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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Voltage Mining for (De)lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulas is trained and shows state-of-the-art performance when compared to two established composition-based ML models for material properties predictions, Roost and CrabNet.

25 ENERGY STORAGE↗
DOE OSTI2021

Comparative studies of 2HDMs under the Higgs boson precision measurements

We perform comparative studies for four types of the two Higgs Doublet Models (2HDMs) under the precision measurements of the Standard Model (SM) Higgs observables at the proposed Higgs factories. We explore the discovery potential based on the hypothetical deviations in the precision data for the 2HDMs up to one-loop level. We find 5σ observability from the χ2 fitting in a significant theory parameter space at future Higgs factories. For the Type-I 2HDM, regions with cos(β - α) ≲ -0.1 or cos(β - α) ≳ 0.08 are discoverable at more than 5σ level. For the other three types of 2HDMs, the 5σ region is even bigger: | cos(β - α)| ≳ 0.02 for tan β ~ 1. At small and large values of tan β, the region in cos(β - α) is further tightened. We examine the extent to which the different 2HDM theories may be distinguishable from one to the other at the 95% Confidence Level with four benchmark points as case studies. We show that a large part of the parameter space of the other types of 2HDMs can be distinguished from the benchmark points of the target model. The impacts of loop corrections are found to be significant in certain parameter regions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2005

Mathematical Metaphors: Problem Reformulation and Analysis Strategies

This paper addresses the critical need for the development of intelligent or assisting software tools for the scientist who is working in the initial problem formulation and mathematical model representation stage of research. In particular, examples of that representation in fluid dynamics and instability theory are discussed. The creation of a mathematical model that is ready for application of certain solution strategies requires extensive symbolic manipulation of the original mathematical model. These manipulations can be as simple as term reordering or as complicated as discovery of various symmetry groups embodied in the equations, whereby Backlund-type transformations create new determining equations and integrability conditions or create differential Grobner bases that are then solved in place of the original nonlinear PDEs. Several examples are presented of the kinds of problem formulations and transforms that can be frequently encountered in model representation for fluids problems. The capability of intelligently automating these types of transforms, available prior to actual mathematical solution, is advocated. Physical meaning and assumption-understanding can then be propagated through the mathematical transformations, allowing for explicit strategy development.

Thompson, David E.↗
NASA NTRS1989

The discovery of a new infrared emission feature at 1905 wavenumbers (5.25 microns) in the spectrum of BD + 30 deg 3639 and its relation to the polycyclic aromatic hydrocarbon model

A new IR emission feature at 1905/cm (5.25 microns) has been discovered in the spectrum of BD + 30 deg 3639. This feature joins the family of well-known IR emission features at 3040, 2940, 1750, 1610, '1310', 1160, and 890/cm. The origin of this new feature is discussed and it is assigned to an overtone or combination band involving C-H bending modes of polycyclic aromatic hydrocarbons (PAHs). Laboratory work suggests that spectral studies of the 2000-1650/cm region may be very useful in elucidating the molecular structure of interstellar PAHs. The new feature, in conjunction with other recently discovered spectral structures, suggests that the narrow IR emission features originate in PAH molecules rather than large carbon grains.

Allamandola, L. J.↗
NASA NTRS1989

The discovery of a new infrared emission feature at 1905 wavenumbers (5.25 microns) in the spectrum of BD +30 degrees 3639 and its relation to the polycyclic aromatic hydrocarbon model

We have discovered a new IR emission feature at 1905 cm-1 (5.25 microns) in the spectrum of BD +30 degrees 3639. This feature joins the family of well-known IR emission features at 3040, 2940, 1750, 1610, "1310," 1160, and 890 cm-1 (3.3, 3.4, 5.7, 6.2, "7.7," 8.6, and 11.2 microns). The origin of this new feature is discussed and it is assigned to an overtone or combination band involving C-H bending modes of polycyclic aromatic hydrocarbons (PAHs). Laboratory work suggests that spectral studies of the 2000-1650 cm-1 (5.0-6.1 microns) region may be very useful in elucidating the molecular structure of interstellar PAHs. The new feature, in conjunction with other recently discovered spectral structure, suggests that the narrow IR emission features originate in PAH molecules rather than large carbon grains. Larger species are likely to be the source of the broad underlying "plateaus" seen in many of the spectra.

NASA Discipline Exobiology↗
NASA NTRS2005

Composition of Meridiani Hematite-rich Spherules: A Mass-Balance Mixing-Model Approach

One of the great surprises of the Mars Exploration Rovers (MER) mission is the discovery at Meridiani Planum that the surface hematite signature observed from orbit is attributable largely to a surface enrichment of hematite-rich spherules, thought to be concretions, that have weathered out of rocks similar to the underlying sulfate-rich rock formation [1]. A strong hematite signature has been observed by the Mini-TES [2] and by in-situ measurements of spherule-rich targets by the Mossbauer spectrometer (MB) [3] and the alpha-particle X-ray spectrometer (APXS) [4]. The Mini-TES derived spectrum of spherule-rich targets on the plains is consistent with nearly pure coarse-grained hematite, with perhaps as little as 5-10 areal % of other components [2]. The occurrence and abundance of the spherules as the bearer of the widespread hematite signature observed by MGS TES over much of Meridiani Planum is significant for global remote sensing, and their occurrence as concretions in the outcrop lithology is significant for the diagenetic history and role of water in the formation of the sedimentary rock formation [5].

jOLLIFF, b. l.↗
NASA NTRSDate not supplied

Autonomous Ocean World Exploration: Advancement of a Virtual Testbed

The search for life (extinct or extant) and potentially habitable bodies in our solar system and beyond is one of the 12 priority science questions outlined in the National Acadamies’ 2022 decadal survey [5]. Extraterrestrial destinations containing liquid water present an opportunity to search for life as we know it, and in recent years an increasing number of such locations have been discovered within our solar system. Several Jovian moons—Europa, Ganymede, and Callisto [10]—and the Saturnian moons Enceladus [8] and Titan [9] are known or suspected to harbor massive subsurface oceans. Of these "ocean worlds", Europa is the focus of at least one planned NASA orbiter mission, Europa Clipper [4], and an early lander mission concept, the Europa Lander [2, 3]. Whereas most robotic missions to the Moon and Mars (e.g. orbiters, rovers, landers) to date have had ground controllers on Earth tightly involved in mission operations, missions to more distant worlds will require a high degree of onboard autonomy due to long communication lags and blackouts, harsh environments (radiation, cold), and more limited battery and hardware life. The past decade has seen great advances in both AI technologies and computing scalability and performance that offer promising solutions for spacecraft autonomy and motivate the software system and research programs described in this paper. The Ocean Worlds Autonomy Testbed for Exploration, Research, and Simulation (OceanWATERS) [1], which has been in development at the NASA Ames Research Center since 2018, is a virtual environment for testing lander autonomy solutions. It is built on the Robot Operating System (ROS), runs on consumer-grade Linux workstations, and was released as open source in 2020. OceanWATERS provides a physical and visual simulation of a prototypical lander in a Europa-like environment (Figure 1). The lander was modeled after requirements and specifications made in JPL’s Europa Lander Study of 2016 [3]. Simulated lander systems include stereo cameras and spotlights mounted on an antenna mast that pans and tilts, a 6 degrees of freedom (DoF) robotic arm with a force-torque sensor and two interchangeable end effectors, and a battery pack power system. The environment consists of multiple terrain models including a highly detailed model sourced from the FROST dataset [11], simulation of surrounding planetary bodies based on an ephemeris model, and lighting from the sun with associated surface illumination, reflectance, and shadows. Operations supported by OceanWATERS include panoramic and directed imaging of the environment and lander workspace, Cartesian and joint-level arm commanding, grinding of the terrain surface (e.g. digging a trench), and scooping of ground material (Figure 2) which can be discarded or collected as science samples in a receptacle that can be emptied (science operations themselves are not simulated). These operations are realized as ROS Actions and are complimented by a wide selection of telemetry that is continually produced by each lander subsystem. The power system model is driven by the open-source Generic Software Architecture for Prognostics (GSAP) [11] that predicts the battery’s remaining useful life and other characteristics. As a testbed for high-level autonomy, OceanWATERS provides an execution framework based on PLEXIL [12], an open-source plan specification language and execution engine developed largely at Ames. NASA's initial development of OceanWATERS, as well the Ocean Worlds Lander Autonomy Testbed (OWLAT) [6], a complimentary physical testbed developed at JPL, was the first step in a plan for realizing candidate onboard autonomy solutions for such planetary landers. In 2020 NASA solicited applications for its Autonomous Robotics Research for Ocean Worlds (ARROW) program, and in 2021 the similar Concepts for Ocean worlds Life Detection Technology (COLDTech) program. Collectively six research teams, based in universities and companies across the United States, were awarded grants to develop and demonstrate autonomy solutions on OceanWATERS and OWLAT. These 1–2-year projects have now finished or are nearing completion, and a wide variety of autonomy challenges in ocean world surface missions were addressed. Prototyped and demonstrated solutions have included autonomous discovery, response and adaptation to system faults and unexpected environmental events, world model synthesis through perception, plan synthesis using learned models, methods to optimize sample target selection and prioritize science data transmission, extension of PLEXIL for stochastic decision-making, and an integration of a model of JPL’s mission-ready COLDArm [7]. Technologies used in these projects include many forms of machine learning, causal reasoning, automated planning, Markov decision processes, formal methods, and other advanced techniques. A more detailed summary of the ARROW and COLDTech projects is given herein. OceanWATERS has had significant enhancements since its open-source release in 2020. Many of its new features were driven or shaped by feedback from the ARROW and COLDTech teams and requirements of their projects. In support of enabling autonomous adaptation to spacecraft faults (a specific capability solicited by both programs), a fault injection and detection framework was developed that supports a wide and growing range of fault types such as locked joints, image loss, and battery failures. The power system model was completed and integrated into the simulator, starting as a single-cell battery model and later upgraded to a multi-cell model with associated faults such as cell disconnection. Arm/terrain interaction was improved by adding a force-torque sensor and associated faults, and an analytic dig force model based on the Balovnev bucket force equations. Environment fidelity was increased by modeling terrain deformation resulting from digging and scooping; visual improvements were made in textures, lighting, and shadows. To facilitate interoperation with OWLAT, a unified command and telemetry interface between the testbeds was developed at the ROS level, along with a PLEXIL interface. The number of lander operations was greatly expanded (e.g. with Cartesian-based arm and antenna movement), and a framework was designed for users to build their own lander actions. A GUI for PLEXIL plan selection was created (Figure 3), and an expansive set of plans were added, such as those that illustrate patterns for fault handling. This paper provides a self-contained high-level description of OceanWATERS, focusing on more detailed coverage of the aforementioned enhancements. It provides a high-level summary of the projects undertaken by participants in the ARROW and COLDTech programs and how these efforts have helped shape OceanWATERS. Finally, potential future work and directions for the testbed are listed, as likely informed by the recent planetary science decadal survey [5].

K Michael Dalal↗
DOE OSTI2025

Review of neutral naturalness

The hierarchy between the mass parameter of the Higgs boson and larger mass scales becomes ever more puzzling as experiments explore higher energies. Neutral naturalness is the umbrella term for symmetry-based explanations for these hierarchies whose quark symmetry partners are not charged under the SU(3) c color gauge group of the Standard Model. Though the first manifestations of this idea predate the physics runs of the Large Hadron Collider, since the Higgs discovery this paradigm has grown and developed to include a wide variety of concrete realizations with connections to intriguing collider signals. Determining the phenomenology of such models often requires the characterization—typically relying on lattice calculations—of a new confining gauge symmetry. This presents additional motivation to further develop our understanding of nonperturbative field theory as well as to pursue specific lattice studies. The wide range of suggested hidden sectors also produces a variety of dark matter candidates, intersections with astrophysics and cosmology, and ties to neutrinos and flavor. In this review, we orient the reader within both this growing collection of specific models and the physical phenomena they produce. We also survey the often less familiar dynamics of hidden-sector glueballs and quirks. In addition to providing a guide to past efforts, we reveal interesting directions for further study.

Batell, Brian [Univ. of Pittsburgh, PA (United Sta↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

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

Emergence of p+ip superconductivity in two-dimensional doped Dirac systems

Searching for the $p + ip$ superconducting (SC) state has become a fascinating subject in condensed matter physics recently, as a dream application awaiting in topological quantum computation. Unfortunately, so far there is no universal principle for realizing $p + ip$ in generic solid-state systems. Here we report a theoretical discovery of a $p + ip$ SC ground state (coexisting with ferromagnetic order) in the honeycomb lattice Hubbard model in the extremely strong-coupling limit (e.g., infinite U) at low doping $(δ < 0.2)$, by using both the state-of-art Grassmann tensor product state approach and a continuum quantum field theory approach. Our discovery suggests a mechanism for the $p + ip$ SC state in generic strongly correlated systems based on spin-charge separation and the charge current–current coupling scenario, which opens a door towards experimental realization. The $p + ip$ SC state has an instability towards a potential non-Fermi liquid with a large but finite U . Nevertheless, by applying an in-plane Zeeman field, such a $p + ip$ SC state can be stabilized with finite U in a very wide range of doping. Relevant realistic materials are also proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

A long-term space astrophysics research program: The evolution of the quasar continuum

The research program supported by this grant now has great momentum. Numerous papers are in progress, and a strong multi-wavelength observing program is rapidly accumulating data on samples of high redshift quasars across the spectrum. ROSAT spectra of quasars continue to yield surprises. Of four z = 3 quasars with X-ray spectra, three show strong absorption. This contrasts strongly with the situation for luminous AGN at low redshifts where fewer than 1 in 20 show X-ray absorption. A new site for this absorption is probably needed, either around the quasar (e.g. in a cluster cooling flow) or along the line of sight (e.g. in a Damped Lyman-alpha system). The unabsorbed quasar allows limits on the physical conditions in a damped Lyman-alpha cloud to be calculated, and will allow a X-ray Gunn-Peterson test to be applied that will limit the fraction of the closure mass in an intergalactic medium. The X-ray spectral indices of these z = 3 quasars show no change from those of similar objects at low z, suggesting that 'short-lifetime' models apply. Eight other z = 3-4 quasars have been detected and their energy distributions from X-rays to Infrared (using new infrared spectrographs) have been compiled. These are now being compared with the low z continua from the 'Atlas of Quasar Energy Distributions' to search for evolutionary changes. The discovery of a likely warm absorber in 3C351 made recognition of another example simple. Also, modeling of the conditions in the absorber in 3C351 using the OVI absorption line from HST and the high ionization emission lines, suggests that the broad line region is indeed the origin of the warm absorber in this quasar, and by extension, others. Warm absorbers can now be used as a new diagnostic of this region. The X-ray spectrum of a 'Red Quasar', 3C212, has a cut-off spectrum, which could be fitted by an absorbed power-law, or more remarkably, by an unabsorbed black body. Using our quasi-simultaneous optical data and photoionization modeling we are able to rule out this latter possibility. The long-sought key to understanding red quasars has now been found. (Reddening was earlier ruled out because no 2175A absorption feature was seen. This is now seen to have been misleading.)

Elvis, M.↗
DOE OSTI2024

Assembly and Repair of Photosystem II in Chlamydomonas reinhardtii

Oxygenic photosynthetic organisms use Photosystem II (PSII) to oxidize water and reduce plastoquinone. Here, we review the mechanisms by which PSII is assembled and turned over in the model green alga Chlamydomonas reinhardtii. This species has been used to make key discoveries in PSII research due to its metabolic flexibility and amenability to genetic approaches. PSII subunits originate from both nuclear and chloroplastic gene products in Chlamydomonas. Nuclear-encoded PSII subunits are transported into the chloroplast and chloroplast-encoded PSII subunits are translated by a coordinated mechanism. Active PSII dimers are built from discrete reaction center complexes in a process facilitated by assembly factors. The phosphorylation of core subunits affects supercomplex formation and localization within the thylakoid network. Proteolysis primarily targets the D1 subunit, which when replaced, allows PSII to be reactivated and completes a repair cycle. While PSII has been extensively studied using Chlamydomonas as a model species, important questions remain about its assembly and repair which are presented here.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Search for Neutral Heavy Scalars in ttZ Events Using 13 TeV pp Collisions at CMS

This thesis presents a search for a heavy pseudoscalar particle, A, which decays into a lighterscalar, H, and a Z boson within the Two-Higgs-Doublet Model (2HDM). We focus on Zboson decays to leptons, with H decays into a t t ̄ pair. The search uses the Asimov dataset–a theoretical dataset generated to predict the expected results for discovery and exclusion based on conditions from proton-proton collisions at the LHC, atintegrated luminosity of 138f b−1, as recorded by the CMS detector. The analysis focuses on events with three charged leptons, two of which come from the Z boson decay and one from a top quark decay. The expected results suggest the search has high sensitivity, showing discovery potential across much of the 2HDM Type-II parameter space, with possible deviations from the Standard Model background exceeding 5σ significance. The search sets upper limits on the cross-section times branching ratio, assuming narrow particle widths, for A masses up to 2100 GeV and H masses up to 1900 GeV. The results are interpreted in the 2HDM Type-II framework to exclude certain regions of parameter space at the 95% confidence level as a function of particle masses and variables such as tanβ and cos(β−α). The findings highlight the potential reach implications of future search in this parameter region.

Rudrabhatla, Sahithi [Illinois U., Chicago]↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗
NASA NTRS2016

The First Neptune Analog or Super-Earth with a Neptune-Like Orbit: MOA-2013-BLG-605Lb

We present the discovery of the first Neptune analog exoplanet or super-Earth with a Neptune-like orbit, MOA- 2013-BLG-605Lb. This planet has a mass similar to that of Neptune or a super-Earth and it orbits at 9 approximately 14 times the expected position of the snow line, a(sub snow), which is similar to Neptuneʼs separation of 11 a(sub snow) from the Sun. The planet/host-star mass ratio is q = (3.6 +/- 0.7) × 10(exp −4) and the projected separation normalized by the Einstein radius is s = 2.39 +/- 0.05. There are three degenerate physical solutions and two of these are due to a new type of degeneracy in the microlensing parallax parameters, which we designate "the wide degeneracy." The three models have (i) a Neptune-mass planet with a mass of M(sub p) = 21(+6/-7)(M) orbiting a low-mass M-dwarf with a mass of M(sub h) = 0.19(+0.05/-0.06 (solar mass)), (ii) a mini-Neptune with M(sub p) = 7.9(+1.8/-1.5)(M)) orbiting a brown dwarf host with M(sub h) = 0.068(+0.019/-0.011(solar mass)), and (iii) a super-Earth with M(sub p) = 3.2(+0.5/-0.3(M)) orbiting a low-mass brown dwarf host with M(sub h) = 0.025(+0.005/-0.004)(solar mass)), which is slightly favored. The 3D planet-host separations are 4.6(+4.7/-1.2)au, 2.1(+1.0/-0.2)au, and 0.94(+0.67/-0.02)au, which are 8.9(+10.5/-1.4)m 12(+7/-1), or 14(+11/-1) times larger than a(sub snow) for these models, respectively. Keck adaptive optics observations confirm that the lens is faint. This discovery suggests that low-mass planets with Neptune-like orbits are common. Therefore processes similar to the one that formed Neptune in our own solar system or cold super-Earths may be common in other solar systems.

Einstein radius↗
DOE OSTI2025

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.

biocuration↗