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NASA NTRS2026

A Multi-Tier Autonomous Aerial Architecture for Wildfire Detection, Characterization, and Communication in Infrastructure-Denied Environments

Wildfire response depends on how fast an ignition can be confirmed and located, especially in remote regions where ground-based communication and monitoring may be limited. Geostationary sensors provide frequent observations but at kilometer-scale resolution, which is too coarse to resolve small fires in remote terrain. Ground camera networks require sightlines and infrastructure that back-country areas lack. To address these limitations, this work proposes a Multi-Tier Autonomous Wildfire Intelligence System that combines wide-area monitoring with targeted, high-resolution sensing. A solar-powered high-altitude long endurance (HALE) platform operating at approximately 60,000 ft provides persistent wide-area thermal and optical surveillance, running onboard edge inference to screen candidate ignitions and reduce false positives and downlink bandwidth. When a candidate ignition is detected, low-altitude uncrewed aircraft systems (UAS) can be deployed to conduct localized observations, including high-resolution imaging and atmospheric measurements such as wind and plume observation. By combining persistent detection with local sensing, the proposed architecture is designed to provide first responders with timely, high-resolution information about fire location and behavior to aid in emergency decision making.

Wildfire management, UAS, drones↗
NASA NTRS2009

FPGA-Based Networked Phasemeter for a Heterodyne Interferometer

A document discusses a component of a laser metrology system designed to measure displacements along the line of sight with precision on the order of a tenth the diameter of an atom. This component, the phasemeter, measures the relative phase of two electrical signals and transfers that information to a computer. Because the metrology system measures the differences between two optical paths, the phasemeter has two inputs, called measure and reference. The reference signal is nominally a perfect square wave with a 50- percent duty cycle (though only rising edges are used). As the metrology system detects motion, the difference between the reference and measure signal phases is proportional to the displacement of the motion. The phasemeter, therefore, counts the elapsed time between rising edges in the two signals, and converts the time into an estimate of phase delay. The hardware consists of a circuit board that plugs into a COTS (commercial, off-the- shelf) Spartan-III FPGA (field-programmable gate array) evaluation board. It has two BNC inputs, (reference and measure), a CMOS logic chip to buffer the inputs, and an Ethernet jack for transmitting reduced-data to a PC. Two extra BNC connectors can be attached for future expandability, such as external synchronization. Each phasemeter handles one metrology channel. A bank of six phasemeters (and two zero-crossing detector cards) with an Ethernet switch can monitor the rigid body motion of an object. This device is smaller and cheaper than existing zero-crossing phasemeters. Also, because it uses Ethernet for communication with a computer, instead of a VME bridge, it is much easier to use. The phasemeter is a key part of the Precision Deployable Apertures and Structures strategic R&D effort to design large, deployable, segmented space telescopes.

Rao, Shanti↗
DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

From IMPEL to Impact: Lessons Learned in Accelerating Innovative Building Technologies

The built environment is a complex ecosystem of social institutions and physical infrastructures. Innovation and entrepreneurship in the building industry are critical levers for market transformation toward equitable climate action. However, climate tech innovation for the built environment is not moving fast enough for global needs, and it lacks fundamental diversity, leading to inequitable outcomes. IMPEL (Incubating Market-propelled Entrepreneurial-mindset at the Labs and Beyond) - a U.S. Department of Energy incubator–addresses these critical issues. Over five years, IMPEL has enabled 250 innovators, including 55% women and diverse founders, to accelerate their buildings and clean energy technologies towards market and climate impact. IMPEL provides access to strategic mentoring and coaching, carbon tools training, testbeds, and powerful public-private pipelines, including industry demonstrations, non-dilutive grants, and venture capital networks. The IMPEL innovation ecosystem has accelerated the pace of innovation and market adoption of building decarbonization technologies. In this paper, leverage the IMPEL stakeholder ecosystem - from innovators to investors and product industry to policymakers - to analyze the critical barriers to decarbonization still encountered in the building industry. We study the IMPEL approach and highlight lessons learned that benefit young businesses pursuing innovative building and building-edge energy technologies to develop new ideas and products. Finally, we propose a ‘market forming’ framework to improve the quality and efficiency of the entrepreneurial ecosystem in the building industry. This framework could scale vetted technologies and the participation of diverse founders to de-risk the climate tech

Singh, Reshma↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
NASA NTRS2016

Generalized Fluid System Simulation Program, Version 6.0

The Generalized Fluid System Simulation Program (GFSSP) is a general purpose computer program for analyzing steady state and time-dependent flow rates, pressures, temperatures, and concentrations in a complex flow network. The program is capable of modeling real fluids with phase changes, compressibility, mixture thermodynamics, conjugate heat transfer between solid and fluid, fluid transients, pumps, compressors, and external body forces such as gravity and centrifugal. The thermofluid system to be analyzed is discretized into nodes, branches, and conductors. The scalar properties such as pressure, temperature, and concentrations are calculated at nodes. Mass flow rates and heat transfer rates are computed in branches and conductors. The graphical user interface allows users to build their models using the 'point, drag, and click' method; the users can also run their models and post-process the results in the same environment. Two thermodynamic property programs (GASP/WASP and GASPAK) provide required thermodynamic and thermophysical properties for 36 fluids: helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, parahydrogen, water, kerosene (RP-1), isobutene, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, R-11, R-12, R-22, R-32, R-123, R-124, R-125, R-134A, R-152A, nitrogen trifluoride, ammonia, hydrogen peroxide, and air. The program also provides the options of using any incompressible fluid with constant density and viscosity or ideal gas. The users can also supply property tables for fluids that are not in the library. Twenty-four different resistance/source options are provided for modeling momentum sources or sinks in the branches. These options include pipe flow, flow through a restriction, noncircular duct, pipe flow with entrance and/or exit losses, thin sharp orifice, thick orifice, square edge reduction, square edge expansion, rotating annular duct, rotating radial duct, labyrinth seal, parallel plates, common fittings and valves, pump characteristics, pump power, valve with a given loss coefficient, Joule-Thompson device, control valve, heat exchanger core, parallel tube, and compressible orifice. The program has the provision of including additional resistance options through User Subroutines. GFSSP employs a finite volume formulation of mass, momentum, and energy conservation equations in conjunction with the thermodynamic equations of state for real fluids as well as energy conservation equations for the solid. The system of equations describing the fluid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods. The application and verification of the code has been demonstrated through 30 example problems.

Majumdar, A. K.↗
DOE OSTI2020

Symmetry-breaking polymorphous descriptions for correlated materials without interelectronic U

Correlated materials with open-shell d- and f-ions having degenerate band edge states show a rich variety of interesting properties ranging from metal-insulator transition to unconventional superconductivity. The textbook view for the electronic structure of these materials is that mean-field approaches are inappropriate, as the interelectronic interaction U is required to open a band gap between the occupied and unoccupied degenerate states while retaining symmetry. We show that the latter scenario often defining what Mott insulators are, is in fact not needed for the 3d binary oxides MnO, FeO, CoO, and NiO. The mean-field-like band theory can indeed lift such degeneracies in the binaries when nontrivial unit cell representations (polymorphous networks) are allowed to break symmetries, in conjunction with a recently developed non-empirical exchange and correlation density-functional without an on-site interelectronic interaction U. We explain how density-functional theory (DFT) in the polymorphous representation achieves band gap opening in correlated materials through a separate mechanism to the Mott-Hubbard approach. Here, we show the method predicts magnetic moments and gaps for the four binary monoxides in both the antiferromagnetic and paramagnetic phases, offering an effective alternative to symmetry-conserving approaches for studying a range of functionalities in open d- and f-shell complex materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Investigating the effect of screen-printed structured graphite electrodes with low tortuosity for high-capacity and fast-charging lithium-ion batteries

A flexible screen-printed graphite electrode was fabricated as an anode for developing fast-charging lithium-ion batteries with low tortuosity. A homogenous anode ink was prepared by mixing graphite as the active material, carbon black (C45) as the conductive additive, and polyvinylidene fluoride (PVDF) as the binder in N-Methyl-2-pyrrolidone (NMP) solvent. The ink was deposited on a flexible copper foil via a stainless-steel screen consisting of an array of pores, that act as secondary pore networks (SPNs), using the screen-printing process. Lithium-ion battery half-cells were assembled using the printed graphite anode, lithium metal foil as the counter electrode, and 1.2 M lithium hexafluorophosphate (LiPF 6 ) in ethyl carbonate: ethyl methyl carbonate (EC: EMC = 3:7) as the electrolyte. The effect of SPNs on the cell performance was investigated by performing formation, rate and cycling tests on the assembled cells, at different C-rates. It was observed that the cells consisting of SPNs with a pore size of 100 μm and edge-to-edge distance of 100 μm between the pores exhibited significantly higher specific capacities of 168 and 129 mAh/g when compared to reference cells without SPNs, which had capacities of 120 and 85 mAh/g, at high C-rates of 4 C and 6 C, respectively. The cells with SPNs also demonstrated excellent cycling performance with ~ 95% capacity retention after 100 cycles at 2 C.

Fast charging lithium-ion battery↗
DOE OSTI2022

On the effect of mixing-driven vaporization in a homogeneous relaxation modeling framework

The homogeneous relaxation model (HRM) is one of the most widely used models to describe the liquid–gas phase transition in multiphase flows due to the occurrence of cavitation. However, in its original formulation, the HRM does not account for the presence of ambient gas species, which generally limits its applicability to the injector's internal flow where ambient gases are negligible. In this work, a mixing-driven vaporization (MDV) model was developed to extend the capability of the HRM in handling the mixing effect in the regions external to the nozzle, where vapor–liquid equilibrium for multi-species mixtures of fuel and ambient gas is considered. Herein, to assess the model performance, simulations of the Engine Combustion Network's Spray G injector were performed with the HRM and the MDV model under both flash-boiling and evaporating conditions. It was found that the MDV model led to a better match against x-ray measurements of fuel density in the near-nozzle region. In contrast to the HRM, the MDV model was able to reproduce the vaporization process in the mixing zone at the edge of the fuel jet, which aligns with the expected physics. This resulted in substantial differences in the prediction of other flow characteristics such as mixture temperature and pressure. Furthermore, this work demonstrates that evaporation timescales have a considerable effect on the MDV model's predictions, as shown by a parametric study in which a time factor was introduced to mimic the effect of different timescales due to different phase change mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2023

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

Introduction Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. Methods We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). Results The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Discussion Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git .

Chen, Xiaoyi↗
DOE OSTI2024

Electric Utility Industry Standards Landscape

The electric utility industry relies on robust communication protocols to manage complex electrical grid data. The inherent networked nature of electrical grids, coupled with the radial structure of the “last mile” portion delivering power to end-use customers, presents difficulties in describing electrical models using simple data constructs. The paper provides an overview of communication protocols that address electric utility data, including grid data, and classifies these protocols through identification key characteristics that make them suitable for different electric grid data domains. Because of their variety, grid edge devices and their associated dedicated protocols are assessed by groups: those primarily designed for energy production and storage, those related to flexible loads, and those related to electric vehicles. The intent of this report is to provide guidance for stakeholders to navigate the challenges posed by the numerous overlapping protocols available to address electric grid data. Although further industry review, refinement, and validation of the categorization of these protocols is recommended, the authors propose this categorization as a start to improve electric grid awareness and understanding. In addition, this report includes three recommended industry actions regarding protocols to improve communications on the electric grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1999

A New Frontier Beckons: Space Exploration in the 21st Century

Throughout recorded history, small groups of explorers have Pushed back the edge of the frontier and opened up new territories for others to follow. As the 20th Century closes, history will record that in the 1960's humankind opened the vast frontier beyond Earth's atmosphere. Although the Apollo program blazed a trail of exploration to the Moon, the development of space has only reached into low Earth orbit. The Space Shuttle and International Space Station programs, and the communication and scientific satellite networks established in orbit around the Earth, are all stepping stones that will enable explorers to venture once again beyond our home planet. Exploration is difficult. It is difficult for the people and machines that travel in extreme environments and endure harsh conditions, and it is difficult for the national leaders who must champion and fund the programs that lead to discovery and reward. Christopher Columbus spent many years meeting with the Kings of Portugal, England, and France only to see his dream of sailing west into the vast Atlantic with ships and crew. discredited. Queen Isabella of Spain was willing to look beyond the many problems plaguing her own shores and see the potential reward for her investment in the future. The voyages of Columbus set the stage for more Spanish explorers, who turned Spain into a great world power. The Apollo program and the unpiloted Lunar Orbiter, Surveyor, Mariner and Viking spacecraft that NASA launched in the 1966's &1970's were our country's first investment, in the exploration of the solar system. These human and robotic missions rewarded us with the first close views of the lunar and martian surfaces, and laid before our eyes territories as vast as all the continents of Earth combined. The Lunar Prospector, Near Earth Asteroid Rendezvous, and Mars Pathfinder and Global Surveyor missions are continuing our scientific conquest of the inner solar system, leading the way for humans to follow.

Mendell, Wendell W.↗
NASA NTRSDate not supplied

Tin-Essako 001: A Metal-Rich Ureilite?

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

olivine↗
DOE OSTI2023

Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations

Parallel computing plays a pivotal role in the efficient processing of large-scale graphs. Complex network analysis stands as a capti- vating research frontier, holding promise across diverse scientific domains such as sociology, biology, online media, and recommenda- tion systems. In this era, Machine Learning (ML) and Deep Learning (DL) have emerged as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around advancing parallel algorithms tailored for large-scale graph operations. To achieve this, I harness the power of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on the High-Performance Computing (HPC) platforms to unlock optimal performance. I also apply ML/DL techniques to HPC operational data, to streamline the monitoring and maintenance of supercomputers, alleviating the complexities associated with their upkeep and enhancing user support. My research echoes the syn- ergy between parallel computing, large-scale graph analysis, and ML/DL, improving computational efficiency and user experience.

Sattar, Naw Safrin↗
DOE OSTI2023

Wavelength-Dependent Spin Excitation with Circularly Polarized Light in CdSe Nanoplatelets

Zinc-blende (ZB) cadmium selenide (CdSe) nanoplatelets (NPLs) have drawn increasing attention as an ideal condensed-phase material for facilitating photon-to-spin transduction in quantum networks. Yet, a systematic investigation of their fidelity in converting circularly polarized light (CPL) to exciton spin polarization as a function of excitation wavelength is lacking. Here, this work demonstrates using time-resolved transient absorption (TA) spectroscopy combined with density functional theory calculations that ZB CdSe NPLs exhibit wavelength-dependent CPL-induced spin polarization. The capacity of CPL to inject spin polarization is maximized for resonant excitation of band-edge valence-to-conduction band (VB/CB) transitions, and the degree to which CPL induces spin polarization can be modified for non-resonant excitation wavelengths as excitation occurs at k-points away from the Γ point from VB states with varying degrees of mixed heavy-hole (HH) and light-hole (LH) characters. Furthermore, it is highlighted that spin polarization within the LH exciton (LX) population created from resonant LH/CB excitation is retained upon LX → HH exciton (HX) relaxation due to the slow electron spin-flip rate (~1.1 ps) compared to the LH → HH inter-band relaxation rate (~200 fs).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Semantic Property Graph for Scalable Knowledge Graph Analytics

Graphs are a natural and fundamental representation to describe entities, relationships, activities, and evolution of complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used data models to encode information in a graph. Both models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of the modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model to perform scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPG lacks the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We propose Semantic Property Graph (SPG) as a logical projection of reified RDF into the LPG model. SPG continues to use RDF ontology to define the type hierarchy of the projected graph and validate it against a given ontology. We present a framework to convert reified RDF graphs into SPG using two different computing environments. We also present cloud-based graph migration capabilities using Amazon Web Services.

Purohit, Sumit↗