University of Maryland, College Park

NSF Awards · FY 2025 · 2025-09

The objective of this Smart & Connected Communities - Development Grants (SCC-DG) project is to support research on co-design of bike networks that are safe, connected, and aligned with local needs. Cities struggle to design bikeways that balance technical constraints, safety considerations, lived realities, and aspirations of their citizens. This project explores how advances in generative artificial intelligence (AI) bridge the gap by integrating community input directly into the design process. During the planning phase, the team builds strong partnerships with key stakeholders, assesses how generative AI can support human-centered design, and refines key research questions on balancing engineering feasibility with community values. Infrastructure planning is driven by complex datasets of community needs, safety requirements, engineering constraints, and available funding. Current tools and methods often lack the ability to process varied information in an integrated way. The central research question guiding the planning effort is whether a generative AI-based system can effectively integrate multimodal and heterogeneous data inputs to produce bikeway designs that are technically feasible, regulation-compliant, and aligned with community priorities. Ultimately, this project contributes to advancing the responsible use of AI in participatory infrastructure design and planning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Fire investigation training programs aim to equip investigators with the skills to identify fire origins and causes, but the chaotic nature of post-fire scenes presents substantial challenges. Investigators must connect evidence and scene features to the fire dynamics that shaped a scene, which requires strong spatial-temporal reasoning skills. Immersive training in realistic environments is essential to help investigators piece together evidence, analyze fire progression, and accurately trace fire origins. However, most training programs in the U.S. rely on lectures and 2D visuals, lacking the immersive experience needed to develop these crucial reasoning skills. Further, many investigators lack formal education in fire science, which is essential for understanding fire behavior. This project seeks to create a multimodal embodied training platform that advances fire investigation training through adaptive deliberate practice and learning analytics, focusing on the spatial-temporal reasoning skills needed to reconstruct fire development from observed fire damage and scene features. This new training approach will improve the quality and effectiveness of fire investigation practices, benefiting public safety by enabling more accurate identification of fire origins and causes. Many of the ideas can be extended to related fields such as crime scene investigation and other STEM areas requiring advanced spatial-temporal reasoning skills. To achieve these goals, the training platform will incorporate an AI-driven, physics-informed 3D fire modeling system that dynamically generates and visualizes fire scenarios based on learner-selected fire origins. Learners will identify and analyze scattered evidence, reconstruct fire progression, test interpretations, and explore variations in fire dynamics relative to observed damage patterns. Multimodal sensors will track learner interactions, enabling adaptive instructional approaches, enhancing engagement, and fostering seamless interactions between learners, instructors, and virtual fire scenarios. A deliberate practice pedagogical model will integrate structured skill-building exercises, multimodal analytics for performance assessment, and personalized adaptive training tailored to individual learner profiles. The platform's effectiveness will be evaluated in three phases: iterative expert reviews, student prototype assessments, and nationwide testing by early-career fire investigators, ensuring robust skill development in spatial-temporal reasoning for fire investigation. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project will develop mathematical models that will aid in the understanding of animal migration. Migration is a widespread phenomenon that occurs seasonally as animals shift their locations in response to changing conditions. Oftentimes these changes involve spatial variation in resources that serve as cues for animals to track, resulting in wave-like population expansions. This research will use a series of novel mathematical modeling approaches to explore such seasonal, wave-like migratory dynamics, with a specific focus on understanding how the quality and quantity of resources interact to shape the pace and pattern of migration for varied theoretical scenarios. In addition, a pre-existing dataset of GPS tracking data for the critically endangered scimitar-horned oryx (Oryx dammah) will be analyzed to characterize when, where, and how well the animals track seasonal changes in resource availability in a resource-poor landscape. The project will support the training of undergraduate and graduate students who are developing skills and knowledge at the interface of mathematics and biology. Consumer tracking of transient resources occurs worldwide in a wide range of systems and taxa. The 'green wave surfing' hypothesis is a recent conceptual advance in understanding such resource tracking that is now widely discussed with regard to seasonal migrations of ungulates, birds, and marine species. According to this hypothesis, migrating consumer species living in seasonal systems should closely track the progression of the highly nutritious plant green-up wave that moves across the landscape as the growing season begins. Empirical data demonstrates that such tracking does occur for some individuals, populations, and species; however, 'surfing the green wave' is not universal, and instead some taxa either jump ahead of the green wave or lag behind it as it seasonally translates in space. The project will develop hybrid dynamical system models involving reaction-advection-diffusion equations with reaction and diffusion coefficients and growth governed by the quantity and quality of the resource green-up wave. Model variants including Allee effects, shifting habitats, and population structure will bring added biological realism. Research will address the impacts of sex- and age-specific migratory behaviors, predation, and mating success on migratory dynamics. Methods from differential equations, integral equations, and dynamical systems will be employed to identify conditions under which populations can persist in the long run. Existence of equilibrium solutions, traveling wave solutions, and oscillating solutions in time and density will be established to understand how 'surfing the green wave' promotes population growth and develops spatiotemporal patterns in population persistence on bounded domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Southern Ocean is a conduit for exchange between the atmosphere and deep ocean, where heat and carbon are stored for thousands of years, via the ocean's overturning circulation. However, there remains incomplete understanding how the shape of the seafloor can impact the overturning circulation and the carbon transported by it. Recent theoretical work has suggested that there will be upwelling hotspots downstream of undersea ridges, evidenced by localized carbon outgassing hotspots in models and mapped-data products. However, there has been no direct evidence of localized outgassing near bathymetric features so far. This project will use novel analyses that combine ocean dynamics with corresponding biological and chemical oceanographic observations to more completely account for outgassing and better constrain the global carbon budget, of which the Southern Ocean plays a large role. In addition, a better understanding of the three-dimensional nature of the Southern Ocean's overturning circulation will help improve predictions of ocean transport, which affects large-scale weather patterns, the yield of fisheries and the melting of the polar ice caps. This work provides broader impacts in two categories, supporting junior scientists through workforce development and promoting research infrastructure by developing new open numerical code that can be used by many research groups. This project proposes to investigate the patterns of carbon outgassing in the Southern Ocean and its connection to the three-dimensional meridional overturning pathways using the Biogeochemical Southern Ocean State Estimate (B-SOSE), a model that assimilates most available observations with the model physics in a self-consistent and conserving manner. The project will first construct an Eulerian isopycnal carbon budget to investigate local carbon outgassing. Then they will use a complementary Lagrangian analysis of the thickness-weighted velocities mapping the three-dimensional pathways of the overturning circulation, critical for interpreting the carbon budget. Finally, the project compare B-SOSE model output and wave glider and autonomous float data at the Southwest Indian Ridge using a suite of ‘virtual’ deployments to understand why outgassing hotspots have not yet been seen in float data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

In this project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Daniel E. Falvey of the Department of Chemistry and Biochemistry at the University of Maryland, College Park will examine unconventionally bonded molecular ions that contain electron-deficient nitrogen atoms. These species, which include nitrenium ions and several related structures, have been shown to have interesting electronic and magnetic properties that could potentially be harnessed for important applications such as advanced energy storage solutions, quantum computing, novel digital memory devices, optoelectronic sensors, etc. However, the few examples that have been carefully examined are unstable, often existing for only fractions of a second, inhibiting any technological advances. An important goal of the project will be to learn what types of modifications to the molecular structures of these ions will stabilize them yet still preserve the desirable electronic and magnetic properties. The set of structures that prove to be less stable will be studied for a different set of applications. In this case their high reactivity can be used to chemically modify proteins in a way that will convert them into therapeutic or bioimaging agents. The undergraduate and graduate students that participate in this project will receive training and experience in computer modeling of molecular properties and reactions, organic synthesis, and advanced spectroscopic techniques for chemical analysis. Planned studies will focus on two families of electron-deficient nitrogen-based intermediates: nitrenium ions, which are characterized by an di-coordinate nitrogen atom that bears a formal positive charge, and azapyramidinium ions, which are non-classical cations that feature a square pyramidal array of atom, including one nitrogen atom. Several decades of experimental and theoretical studies of nitrenium ions have focused almost exclusively on the arylnitrenium ions–species where the electron deficient nitrogen is substituted with one or two carbocyclic aromatic rings. The latter have been shown to have very small singlet-triplet energy gaps. A recent experimental study demonstrated that appropriately substituted phenylnitrenium ions have a triplet ground state. However there has been little experimental exploration of heteroarylnitrenium ions– related structures where the electron deficient nitrogen is part of a (formally) aromatic ring and/or the electron deficient nitrogen has a heteroaromatic substituent. Preliminary calculations imply that the former have either triplet ground states or small singlet-triplet energy gaps. The latter appear to show singlet-triplet energy gaps that can be manipulated through simple acid-base chemistry. A combination of calculations, organic synthesis, laser flash photolysis experiments, and low-temperature EPR spectroscopy will be used to examine these species. There is very little information on azapyramidinium ions. These species have been predicted to exist by high-level calculations but have not been characterized experimentally. A recent computational study by Professor Falvey provides guidance that should lead to the first successful synthesis and characterization of such a species. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Saltwater intrusion is an often-invisible process that is challenging to identify until it has already caused substantial harm to coastal lands. Salty waters seep inland – above and below ground – salinizing soils and waters, devastating crop harvests, and burning forests from the inside out. In the low-lying Mid-Atlantic region, large areas of coastal farmland and forest have converted to marsh, causing substantial economic losses and damage to ecosystems. To address these pressing challenges, an assembled coalition of farmers, landowners, researchers, government, non-profits, and the private sector will work together to develop, evaluate, and implement science-based solutions, focused on two important coastal economic sectors: farming and forestry. By developing and implementing a portfolio of practical solutions, such as novel agricultural easements, web applications to map saltwater intrusion, market development for salt-tolerant crops, and alternative timber harvest strategies, the project will improve the resilience and well-being of rural coastal communities impacted by saltwater intrusion, now and in the future. Thus, the project will translate research into practical solutions to promote regional resilience through community-engaged team science. The project goal is to improve regional resilience across rural coastal lands affected by saltwater intrusion by extending the life of farms and forest tracts, reducing storm surge damage and revenue losses, and supporting regional terrestrial and aquatic biodiversity. We will achieve this by developing and implementing coordinated, community-engaged solutions, focusing on agricultural and forest lands in Maryland, Delaware and New Jersey. To co-develop solutions, the project will bring together leaders from academia, government (local to federal), non-profits, and the private sector—who often have worked to face these challenges in isolation. Building on recent advances in earth system science at the land-sea interface, knowledge of the region’s complex hydrological, ecological, geomorphological, biogeochemical, and human systems will be synthesized to develop and evaluate a portfolio of social, technological, and nature-based resilience strategies. Selection of solutions will be informed by both research and community input and assessed for feasibility, risk, cost, and benefit through approaches such as techno-economic analysis. A Guide to Coastal Resilience will be developed that details the coalition’s shared vision of resilience and coordinated implementation solutions and will be disseminated broadly to guide policy, investment, and advocacy. This coordinated effort will bridge the gap between basic science and practical, community-aligned resilience strategies to meet the region’s evolving challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Non-Technical Abstract: Spin-triplet superconductivity is an exotic quantum phenomenon with potential for future quantum computing applications. This type of superconductivity is extremely rare in nature, so understanding its basic properties is of fundamental interest. In uranium ditelluride, this form of superconductivity has been identified; it has been found to survive up to extremely large magnetic fields and to exhibit other poorly understood effects, such as an angular halo shape. This project is an experimental investigation of the unusual superconductivity and the related electronic configurations that exist in high magnetic fields in uranium ditelluride. The research advances fundamental physical understanding of strong electron interactions and the resulting emergent quantum phases, including the stability of spin-triplet superconductivity and possibly topological superconductivity. This research program trains a graduate student, contributes to the training of junior researchers in quantum materials synthesis and measurement, and utilizes national scientific user facilities to perform specialized experiments. The program contributes to public outreach activities and educational opportunities for undergraduate and graduate students. Technical Abstract: Uranium ditelluride is a quantum material that hosts novel spin-triplet - possibly topologically nontrivial - superconductivity, which is of interest for possible quantum computing applications. This compound hosts several superconducting phases that are stable at high magnetic fields and exhibit unconventional behavior. This project focuses on studying the properties of the high-magnetic-field ordered phases, with the goal of improving understanding of the fundamental interactions that lead to the unusual superconducting states found in this material. The team synthesizes crystals of uranium ditelluride and performs a range of high magnetic field measurements, including electrical resistivity, contactless conductivity, magnetometry, and calorimetry. These measurements characterize the dependence of the superconductivity and other ordered phases on temperature, magnetic field, pressure, as well as other variables. The project advances understanding of strong electron correlations involving localized electrons, emergent states in quantum materials, and the properties of spin-triplet and topological superconductivity. A further benefit is the improvement of measurement techniques at high magnetic fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Advancing the semiconductor industry is a national priority, but a major skilled workforce gap is projected in the coming decade. Closing this gap requires early, proactive engagement in STEM education. This project directly addresses this need by cultivating STEM interest and skills in younger learners to prepare them for careers in this critical industry. This project will advance scientific progress and workforce development by introducing middle school students to foundational semiconductor concepts and manufacturing processes. Through hands-on experiences in community-based makerspaces, the project will directly empower approximately 110 students, 20 educators, and 16 family members across two states to develop an interest in science, engineering, and technology. Using tangible toolkits and immersive mixed reality (MR) environments, students will learn about transistors, logic gates, and integrated circuits in ways that make abstract concepts more tangible and accessible. Partnerships with organizations such as the Rockville Science Center, WestGate Academy, and the KID Museum will further support outreach and implementation efforts, helping to expand STEM education opportunities for all Americans. Co-design with families, educators, and industry partners will ensure the curriculum is relevant and responsive to the needs of the community. Research findings and curriculum resources will be made publicly available to promote wide adoption and lasting impact. The project will follow a three-phase research and development model. In Phase I, the team will conduct co-design workshops with students, families, educators, and experts to identify learning needs and develop early prototypes. Phase II will focus on developing and piloting tangible kits and AR/VR applications through iterative workshops. In Phase III, the refined workshops will be implemented in makerspaces, and the research team will evaluate outcomes related to students’ understanding of semiconductors, development of STEM identity, and interest in related careers. Guided by a design-based implementation research framework, the study will investigate how hands-on, community-based learning supports students' conceptual understanding, fosters a sense of belonging, and strengthens STEM identity, particularly through family involvement. Data collection will include pre-/post-knowledge assessments, attitudinal surveys, learning artifacts, and interviews. Mixed methods analysis, including ANOVAs and thematic analysis, will be used to evaluate the impact. This research will contribute new knowledge on how embodied, informal learning can support broader participation in advanced STEM fields and help close the gap in the semiconductor workforce pipeline. This project is co-funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This project is also co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The High-Altitude Water Cherenkov Observatory (HAWC) in Sierra Negra, Mexico, is a US led project that studies the sources of the most energetic light in the Universe. Known as gamma-rays, this light is emitted by some of the most extreme energy objects and environments in the universe. The detection and study of high energy gamma rays led to many exciting and important discoveries about the origin of the highest-energy light in the Universe and what that can tell us about the nature of the cosmos. This award supports the operation and maintenance of the central data archive for HAWC that is housed at the University of Maryland. It also funds the critical engineering, technical, and operational support for the remote operations of the HAWC observatory. Since 2015, HAWC has been operating continuously, both day and night, viewing 2/3 of the sky every day. It uses giant water tanks to detect these cosmic signals by capturing the showers of particles created when high-energy gamma rays interact with molecules in the Earth’s atmosphere. Every day, HAWC generates 2 Terabytes of new data, which is stored and analyzed by scientists through the US data archival facility at UMD. Through this work, we train students and postdoctoral researchers in the use of large-scale data systems, cutting-edge big data analysis techniques, and data-mining techniques, critical skills for support of the U.S. high-tech industry. This investment helps maintain US leadership in a field of high-energy astrophysics which we helped pioneer. This grant supports the operation and maintenance of the 13 PB U.S. data archive for the High-Altitude Water Cherenkov (HAWC) Observatory, housed at the University of Maryland. It also funds critical engineering, technical, and operational support for the HAWC Observatory itself, located at 4,100 m above sea level on the side of the Sierra Negra volcano in Mexico but operated remotely from the U.S. The HAWC Observatory, the construction of which was funded by the NSF, DOE, and CONACyT (the Mexican science agency), is a wide-field, continuously operating experiment designed to detect extensive air showers initiated by TeV gamma rays and cosmic rays. Since beginning full operations in 2015, HAWC has maintained a near-continuous duty cycle, triggering at ~24 kHz and generating approximately 2 TB of raw data daily. The observatory has identified over 80 very-high-energy gamma-ray sources, including previously undetected classes such as TeV halos, microquasars, and star-forming regions. The University of Maryland hosts and operates the U.S. data and computing center for the collaboration and plays a central role in operations, monitoring, and data stewardship. Recent advances in event reconstruction and data analysis techniques have enabled the reprocessing of the past ten years of data with improved sensitivity and resolution, leading to new discoveries and enhanced source characterization. Through this work, we also train students and postdoctoral researchers in the use of large-scale data systems and cutting-edge analysis and data-mining techniques, critical skills for support of the U.S. tech industry. This investment helps maintain U.S. leadership in a field of high-energy astrophysics which we helped pioneer. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemistry of Life Processes program in the Division of Chemistry, Ling Hao from George Washington University is developing novel bioanalytical chemistry methods to study mitochondrial dynamics in human neurons. Mitochondria are the powerhouses of the cell; they produce ATP and function as hubs for many metabolic pathways. Neurons in the brain rely heavily on mitochondrial functions because have extremely high energy demands to support synaptic activities. However, the specific molecular microenvironment of neuronal mitochondria is not fully understood, partly due to technological limitations. This project will develop mass spectrometry (MS)-based techniques to characterize the dynamic mitochondrial activities and interactions at the molecular level in human, stem cell-derived neurons. Through partnership with local chemistry instrument companies and MS discussion group, a summer MS workshop will be organized to bring together students from different levels as well as scientists from local academic, industry, and government institutions. A set of research, education and outreach activities will be integrated to enhance student learning and training experiences at the university and regional levels and to promote accessibility and diversity in STEM (science, technology, engineering and mathematics). The proposed research aims to address major technical challenges and knowledge gaps to study molecular networks and the subcellular microenvironment in human neurons using mass spectrometry (MS) techniques. Despite the advancements in understanding mitochondrial functions, it is technically challenging to capture the dynamic mitochondrial microenvironment, particularly in neurons with highly polarized structures. A set of MS-based proximity labeling strategies will be developed to study mitochondrial activities, functions, metabolic regulations, and membrane dynamics in human induced pluripotent stem cells (iPSCs)-derived neurons. This research program will provide widely applicable chemistry tools to study sub-cellular biological microenvironment as well as provide new insights into mitochondrial dynamics and neurobiology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Near-inertial waves (NIWs) contain a significant fraction of the energy in the ocean internal wave field and form a prominent signal in near-surface ocean currents. Generated at the ocean surface by wind, their high vertical shear can modify the depth of the surface mixed-layer—a key constraint on the coupled large-scale ocean-atmosphere system—and they provide a pathway for transporting kinetic energy out of the surface ocean into the interior, where they are believed to play an important role in the maintenance of the abyssal circulation through mixing. Recent work has highlighted the sensitivity of NIWs to coupled air-sea interactions at the mesoscale and submesoscale, showing for instance dramatic reductions of near-inertial kinetic energy when surface currents are included in the calculation of the air-sea flux of momentum. However, fundamental gaps in understanding and quantification of the coupled processes remain—both in terms of the relevant physical mechanisms and in terms of the impact on ocean dynamics and energetics. The objective of this project is to use high-resolution coupled ocean-atmosphere models (both realistic and idealized), along with analysis of recent observations, to determine how coupled air-sea interactions modify the lifecycle of NIWs—from generation at the ocean surface to dissipation in the interior. In addition, outreach materials will be developed to address a current gap in science communication materials on NIWs for the general public. This will include development of an outreach video made available online and via a program that provides materials to museums and education centers across the country, and the development of a rotating table demonstration for use in teaching and outreach. The majority of the project budget will be used to support early-career scientists, including a postdoctoral researcher and a PhD student, both of whom will receive training on geophysical fluid dynamics and coupled ocean-atmosphere modeling. Finally, an undergraduate student will also receive training in physical oceanography and science communication during a summer project helping to design and implement the above-mentioned outreach materials. This research aims to improve our understanding of the coupled ocean-atmosphere system, with a specific focus on the generation and lifecycle of NIWs—a topic which touches aspects of oceanography ranging from biogeochemistry in the surface ocean to the slow abyssal overturning circulation. It is particularly timely given broader community efforts towards improved observations of near-surface currents and winds, where near-inertial variability is identified as a key target. Preliminary results highlight the need for improvement in both the conceptual understanding, and quantitative estimates, of coupled air-sea interaction effects on NIWs. For example, sea-surface temperature variability modifies surface winds, which is shown to provide a significant, previously unaccounted for, source of kinetic energy to NIWs. Likewise, coupled air-sea interactions introduce mesoscale and submesoscale variability into the surface wind-stress which in turn imprints on the spatial scales of NIWs. This leads to regions of strong horizontal divergence that generates inertial pumping and enhances the flux of kinetic energy into the interior. This project will therefore develop a more complete mechanistic understanding of how coupled air-sea interaction processes modify NIWs—extending classic conceptual models of air-sea interaction developed considering only the low-frequency balanced flow—while also quantifying the impact on both mixed-layer dynamics and interior mixing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The IceCube Neutrino Observatory consists of a cubic kilometer of deep, clear glacial ice, embedded with specialized cameras. Located in Antarctica near the South Pole, IceCube has transformed the South Polar ice cap into a most unusual telescope; instead of sensing light, it makes an image of the sky using a subatomic particle called a neutrino. Neutrinos can travel through light years of steel without stopping, allowing us to peer into the dense cores of some of the most energetic objects in the universe, areas from which light cannot escape. Their presence gives us insight into the forces that power stars and galaxies. Since it was built, IceCube, the first telescope of its kind, has made a number of exciting discoveries. We have observed the first neutrinos from outside our galaxy and the first high energy neutrinos from our own Milky Way galaxy. We even have the ability to probe some of the secrets of neutrinos themselves, which could change our understanding of one of the most fundamental building blocks of our universe. IceCube scientists are always eager to share this knowledge with the general public through a number of activities, from school programs to reach young people, to radio and television productions to engage adult audiences. This grant allows us to continue to analyze these images, make new discoveries, and maintain the leadership of the United States in this new and exciting field of science. This grant provides funding for scientists at 12 U.S. institutions to analyze the rich dataset from the IceCube Neutrino Observatory through August 2026. The IceCube detector, completed in 2011, operates continuously with 99% uptime and collects neutrino data from GeV to PeV energy scale, from all directions in the sky, enabling an incredibly wide array of analyses ranging from the fundamental physics of neutrino oscillations to the detection of neutrinos from the most energetic environments in the universe. Landmark discoveries by IceCube include the first detection of a high energy diffuse flux of cosmic neutrinos, the multi-messenger observation of neutrinos in coincidence with gamma-ray emission from the blazar TXS 0506+056, observation of neutrinos from the active galaxy NGC 1068 and the first identification of a cosmic anti-neutrino through the Glashow resonance. IceCube’s unbroken track record of breakthroughs in neutrino astronomy has continued recently with the machine-learning-enabled detection of neutrinos from the Galactic Plane and the unambiguous detection of cosmic tau neutrinos. IceCube data is beginning to illuminate the high-energy Universe, pointing toward obscured gamma-ray sources as the most significant neutrino emitters. IceCube is now entering a new era with the forthcoming construction of the IceCube Upgrade (NSF grant #1719277), scheduled for the 2025-2026 austral season, the first major addition of hardware to the detector in 15 years. This proposal will fund the participation of U.S. institutions in data analysis. It will enable the characterization of the diffuse flux, the further identification of neutrino sources both with IceCube data and with multi-messenger studies, extending neutrino astronomy higher and lower in energy, monitoring the Galaxy for supernova explosions, exploring the fundamental properties of neutrinos, and searching for signals of physics beyond the standard model. We will also continue running the Name That Neutrino citizen science project, our successful IceCube Masterclass, and outreach programs that connect IceCube scientists with school classrooms and the general public. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Fungal pathogens pose a significant threat to crop plants and consequently, food safety and security. Previously, several years of rigorous testing of a chemically mutagenized population in field and greenhouse conditions identified two unique variations in an elite wheat variety to provide resistance to Fusarium graminearum, the causal pathogen of Fusarium Head Blight (FHB) disease. This project will identify and validate the genomic variations responsible for resistance to the fungal pathogen. To corroborate findings, the project will recreate the variations in another wheat variety using gene editing and study the effects on plant performance. The long-term goal of the project is to investigate and implement novel resistance strategies in plants to combat challenging fungal pathogens that utilize a variety of pathogenic lifestyles. To broaden the impact of the work, the project team will share seeds of the resistant wheat lines developed in the project with interested breeders and geneticists for field testing and potential deployment in crop varieties. The project will train high school students, undergraduate students, PhD students, and postdoctoral scientists with the next generation of tools and technologies in plant biotechnology research and their application to ensure food security. This project builds upon a multi-year, rigorous genome-wide screen in wheat to select independent mutations conferring resistance against the hemibiotrophic fungal pathogen Fusarium graminearum. The specific aims of this project are: 1) Validation and assessment of FHB resistance mutations on chromosome 2D, and 2) Fine-mapping and validation of the causal mutation for resistance on chromosome 7A. Mutation mapping populations were developed and phenotyped, followed by whole genome sequencing of the resistant bulks to map and shortlist independent candidate mutations on the short arms of chromosomes 2D and 7A. Multiplex editing will be done to validate the mutations underlying the resistant phenotype. The confirmed mutation will be further validated in another variety, and the fitness cost, if any, of the mutation on the plants will be determined by a thorough assessment of plant health and performance. The independent mutations identified in the project will provide new targets for engineering resistance against the difficult necrotrophic and hemibiotrophic pathogens in crop plants. The project team will share the seeds of edited wheat generated in the project with interested breeders and geneticists for their field testing and deployment. Research findings and knowledge about tools, technologies, and applications of plant science research will be disseminated to the general public. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Temperature can affect the severity and contagiousness of infectious diseases. For some diseases, warmer temperatures can increase severity, but for other diseases, especially those caused by fungal pathogens, high temperatures can lead to symptom reduction and improved health for the hosts. In such cases, warmer summers could bring a bit of climate ‘good news,’ releasing hosts populations from the burden of disease. However, the evolution of genetic changes in either host susceptibility to infection or pathogen sensitivity to heat have the potential to alter this outcome. This research will investigate the potential for such evolutionary change to buffer hosts and pathogens against rapidly changing seasonal temperatures using a combination of mathematical models and studies of a highly tractable model plant-disease system that naturally occurs across a large range of temperature variation. Results from this research will advance scientific knowledge of seasonally-dependent disease transmission, which is known to occur in humans, domesticated populations and wildlife, and lead to improved understanding of how evolutionary changes in hosts and pathogens feedback to affect risks posed by infectious diseases. Outcomes from this research could help improve temperature-based forecast models for disease spread, and will also inform management practices by identifying any unintended consequences of using heat to control disease symptoms and spread. This research will also result in the development of new educational material and opportunities for undergraduate students just entering studies in the life sciences. The research will investigate the central hypothesis that temperature-driven reductions in disease expression result in an evolutionary increase in host susceptibility by reducing selection for resistance. It will also explore the consequences of these host evolutionary shifts on the ability of disease to persist in habitats that would otherwise be too warm. To achieve this, new theoretical models will be developed that explore the impact of seasonal variation in temperature on host-pathogen evolutionary feedbacks for diseases that experience heat-induced curing. The team will also test the model predictions in a real-world disease system by quantifying susceptibility and transmission of the wild plant, Silene vulgaris, to its endemic fungal pathogen, Microbotryum silenes-inflatae, across an elevational gradient. This will determine whether populations at warmer, lower-elevation locations have increased susceptibility and that these shifts in susceptibility in turn impact the distribution and prevalence of disease. The research will also investigate whether pathogen populations are capable of evolving higher levels of heat tolerance, and exploring the impact of such evolution on disease transmission through a combination of models and field-based disease transmission experiments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Nontechnical: Semiconductor materials, devices, and computing must be optimally co-designed, with simultaneous consideration of elements across the technology chain. The benefits of co-design to advance semiconductor technology have been widely recognized in a variety of government and industry studies. A holistic, co-design approach can more rapidly create high-performance, robust, secure, compact, energy-efficient, and cost-effective solutions. Meeting Topics: Topic 1: Collaborative Research in Domain-Specific Computing Topic 2: Advanced Function and High Performance by Heterogeneous Integration Topic 3: New Materials for Energy Efficient, Enhanced-Performance and Sustainable Semiconductor-Based Systems Technical: The Future of Semiconductors (FuSe2) Awardees Workshop will foster communications among FuSe2 awardees, the industry partners (Intel, Micron Technologies, Samsung, and Ericsson), and NSF program officers through a combination of context setting presentations by the industry partners, presentations from awardees in the three FuSe2 topics, and unstructured discussions. This meeting will take place over 2 days on September 22 and 23, 2025, at the National Science Foundation headquarters. The format of the meeting will foster communications through a combination of context setting presentations by the industry partners, presentations from awardees in the three FuSe2 topics, and scheduled time for awardees to have unstructured discussions. The meeting will be co-chaired by Nadia El-Masry (NSF/ENG/EEC), Yaroslav Koshka (NSF/MPS/DMR), and X. Sharon Hu. About 75 participants will attend the meeting. Participants will be FuSe2 awardees, representatives of the FuSe2 industry partners, and NSF staff representing participating directorates, including the Semiconductor Working Group. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This award provides support to U.S. researchers participating in a project competitively selected by a 55- country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. Working together in this Collaborative Research Action, the partner agencies have provided support to foster global transdisciplinary research teams of natural, health and social scientists and stakeholders from across the globe to improve understanding of climate, environment and health pathways to protect and promote health. The projects will provide crucial new understanding into the health implications arising from the impacts of climate change and variability on; 1) decision-science approaches to adaptation and implementation, 2) food, environment, and biological security and 3) risks to ecosystems and populations. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries to increase our knowledge of the complex linkages and pathways between the climate, environment and health to help solve complex challenges that face societies. populations. The AWARE project seeks to expand on a previous Belmont Forum project that investigated the impact of extreme weather events on the burden of food- and water-borne illnesses and built a built a prototype early warning system using statistical methods. The research team will improve accuracy of the early warning system by incorporating more recent data, improve spatial resolution, and perform prospective evaluation. There is a pressing need to enhance public health adaption measures including preparedness, capability for rapid mobilization of public health resources, healthcare infrastructure resilience, and awareness training. Leveraging novel datasets and recent advances in modern machine learning, the project team will enhance the predictive power and geographic resolution of our prototype early warning system to forecast diarrheal disease burden at seasonal to sub-seasonal scales in the study area (India, Indonesia, Nepal, South Africa, Taiwan, Vietnam), and carry out a prospective evaluation of the early warning system to predict disease burden ahead of time. AWARE will leverage artificial intelligence to synthesize high dimensional data and develop early warning systems in partnership with local stakeholders to enhance resilience against health threats posed by extreme weather events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will examine the transfers of energy between spatial scales at oceanic fronts. The focus is to examine the effects of symmetric instabilities and interactions between the mesoscale and submesoscale. The investigators will employ high-resolution numerical modeling that can resolve both the submesoscale and the turbulence scale. The results are expected to lead to new understanding of oceanic frontal energetics and how to represent energy transfer processes in predictive models. This project focuses on two mechanisms that have been separately shown to influence the energetics of submesoscale fronts, but whose joint effects have not yet been investigated: mesoscale strain and symmetric instabilities. The goal is to examine how symmetric instabilities modify the forward cascade of energy in fronts in the presence of mesoscale strain. High-resolution, turbulence-resolving large-eddy simulations will be run with a novel configuration that permits representation of mesoscale strain, allowing for a more realistic representation of submesoscale fronts than previous investigations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Forensic peer review is the task of reviewing published studies to uncover errors or manipulation in data, analyses or theories. Forensic peer reviewers might identify problems that were undetected during traditional peer review processes. This project brings together reviewers, authors, research integrity officers, and platform designers to co-develop guidelines and practical tools that promote fair, constructive, and collaborative forensic peer review practices. The project brings together a wide range of stakeholders, including citizen scientists, journalists, and early-career researchers. The project combines digital ethnography, interviews, and participatory design to explore and improve the landscape of forensic peer review. In Phase One, the research team will document a set of forensic investigations and conduct interviews with forensic reviewers and authors to understand their values, needs, and experiences. In Phase Two, the project team will convene interdisciplinary workshops to co-create guidelines and prototype interventions, such as communication protocols, community moderation strategies, and author alert tools. During Phase Three, the team will pilot and evaluate these outputs in collaboration with existing and emerging review platforms. The project outcomes include research publications, public datasets, and actionable resources to guide forensic peer review. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The notion of a moduli space plays an important role in geometry and physics. It has also proved useful to certain applied fields such as robotics. Moduli are parameters describing the variation of a particular geometric or algebraic structure. The construction of a moduli space brings with it a deeper understanding of which geometric structures behave well in families, and the geometric analysis of the moduli space itself reveals invariant properties of the objects they parametrize. The current project seeks to extend the PIs previous work on certain moduli spaces that arise naturally from the gauge theory of elementary particles. The Yang-Mills equations, for example, are a major point of intersection between mathematics and theoretical physics. Moduli spaces of Higgs bundles have been used to study the space of representations of surface groups into complex Lie groups and their noncompact real forms. They appear in supersymmetric gauge theories and are also important in the Geometric Langlands problem. The research projects covered by this grant will further our understanding of the relationship between the geometric, analytic, and algebraic properties of moduli spaces. The specific goals of this project lie in several areas of complex geometry related to holomorphic bundles, gauge theory, and moduli problems. The first consists of problems stemming from previous work of the PI on moduli spaces of Higgs bundles on Riemann surfaces. These include the following subprojects: (1) Giving a gauge theoretic construction of a joint moduli space of Higgs bundles over varying Riemann surfaces; (2) further understanding the asymptotic structure of the moduli space and its topological properties. The latter is related to important conjectures concerning the geometry of the Hitchin moduli space, in part arising from supersymmetric gauge theories; (3) extending work on Chern-Simons line bundles to moduli spaces of parabolic Higgs bundles. In the second area of proposed research, the PI will study higher dimensional generalizations of the Yang-Mills equations and their relationship to the complex geometry of holomorphic bundles. This includes: (1) a study of the adjoint Seiberg-Witten equations on Kähler surfaces; (2) giving a gauge theoretic proof of the Bogomolov-Miyaoka-Yau inequality for projective surfaces of general type using monopoles; (3) studying the nature of Z/2 harmonic forms and spinors as they relate to asymptotics of coupled moduli spaces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

NON-TECHNICAL SUMMARY This research explores how to produce a new class of advanced metals that are both exceptionally strong yet very formable and ductile. In most materials, increasing strength typically makes them more brittle, while increasing ductility often reduces their strength. This tradeoff limits performance in critical structural applications. A special class of materials known as nanotwinned metals overcomes this limitation by reorganizing the atomic structure to form nanoscale layers that improve both strength and deformability. However, current manufacturing methods can only produce these beneficial structures in thin films or small-scale components. This project investigates two advanced manufacturing techniques designed to engineer nanotwinned structures within bulk materials while enabling their formation across larger volumes. The goal is to establish science-based protocols for the scalable production of nanotwinned metals and alloys. This project is actively seeking to unlock a new generation of high-performance structural materials for use in extreme environments. These outcomes have wide-reaching impacts in aerospace, energy systems, and advanced manufacturing, while also supporting the training of future engineers and scientists in cutting-edge materials technologies. This effort is contributing to the national workforce in advanced manufacturing and materials science. TECHNICAL SUMMARY This research investigates new strategies for synthesizing bulk nanotwinned metals – materials that combine high strength, ductility, and electrical conductivity through nanoscale twin boundaries. These interfaces enhance mechanical performance, thermal stability, and damage tolerance, making them desirable for extreme structural applications. Despite their promise, nanotwinned metals remain limited to thin films and small-scale components due to challenges in controlling twin formation during bulk processing. This project is establishing a fundamental understanding of how to intentionally and scalably produce prolific deformation twins in bulk metals and alloys. The central hypothesis is that suppressing competing microstructural mechanisms – such as dislocation cell formation in laser powder bed fusion (L-PBF) and recrystallization in additive friction stir deposition (AFSD) – can promote deformation twinning across large volumes of material. Specifically, thermal fatigue from rapid heating and cooling in L-PBF and intense shear deformation in AFSD offer unexplored but promising pathways for twin formation. To evaluate this hypothesis, the project develops and applies two cryogenically modified additive manufacturing techniques: (1) cryogenic L-PBF, where sub-ambient cooling increases solidification rates to disrupt dislocation substructures; and (2) cryogenic AFSD, a novel extension of solid-state processing that couples high strain rates with thermal control to limit recrystallization and enhance twinning. Low- and medium-stacking-fault energy metals, including Cu and Cu-Al alloys, serve as model systems for this investigation. A systematic experimental approach employs electron microscopy and nanoindentation to elucidate how processing conditions affect twin formation and local mechanical behavior. This early-stage work is establishing science-based protocols for engineering twinned structures in bulk form factors, seeking to expand their applicability beyond small-scale uses. In alignment with the EArly-concept Grants for Exploratory Research (EAGER) program, this project explores a high-risk, high-reward strategy while training a technically skilled workforce in advanced manufacturing and materials science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Understanding slender structures is one of the great unresolved challenges of modern science and technology. Such structures permeate biological systems (flowers, leaves, tissue, and active matter), materials science (programmable materials), robotics (deployable devices), and biomedical engineering (soft robotics). These problems are typically lower-dimensional and susceptible to geometric effects such as metric constraints, length and area constraints, and curvature. Corresponding models are thus governed by geometric partial differential equations (PDEs), which, much like nature, are nonlinear. Fabrication of slender materials is time-consuming, expensive, and often erratic, which makes the development of predictive computational tools of paramount importance in engineering and science. The numerical treatment of nonlinear geometric PDEs must cope with the dynamic deformation of geometries, the presence of strong nonlinearities, and the development of self-penetrating structures and topological changes. Central to this proposal is the essential role of liquid crystals (LCs) as key constituents in the fabrication and actuation of slender structures. Models of nematic LC films are used to describe morphogenesis (shape formation) and active matter. Prestrained plates and LC networks are used to comprehend the shapes of flowers and leaves as well as to design and actuate programmable materials. Moreover, approximating local and nonlocal geometric problems, governed by fully nonlinear PDEs and singular integro-differential operators, constitutes a formidable yet distinct computational challenge. This research program combines reduced-order modeling (using differential geometry), structure-preserving algorithms, and efficient computation, and is supplemented by analysis (asymptotics and G-convergence). This project consists of three intertwined thrusts involving modeling, analysis, and approximation of several nonlinear geometric PDEs and nonlocal equations. The research is suitable for student training in exciting, mathematically and computationally challenging, and practically relevant areas of contemporary research, and is conducted together with former students, postdocs, and collaborators, who visit regularly. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The mathematical theory of dynamical systems aims to describe the behavior of systems that evolve with time, such as the motion of the planets or of gas particles in the air. The modern theory has accumulated a corpus of concepts and tools that are fundamental in understanding classical mechanics and other problems from mathematical physics. One of its central topics is the stability (and instability) study of conservative systems. The principal investigator (PI) intends to push this study from various points of view including topological stability, measure-theoretical or statistical stability, effective or long finite time stability, as well as rigidity under perturbations. This project will also provide opportunities for graduate student research training. One of the main objectives of the project is to push the stability and rigidity results of KAM theory (after Kolmogorov Arnold and Moser) in various new directions, namely when one or several hypotheses of the classical KAM theory are not verified such as absence of any transversality condition, lack of regularity, combination with hyperbolic or parabolic dynamics, global (non-perturbative) results, presence of noise or presence of dissipative terms, large or infinite dimension. The project also encloses several themes related to diffusion and instability in conservative dynamics. Resonances and small divisors problems are central to the stability study and provide a unifying aspect in the following topics, to be investigated during the project's span: • Transfer of energy in Hamiltonian systems with excitation and partial damping. • Lyapunov instability of elliptic equilibria for real analytic Hamiltonians. • KAM stability without any transversality condition. • Stability in non-smooth Hamiltonian systems. • KAM for dissipative systems. • Stability in Hamiltonian systems in large and infinite number of degrees of freedom. • Linearization beyond the Diophantine condition. • Local rigidity of affine higher rank actions on the torus. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Effective seeking of information is a crucial aspect of daily life that relies heavily on language skills. In societies with a diverse population, many people are not proficient in the majority language. People with less majority-language proficiency face challenges communicating their needs in daily activities such as medical consultations, discussing workplace benefits with employers, exploring housing, and others. A typical solutions pairs these individuals with professional interpreters or bilingual volunteers. Such support is often expensive and unsustainable. To fill the gap, this project will develop innovative tools based on AI Large Language Models (LLMs) to help people advance their language abilities for effective information seeking. Users without technical expertise in computers or AI will be guided to design their own personalized tutoring systems. The resulting digital tutor will assist its user in creating learning plans, practicing strategies, and tracking their progress in advancing their language proficiency. The study will generate rich data, metrics, and benchmarks for language learning for real-life information-seeking practices. The project will advance the science of human-computer interaction, language science and education, and natural language processing. The process of designing and using their own digital tutor will increase user's understanding of AI (AI literacy), and both the power and limitations of LLMs. The tutoring systems will assist users in navigating various information-seeking scenarios, ultimately improving the person's language proficiency and quality of life. This project provides a novel approach to tackling the challenges faced by people with less majority-language proficiency. They will go through a process wherein they a) design, utilize, and refine an AI-enabled tutoring system that guides their language use for situational purposes, and b) advance their language proficiency along a learning path tailored by themselves to their needs. The experience will stimulate the individual's critical thinking regarding actions to take at the different stages of their language learning. Using the system provides the individual with feedback to adjust their learning to improve outcomes. There are four sets of research activities involved in the lifecycle of this project. Activity 1 employs experience sampling to pinpoint individuals' current practices and challenges in language use for daily information seeking. It help pinpoint the user's essential needs in fine-grained manner. Activity 2 compiles a bank of system building blocks, setting the stage for each individual-as-designer's assembly and customization of their tutoring system. Activity 3 engages each individual-as-learner-and-designer in crafting, using, and refining their tutoring system. It will generate longitudinal data to track the progress of a person's language use in daily information seeking, as well as their coordination with the tutoring system. Activity 4 initiates an open data program to uncover value trade-offs in interactions between participants and large language models. It will enable the exploration of emerging ways to align model-generated content with individuals' core values. Together, these efforts will generate essential knowledge, novel methods and datasets, functioning tool-kits, and educational materials for enhancing language minorities' information seeking through effective language use. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Non-Technical Abstract: The Fundamentals of Quantum Materials (FQM) Winter School and Workshop is a nationally recognized activity that provides essential education and training in the rapidly growing field of quantum materials. Hosted by the University of Maryland since 2017, this annual event brings together undergraduate and graduate students, postdoctoral researchers, and leading scientists for an immersive experience in both theory and hands-on practices. The event aims to develop the next generation of quantum science researchers by combining morning lectures on pedagogical background, lab-based hands-on training sessions, and networking opportunities to address topics at the forefront of current research into quantum materials. The program fosters connections across disciplines—spanning physics, chemistry, and materials science—and strengthens the national research infrastructure in quantum materials. Its strong emphasis on mentorship and public engagement plays a vital role in maintaining U.S. leadership in quantum research and workforce development. Technical Abstract: This activity developed out of a mandate to educate junior quantum scientists in the synthesis, characterization, and theoretical modeling of quantum materials. The annual program involves a week-long school focused on providing foundational training in bulk single-crystal and thin-film materials synthesis, characterization and fundamental exploration techniques based on a focused theme that ties to current thrusts in the quantum materials community - including topics ranging from superconductivity to magnetism to correlated electron physics. Pedagogical lectures on topics ranging from basic crystal growth techniques to more specialized approaches are provided by ten or more of the nation's top practicing quantum materials scientists, and laboratory sessions are organized and conducted at the University of Maryland’s Quantum Materials Center, which offers participants direct exposure to cutting-edge experimental tools and techniques. The structure of the school includes mornings of lectures and tutorials, with afternoons devoted to practical demonstrations in laboratories in the Quantum Material Center. The school also includes a visit to a local scientific facility, a poster session attended by senior scientists, and a one-day workshop where all school attendees participate in top-level research presentations by experts in the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Non-Technical Summary As laboratory tools become increasingly automated and machine learning (ML) continues to transform scientific research, a new challenge has emerged: how can ML agents control and coordinate multiple instruments at different labs, share information between them, and make meaningful decisions to accelerate discovery? This project tackles that challenge by developing strategies for building smart experimental systems that allow different tools - such as microscopes, structural characterization, and synthesis instruments - to work together autonomously, and develop metrics that allow evaluation of the return on the investment. These systems are designed to not just automate tasks, but also to “learn” which experiments to run next based on previous results, optimizing both speed and insight. The research is focused on discovering new materials for energy storage and information technologies, such as batteries and next-generation electronics, where even small improvements in materials can have large technological and economic impacts. In addition to scientific breakthroughs, the project shares tools and training with students and researchers from a broad range of institutions, helping to build an innovation-driven workforce for deep tech industries and manufacturing. In doing so, this work supports NSF’s mission to promote the progress of science, support national prosperity and security, and prepare a skilled workforce. Technical Summary This research develops an autonomous experimental framework for materials discovery based on multi-instrument coordination, active learning, and reward-driven optimization. The central objective is to create machine learning agents that can operate multiple experimental tools - such as scanning probe microscopes, structural probes, and synthesis platforms - in parallel, sharing information and prioritizing experiments in real time. These systems are applied to the exploration of combinatorial materials libraries, particularly targeting ferroelectric and electrochemical functionalities relevant to energy storage and electronics. The proposed methods include the use of structured and deep Gaussian Processes, causal learning, and symbolic regression, all within a framework of reward function design - where experimental strategies are guided not only by accuracy but by their expected contribution to downstream functionality. In addition to combinatorial libraries, the same strategies can be applied to multiple identical synthesis tools exploring the same parameter space, enabling high-throughput autonomous experimentation across facilities. Emphasis is placed on multi-objective and multi-fidelity optimization, where low-cost proxy measurements are combined with high-resolution tools, and on building decision-making logic that can generalize concurrent decisions across dissimilar experimental platforms. The project contributes to reproducible AI-driven experimentation and provides shared tools and training to advance materials research infrastructure, aligning with NSF goals in innovation and national competitiveness. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.