensional approximation of the inlet was conducted for the Mach 3.0 condition. The Lockheed Martin Tactical Aircraft Systems-developed FALCON CFD code with a two equation 'k-1' turbulence model was used. The downstream PDE was simulated by an array of four sonic nozzles in which the flow areas were rapidly varied in various opening/closing combinations. Results of the CFD study indicated that the inlet design concept operated successfully at the Mach 3.0 condition, satisfying mass capture, total pressure recovery, and operability requirements. Time-dependent analysis indicated that pressure and expansion waves from the simulated valve perturbations did not effect the inlet's operability or performance.

Pegg, R. J.↗

Toward a Comprehensive Carbon Budget for North America: Potential Applications of Adjoint Methods with Diverse Datasets

A detailed mechanistic understanding of the sources and sinks of CO2 will be required to reliably predict future COS levels and climate. A commonly used technique for deriving information about CO2 exchange with surface reservoirs is to solve an "inverse problem," where CO2 observations are used with an atmospheric transport model to find the optimal distribution of sources and sinks. Synthesis inversion methods are powerful tools for addressing this question, but the results are disturbingly sensitive to the details of the calculation. Studies done using different atmospheric transport models and combinations of surface station data have produced substantially different distributions of surface fluxes. Adjoint methods are now being developed that will more effectively incorporate diverse datasets in estimates of surface fluxes of CO2. In an adjoint framework, it will be possible to combine CO2 concentration data from long-term surface monitoring stations with data from intensive field campaigns and with proposed future satellite observations. A major advantage of the adjoint approach is that meteorological and surface data, as well as data for other atmospheric constituents and pollutants can be efficiently included in addition to observations of CO2 mixing ratios. This presentation will provide an overview of potentially useful datasets for carbon cycle research in general with an emphasis on planning for the North American Carbon Project. Areas of overlap with ongoing and proposed work on air quality/air pollution issues will be highlighted.

Andrews, A.↗

I’ve Looked at Clouds from Both Sides, Now: Viewpoints from Surface and Spaceborne Lidar Systems

Clouds play a critical role in the Earth’s climate system because they are inextricably linked to the hydrological cycle and radiation budget. Information about cloud height, thickness, occurrence, and amount are critical inputs for a host of numerical applications involving climate research. Therefore, it is important to have highly accurate and quantitative data records of cloud properties that span several years and geographic regions. Verification of even the most basic modeling processes demands long term and continuous observations of global cloud occurrence, if there is to be any confidence in their fidelity. For a number of reasons, however, it is impossible to gauge the complexities of clouds from a single source. Fundamentally, an array of remote sensing methods is needed in order to provide a complete picture. Within the Micropulse Lidar Network (MPLNET), we have developed a new algorithm to improve the quality of our cloud products. The largest impact of the changes to the cloud detection algorithm is evident with high clouds (those with cloud base > 5 km). Furthermore, polarized measurements enable us to determine the cloud thermodynamic phase. Naturally, the next step is to investigate how these improvements compare with observations from spaceborne lidars (e.g. CALIOP) which have a better (unobstructed) view of high clouds. Recent advances in remote sensing have revealed that cirrus clouds are the most common cloud genus observed in the atmosphere. Furthermore, cirrus skew highly towards relatively low cloud optical depths, as observed from both surface and spaceborne viewpoints. The radiative impacts of these findings are quite significant, considering the cumulative effect cirrus exhibit when compared to low clouds.

Jasper R Lewis↗

Development of Micro Pulse Lidar Network (MPLNET) Level 3 Satellite Validation Products in Advance of the EarthCARE Mission

Assessing the cirrus cloud radiative effect is crucial to establish their feedback on the Earth- atmosphere system. For this reason, cirrus clouds are of paramount importance in climate. Moreover, these tiny ice clouds are the most common cloud gene, continuously covering 30% of the Earth’s surface, peaking to 70% in the tropical and equatorial regions. The same authors, in three different recent studies, assessed the yearly cirrus cloud radiative effects characteristics for different NASA MPLNET permanent observational sites, deployed at different latitudes, e. g. Goddard Space Flight Center, Singapore, and Fairbanks Alaska. The analysis put in evidence that the cirrus cloud can be both cooling or warming agents of the Earth-atmosphere system during the daytime, depending on their latitude. The cirrus clouds are warming agents in equatorial/tropical regions because of the higher averaged solar zenith angle, and become neutral at mid-latitudes. At polar latitudes instead, cirrus clouds become cooling agents because of the lower solar zenith angle. In this analysis instead, using the Fu-Liou-Gu radiative transfer model, we assess how the cirrus cloud radiative effects, both at the top of the atmosphere and surface, changed over twenty years. The analysis is extended also to evaluate also changes in cloud optical depth over the same period. This is unprecedented research, because to our knowledge, no other analysis has been carried out from ground-based measurement for such a long period. As a future perspective, the analysis will be repeated for the different observational sites of the MPLNET lidar network to evaluate cirrus cloud radiative effects at a global scale.

Jasper R. Lewis↗

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An International Strategy for Human Exploration of the Moon: The International Space Exploration Coordination Group (ISECG) Reference Architecture for Human Lunar Exploration

The International Space Exploration Coordination Group (ISECG) was established in response to The Global Exploration Strategy: The Framework for Coordination developed by fourteen space agencies and released in May 2007. Several ISECG participating space agencies have been studying concepts for human exploration of the moon that allow individual and collective goals and objectives to be met. This 18 month study activity culminated with the development of the ISECG Reference Architecture for Human Lunar Exploration. The reference architecture is a series of elements delivered over time in a flexible and evolvable campaign. This paper will describe the reference architecture and how it will inform near-term and long-term programmatic planning within interested agencies. The reference architecture is intended to serve as a global point of departure conceptual architecture that enables individual agency investments in technology development and demonstration, International Space Station research and technology demonstration, terrestrial analog studies, and robotic precursor missions to contribute towards the eventual implementation of a human lunar exploration scenario which reflects the concepts and priorities established to date. It also serves to create opportunities for partnerships that will support evolution of this concept and its eventual realization. The ISECG Reference Architecture for Human Lunar Exploration (commonly referred to as the lunar gPoD) reflects the agency commitments to finding an effective balance between conducting important scientific investigations of and from the moon, as well as demonstrating and mastering the technologies and capabilities to send humans farther into the Solar System. The lunar gPoD begins with a robust robotic precursor phase that demonstrates technologies and capabilities considered important for the success of the campaign. Robotic missions will inform the human missions and buy down risks. Human exploration will start with a thorough scientific investigation of the polar region while allowing the ability to demonstrate and validate the systems needed to take humans on more ambitious lunar exploration excursions. The ISECG Reference Architecture for Human Lunar Exploration serves as a model for future cooperation and is documented in a summary report and a comprehensive document that also describes the collaborative international process that led to its development. ISECG plans to continue with architecture studies such as this to examine an open transportation architecture and other destinations, with expanded participation from ISECG agencies, as it works to inform international partnerships and advance the Global Exploration Strategy.

Laurini, Kathleen C.↗

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗

Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes

Solid-state batteries are projected to exhibit improved energy densities and safety compared to liquid-electrolyte-based systems. Consequently, the development of solid electrolytes that can conduct cations at rates comparable to that of liquid electrolytes is an active area of research. To facilitate this search it is desirable to understand the chemical and structural features that control ion transport through a crystalline lattice. Here, machine learning (ML) is used to identify features that influence ion mobility in solids, quantify the relative importance of those features, and demonstrate how the variation of individual features alters ion mobility. Adopting the anti-perovskite lattice as a model system, ML algorithms were trained to predict migration barriers from a data set containing hundreds of barriers calculated using density functional theory. In this study, more than 10 6 feature sets of non-redundant descriptors were evaluated for their ability to predict migration barriers. The present analysis correctly reiterates the importance of several features that are known to influence mobility – for example, anion polarizability – but also identifies others whose importance is not widely recognized. Lattice properties such as hopping distance and channel width exhibit the greatest influence on cation mobility. Individual conditional expectation analyses performed on these features shows that barriers decrease as hopping distance decreases and as channel width increases. The defect formation energy – which is commonly associated with the concentration of defects, but not with their mobility – is identified as an important feature for interstitial migration. In sum, this analysis aids in the design of optimal solid electrolytes by simplifying the multi-dimensional design space to a sub-set of properties that are the most important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.

Space Biology↗

BASIN-3D: A brokering framework to integrate diverse environmental data

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

Varadharajan, C↗

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗