University of California-Davis

NSF Awards · FY 2025 · 2025-09

Rapid progress in Artificial Intelligence (AI) makes it possible to unify images, text, and sensor readings inside powerful multi-modal large language models (MLLMs). Yet researchers in areas such as food science struggle to identify which open-source models to trust and how to deploy them safely in high-stakes settings such as pathogen detection and nutrient-delivery design. This project establishes a secure, easy-to-use ecosystem that (i) profiles MLLMs on their effectiveness, robustness, efficiency, and security, (ii) recommends the best candidates for a given scientific task, and (iii) embeds automated safeguards so discoveries remain reproducible and trustworthy. By lowering the expertise barrier and hardening model behavior, the work enables scientists, educators, and regulators to harness cutting-edge AI while protecting public health and accelerating innovation. This project advances the reliability and usability of foundational AI models such as MLLMs for the scientific ecosystem by addressing key challenges in model selection, threat discovery and safeguard, as well as food science research applications. It introduces principled methods for profiling and recommendation that encodes both scientific tasks and candidate MLLMs into a shared space and ranks models that best meet domain objectives, enabling rigorous and task-relevant model recommendation. The project also pioneers a novel evolutionary red teaming and guardrail framework to systematically identify and mitigate critical food safety issues in the scientific models' operation. By applying these methods to real-world problems in food science—such as microbial detection from images and micro-nutrient formulation, the project establishes a concrete pathway for AI to support scientific reasoning in high-stakes, data-intensive domains. The resulting frameworks and technologies are broadly applicable, offering reusable methodologies that will benefit a wide range of scientific fields, including agriculture, biomedical discovery, and environmental monitoring, fostering broader participation and more reliable AI-driven breakthroughs. 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

Given the national priority for America's leadership in science, there is a need to strengthen the quality of teaching and learning in science classrooms. This conference brings together researchers, practitioners, curriculum developers, and policymakers to chart the future of curriculum-based professional development (CPBL) in science education. CBPL is an approach that uses high-quality curricular materials as a catalyst for teacher learning. Historically, efforts to improve classroom learning outcomes have focused on high-quality curricular materials--written to support students for learning beyond rote recall to fundamental understandings. These new materials have been designed so that their use would lead to shifts in teacher instruction. Because the scope, sequence and teaching strategies in these materials are research-based, these materials represent a key leverage point for translating research to practice. Presently, the field is not clear about how teachers learn from these well-designed materials and what other supports might be necessary. The need to understand how teachers learn from them is made more poignant by the advent of open source, because several new high-quality curricula in science are made freely available and come without traditional professional development support. The conference aims to address pressing questions about how high-quality materials can drive teacher learning, how materials should be designed to support teacher learning trajectories, how CBPL can promote high quality science education, and what organizational supports are needed for successful implementation. Through structured collaboration among stakeholders, this gathering will consolidate existing work and generate concrete plans for advancing both research and practice in ways that honor teacher professionalism while supporting student learning in science. The conference employs a four-phase structure to maximize its impact on the field. In Phase 1, commissioned white papers from leading scholars map the theoretical terrain of CBPL and identify critical areas for advancement. Phase 2 involves careful participant selection and meeting design to ensure productive engagement across perspectives. During Phase 3, the conference engages approximately 55 participants in analyzing current practices, identifying shared commitments and assumptions, conducting gap analyses, and developing action plans for moving the field forward. Activities alternate between whole-group sessions examining white paper themes and small-group work focused on specific dimensions of CBPL. Phase 4 focuses on dissemination through a special journal issue that combines the commissioned papers with additional manuscripts emerging from the conference. Throughout all phases, the work is guided by an experienced planning committee and advisory board representing key stakeholder groups in science education. This systematic approach is supporting scholarship that advances understanding of how CBPL can support teacher learning and transformation of science education practice. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. 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 Dean Tantillo of the Department of Chemistry at the University of California, Davis is using modern computational chemistry methods to explore the limits of reactivity principles for organic reactions of fundamental and synthetic interest. The overall goal is to provide fundamental understanding of factors that control reactivity, including non-statistical dynamic effects, at a level useful for predicting laboratory reaction outcomes. In addition to the fundamental importance of the mechanistic models uncovered through this research, the project will be used to train students in multidisciplinary approaches to mechanistic chemistry and expose them to careers that employ such techniques. Educational resources will be developed and widely distributed. This research will advance knowledge in mechanistic organic chemistry through the construction of new mechanistic models, including those based on non-statistical dynamic effects that allow for the control of selectivity in the face of post-transition state bifurcations, and those that predict the feasibility of both dyotropic reactions with transition metal-based migrating groups and homopericyclic reactions. The principles uncovered will likely transform how organic chemists predict mechanisms and product selectivity for reactions of structurally complex organic and organometallic molecules. 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

Forest fires scorch trees, shrubs, and the soil surface, but what are their impacts on deeper soils and the microbes that live there? Tree roots can grow as deep as 5 meters (16 ft) or more, interacting with diverse bacteria and fungi in soils, and potentially controlling how much, and how quickly, carbon moves from the atmosphere to the soil and back. When trees die suddenly in a high-intensity fire, their roots are left behind to rot. This changes which microbes are present, how they interact, and how carbon moves through the soil. In this study, researchers will sample the entire rooting depth of both living and fire-killed ponderosa pine trees to understand how microbial communities change when a tree dies—and how those changes affect how much carbon the soil holds, and for how long. Using these data, the team will build models to predict how fire alters soil processes at the scale of forested landscapes and ecosystems, and how changes to the frequency and size of forest fires could affect these soils in the future. This project will support the next generation of scientists, providing opportunities for a graduate student and a postdoctoral researcher to lead field, laboratory, and modeling work. Undergraduate students will receive hands-on training in processing soil samples and analyzing data through research internships. The PI will also design a first-year seminar focused on microbial ecology, creating a Course-based Undergraduate Research Experience to help students build skills and confidence in science. This project will develop an eco-evolutionary understanding of how microbial traits affect carbon cycling, particularly in deep soil horizons, and how their interactions with tree roots may shift the functional traits of entire microbial communities as stand-replacing fire becomes more frequent. Working at an instrumented study site in the southern Sierra Nevada, the Soaproot Saddle National Ecological Observatory Network site, researchers will use a Geoprobe direct push sampler to extract intact soil samples from soil surface to hard bedrock. These samples will be subjected to soil physical and chemical analyses, metagenomics, metatranscriptomics, organic carbon fractionation, and radiocarbon analysis of those carbon fractions to test three central hypotheses: 1) That deep roots of living trees significantly enrich surrounding soil microbial communities, increasing microbial activity at depth, and that these plant-microbe interactions result in younger pools of soil organic carbon directly below living trees; 2) that sudden widespread fire mortality causes turnover and loss of function in the deep soil microbial community, with functional consequences for carbon cycling, and 3) these processes vary seasonally, requiring multiple timepoints to measure. Finally, this project will apply these links between roots, microbial traits, and carbon pools to build predictive models that mechanistically incorporate microbial traits and their seasonal variation to understand landscape- and ecosystem-scale carbon cycling in forests subject to fire. By focusing on understudied deep soil systems, this project will construct trait-based models of microbial contributions to carbon cycling that will be helpful to forest managers and policymakers. 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 SUMMARY Many technologies, including sensors, electronics, and energy systems, depend on the ability to control how material behavior is affected with changes in temperature and pressure. This project, supported by the Solid State and Materials Chemistry Program in the Division of Materials Research, enables Prof. Koski and her research group at the University of California Davis to grow new layered materials, use chemistry strategies to add and remove atoms inside of these new materials (a process called “intercalation”), and to explore how to tailor these unique materials to control the behavior at high pressure and low and high temperatures. New methods for chemical intercalation are developed that will transform our abilities to manipulate thermodynamics on a molecular level. These investigations address an outstanding need of using chemistry to control 2D layered materials to access new phases and functional behavior. A method called “Brillouin spectroscopy” can study the mechanical behavior of tiny volumes of material without damaging them; the researchers use this to precisely measure new phase changes and extract physical information about the materials. This project is uniquely situated to achieve the goal of controlling thermodynamics in a 2D layered material using intercalation strategies, establishing systematic patterns for rational design of materials with desired properties. Additionally, undergraduate students receive training for careers in science and engineering. This research is open to all individuals; military veterans are recruited for participation in this research. Online resources are created in conjunction with this work, enhancing the knowledge base available for the community and the broader public. TECHNICAL SUMMARY Controlling thermodynamic phase behavior in a 2D layered material using intercalation has been highlighted as a unique strategy to achieve novel material phases and access heretofore unknown functional behaviors. There are two competing or synergistic components of an intercalation layered material that can undergo a phase change, namely the guest intercalant and the host crystal. An intercalant can undergo polytypic phase transitions or disorder-order transitions, many of which can give rise to emergent confined quantum phases. A host can have pressure or temperature driven phase transitions that can be modified by intercalation, including inducing amorphization or stabilization of phases which would not normally occur. With support from the Solid State and Materials Chemistry Program in the Division of Materials Research, this project uses a general set of chemical methods for intercalating high densities of zero-valent elements from across the periodic table, atomic intercalation in 2D layered materials as a route to chemically tune thermodynamic phase behavior. This effort is three-fold and intertwines solid-state chemistry, spectroscopy, and high pressure/variable temperature. Specific objectives are to (i) devise new chemical methods to synthesize 2D layered materials, (ii) develop chemical intercalation strategies to intercalate and deintercalate zero-valent metals in 2D layered materials, and (iii) investigate thermodynamic properties of intercalated 2D layered materials using high pressure generated in a diamond anvil cell and variable temperature techniques. Core to this work is measurement of the acoustic phonons using Brillouin light scattering, which is central to understanding mechanical properties, thermodynamics, and phase states in a material and that can catch phase transformations that X-ray diffraction alone might miss, especially when considering closely related phases. This effort lays the foundation for accessing chemically tunable phase transitions in 2D layered materials using atomic intercalation and is crucial for accessing new functional behaviors, new materials, and understanding of material stability – all which is critical for future applied device performance. Educational and broader outreach goals include training undergraduate students for careers in science and engineering. Military Veterans are recruited for participation in research through the UC Davis Veteran Success Center. This work creates, develops and expands online tools including an online recipe guide for intercalation of metals in layered materials. It enhances and expands an online Brillouin spectroscopy database complementing similarly available databases providing the community with a unique consolidated resource for Brillouin spectroscopy. 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

Accurate statistical inference is essential for making reliable decisions in various fields, such as forensic science, medicine, economics, and machine learning. This project develops and advances generalized fiducial inference (GFI), an innovative statistical method that quantifies uncertainty without requiring subjective assumptions. By addressing complex real-world problems, such as evaluating evidence in criminal cases, understanding causal relationships in economics and health, and improving reliability in machine learning, the project will significantly enhance decision-making processes. Additionally, the project provides valuable research training opportunities for graduate students in science, technology, engineering, and mathematics (STEM), thereby contributing directly to national goals of promoting scientific advancement, health, prosperity, and welfare. This collaborative research aims to advance generalized fiducial inference (GFI), building upon Fisher’s original fiducial argument and recent developments in modern statistics. The primary objectives include extending GFI methods to causal inference models, particularly instrumental variable models, and redefining GFI through normalizing flows to manage computational complexity in non-analytic scenarios. The project will also apply these methodological innovations to pressing real-world problems in forensic science, specifically addressing the accurate calibration of likelihood ratios from machine learning models, as well as, resources permitting, investigations into uncertainty quantification for social network learning and sports analytics. 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 AI Institutes Virtual Organization (AIVO) is a community hub activity that connects the nation’s premier AI Research Institutes into a vibrant and impactful innovation ecosystem. The National Artificial Intelligence (AI) Research Institutes Program is a multi-agency and multi-sector effort implementing key objectives in the U.S. AI strategy. Each Institute is a large-scale collaborative effort involving a network of multiple funded organizations, many research professionals, and community partners working together to advance and apply AI knowledge and methods and build new platforms for AI research infrastructure and innovation. In this project, AIVO connects these individual AI Institutes into a whole that is greater than the sum of its parts. AIVO does this by amplifying Institute efforts and outcomes, by enabling collaboration and connection across the boundaries of individual Institutes, and by raising public awareness of the research and impact of the AI Institutes. AIVO operates at the nexus of a broad and evolving network of stakeholders that are essential to the success of the National AI Research Institutes Program. As a community-led coordination and amplification hub for the National AI Research Institutes Program, AIVO serves the needs of federal funding agencies, the researchers and educators within the AI Institutes, partners and collaborators, and the broader public. In its “Convening” role, AIVO supports inter-Institute workshops and Special Interest Groups, plans and executes annual program meetings, and hosts mechanisms for Institute knowledge exchange. In its “Connecting” role, AIVO provides community collaboration platforms to bring researchers and practitioners together for interdisciplinary innovation and leads in catalyzing an active knowledge exchange network among leading researchers and educators in AI and related disciplines. In its “Collaboration Enabling” role, AIVO catalyzes partnership development with new communities and multidisciplinary dissemination through opportunities such as travel support and programs and partnership development venues. AIVO performs its “Engaging the Public” function in several ways, including working with the leadership of Institutes to develop new content for media outreach. Finally, AIVO’s “Amplifying Institute Efforts” function is realized through a dynamic Web presence featuring Institute directories, events, and highlights; and by cross-posting Institute news and accomplishments across various social media. AIVO’s professional communications staff augment the efforts of Institute staff for communications strategy and promotional campaigns. Through this rich set of programs delivering on the virtual organizations core functions, AIVO enhances the impact of each Institute and the program as a whole, offers value-added programs for workforce development and societal impact, public outreach strategies, and strategic partnerships. In partnership with NSF, AIVO will remain and grow as a dynamic and responsive organization that scales with the growth and activities of the AI Institutes program and helps ensure the nation’s long-term AI success. 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

Cryo-electron microscopy (cryo-EM) has revolutionized the way scientists uncover the 3D structure of proteins, making critical contributions to our understanding of viruses, cancer, and neurodegenerative diseases. However, despite its transformative potential, cryo-EM is limited by the immense computational cost required to process the large, noisy, and unstructured image data it generates. This project aims to overcome these barriers by developing robust, efficient, and theoretically guaranteed algorithms for reconstructing 3D molecular structures from raw cryo-EM data. These algorithms will allow researchers to extract higher-resolution structures more quickly and reliably, accelerating scientific discoveries in biomedicine and supporting translational science in areas like drug development. Graduate student training will also be included in this project. Technically, the project addresses two critical stages of the cryo-EM pipeline: image alignment and molecular orientation estimation. For image alignment, the project will develop new shift-robust and deformation-tolerant metrics to improve classification and registration of raw 2D images. For orientation estimation, the project will develop a novel, decentralized message-passing algorithm to synchronize noisy and partially corrupted pairwise measurements of euclidean motions of 2D images and 3D molecules. By leveraging tools from harmonic analysis, optimal transport, and Riemannian optimization, the proposed methods will significantly improve the speed, accuracy, and transparency of current reconstruction techniques. These innovations will be implemented as open-source software, broadly benefiting applications in biomedical imaging, computer vision, and robotics. 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 tremendous growth and widespread deployment of sensor networks in advanced technologies like robotics, autonomous vehicles, environmental monitoring, and digital farming underscore the historic new industry revolution of the 21st century. As data measurement and processing increasingly take place collaboratively across networks of distributed agents, there is an urgent need to innovate networked systems for collaborative sensing and decision-making while maintaining high spectrum efficiency. Collaborative sensing and decision-making based on edge devices and agents typically features low-cost, modest-power, and limited-computation-capacity wireless devices and sensors. Current wireless protocols, such as IEEE 802.11 Wi-Fi and 3GPP Cellular networks, tend to be inefficient for short data bursts due to their relatively large control and signaling overhead. This project represents a comprehensive effort to develop innovative solutions for collaborative decision-making based on wireless networks of distributed nodes, aiming to jointly achieve high decision accuracy and spectrum efficiency. The project's goal is to develop innovative, simple, and low-cost networks that are easy to deploy for future large-scale applications in smart farming, disaster or hazard detection, air quality monitoring, and security. Leveraging a collaborative framework based on collaborative over-the-air sensing (COTAS), the project focuses on the most common and specific learning tasks: event detection and parameter estimation. Among its major research thrusts, we specifically tackle critical and practical challenges including COTAS detection and estimation under channel uncertainty, robustness against passive eavesdropping attacks, secure and reliable COTAS detection against jamming and Byzantine attacks, as well as deep learning solutions against physical uncertainties. Successes from the proposed research tasks can significantly impact collaborative decision-making, federated learning, sensor networks, and network spectrum management. Broadly, this work promotes reliable and efficient decision-making based on wirelessly-networked agents in cooperative systems ranging from smart agriculture, seismic detection, environmental monitoring, safety monitoring, and national security, thereby providing immense potential societal impacts. 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

Quantum states of matter have unique properties due to the presence of quantum entanglement. They appear at low temperatures in a variety of materials. Some of these materials are minerals found in nature or are derived from them, and others are synthesized in the cutting-edge laboratories of today using trapped cold atoms or optical lattices. Mathematically, these systems are studied in the ground state, which is the state of smallest possible energy of the systems. In experiments, this corresponds to temperatures near absolute zero. These special states of matter have attracted a great deal of attention in the past few decades especially due to their relevance for quantum information science and technology. Any advantage of quantum information processing (including computation) derives from the special properties of quantum entanglement. It is therefore important to characterize the structure of such states in a rigorous manner. Topological phases represent types of specially entangled quantum states. They can be classified by so-called topological invariants which explain why these phases are particularly robust against noise and other perturbations. This robustness is an essential requirement in applications of topological insulators to quantum memory. The term `insulator’ refers to the appearance of a gap in the energy spectrum above the ground state. A key property is the stability of the ground state gap under moderate perturbations of the Hamiltonian. The principal investigator studies this spectral gap in a series of paradigmatic model systems. The topological characterization translates into a succinct description of the elementary excitations of the systems. In two-dimensional systems (thin layers) this description is in terms of a novel type particles called anyons. This study is motivated by the possibility of anyon based quantum computation in solid state devices. Attracting and educating talented junior researchers and preparing them for the future quantum science and technology workforce is an integral par of the project. Mathematics is crucial to the precise and quantitative description of topological phases. At the highest level, different phases in two dimensions are often characterized by a modular tensor category. The principal investigator works with junior collaborators to prove results about the existence and stability of the spectral gap for types of many-body quantum systems for which such mathematical results are currently lacking. For example, in the case of the pseudo-potential models introduced by Haldane and others, the detailed analysis of the operator product structure of the ground states undertaken in this project, may open a new avenue for progress on the spectral gap problem. A related topic is the study of ground state phase diagrams. In addition to the study of topological indices, the principal investigator and his graduate students work on obtaining more detailed descriptions of the entangled ground state phases using string order parameters and new dualities, which have been conjectured in the recent literature. At the technical level, this involves applying the techniques of operator algebras and functional analysis, and also requires further development of those techniques. Spectral analysis for many-body fermions subject to a magnetic field in two dimensions, is an example where new technical advances are pursued. National and international collaborations will be a key part of this activity, both by using online collaboration tools and in person conferences, schools, and research visits. 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 Navier-Stokes (NS) equations are fundamental equations that describe the motion of viscous fluid flows. Boundary layers form for low viscosity flows (such as air and water) in thin regions near physical boundaries where the flow experiences a sharp transition from nearly inviscid flow in the bulk to the no-slip boundary condition at the wall. These boundary layers exist in a variety of flows in nature and society, such as flows over an airplane wing or fluid flow near the nose of a submarine. The mathematical questions of characterization and stability of boundary layers, as well as large time limits (hydrodynamic stability), are classical and notoriously challenging. They pose many novel analytical challenges within the Navier-Stokes equations, such as multiscale phenomena, mixed-type phenomena, and singular perturbations. The project will develop of a systematic theory of hydrodynamic stability and boundary layers which develops the mathematical tools to address these challenges. These mathematical results have large downstream scientific impacts on a variety of applications (such as airfoil modeling, modeling the nozzle of a submarine). More precisely, a better understanding of reduced models and their stability enhances computational algorithms and gives rise to new algorithms to model these important phenomena. The boundary layer theory for stationary NS flows naturally trifurcates into the three regimes: local-in-x, favorable, and adverse. For the local-in-x and favorable cases, the investigator studies the stability of the boundary layer ansatz in the inviscid limit, focusing on physical scenarios of wedge flows as a starting point which feature family of well-known self-similar profiles, the Falkner-Skan profiles. For the adverse cases, the investigator first develops fully the boundary layer equations through separation and reversal. The investigator then studies how these phenomena manifest in the full NS equations. Within hydrodynamic stability, there is a celebrated literature recently in which the subtle stability mechanisms of mixing, through inviscid damping and enhanced dissipation, are propagated at the nonlinear level. These mechanisms have been developed through delicate frequency side techniques on domains without boundaries. There has been a major gap to develop these mechanisms on domains with boundaries, the prototype being the channel, where the physical inhomogeniety of the vertical boundary obstructs the known techniques. The principal investigator develops new techniques that help fill this gap, in the canonical scenario of the Couette flow with Dirichlet boundary conditions. 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

It is estimated that approximately one-third of the world’s gross domestic product involves passes through a catalytic processes at some stagereactor, and the majority of industrial chemistry relies on catalysts. Industrial catalytic processes—such as those used in the production of commodity and specialty chemicals, petroleum refining, pharmaceuticals, and pollution abatement—form the foundation of the global economy and standard of living. Most of these processes utilize heterogeneous catalysts. A significant portion of heterogeneous catalyststhese includesare supported metal catalysts, such aslike those used in automobile catalytic converters. These systems consist of nanoparticles made from expensive metals like platinum and rhodium, which are anchored onto stable, highly porous supports (e.g., aluminum oxide). In a catalytic converter, harmful exhaust gases—including carbon monoxide, nitric oxide, and unburnt hydrocarbons—are adsorbed onto the surface of these metal nanoparticles. There, they undergo chemical reactions that transform them into less harmful products: carbon dioxide, water, and nitrogen. Due to the high cost of the metals involved, the nanoparticles are engineered to be as small as possible to maximize surface area and catalytic activity. However, without anchoring the particles onto a support, these nanoparticles tend to coalesce at elevated temperatures, which significantly reduces their surface area and effectiveness. Developing improved supported metal catalysts is therefore both time-consuming and cost-intensive with the current state-of-the-art. The Center for Rational Catalyst Synthesis (CeRCaS) is tackling this challenge by seeking to understand the fundamental chemistry and engineering principles involved in synthesizing ultrasmall metal nanoparticles on supports. In systems requiring two metals—such as catalytic converters—CeRCaS also focuses on strategies to position both metals in close proximity to enable synergistic activity. These efforts aim to create a more rational, scientifically guided, and streamlined approach to catalyst development across the many industries that rely on heterogeneous catalysis. CeRCaS is composed of three university sites: The University of South Carolina (USC) serves as the lead site and houses the broadest range of catalyst synthesis methods along with high-throughput catalyst evaluation capabilities. Virginia Commonwealth University (VCU) is the second site and contributes specialized expertise in pharmaceutical catalysts, reactions, and processes. The third site, jointly operated by the University of California at Davis and Berkeley (UCD/B), provides deep expertise in metal/zeolite catalyst synthesis, which is particularly relevant to petrochemical applications. Together, these institutions bring complementary strengths to advance the science and engineering of heterogeneous catalyst design. 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 concerns research in analytic number theory. Most questions in analytic number theory are concerned with quantitatively counting arithmetic objects, such as prime numbers and integral solutions to equations. Understanding such questions plays an important role in applications such as cryptography, information theory, physics, economics, and computer science. Powerful analytic tools have proved fruitful in many arithmetic problems, yet there is a natural barrier in these classical methods that limits their applicability. For example, it is not known if there is a prime between every two consecutive squares, even assuming the Riemann Hypothesis. The goal of this project is to develop and exploit new analytic methods to study arithmetic problems on the boundary of classical methods. The educational component of the project will include training and support for undergraduate and graduate students as well as postdoctoral researchers. The barrier often comes from the square root cancellation for individual exponential sums. One way to break the square root barrier is to exploit cancellations in sums of exponential sums over the moduli, which naturally connects analytic number theory and the theory of automorphic forms. The PI will explore the interactions between these two areas by developing new tools in both fields, including various forms of a Kloosterman refinement of the circle method and explicit Kuznetsov trace formulae for congruence subgroups of higher rank groups. The PI will combine these new techniques to study Diophantine problems over thin sets of the integers such as prime numbers and smooth numbers, to count integral points of algebraic varieties beyond hypersurfaces in few variables, and to establish value distributions (e.g. non-vanishing, extreme values and subconvexity) and asymptotic formulae for moments of families of automorphic L-functions of higher rank groups. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Kristie Koski of the University of California, Davis is studying a new silicon-based semiconductor nanomaterial, colloidal silicon telluride (Si2Te3). Si2Te3 nanoparticles offer a cheap, controllable photodetector material that is less-toxic than many current alternatives. More importantly, their optical properties are tunable, meaning that the color of light produced or absorbed by the nanoparticles can be adjusted over a wide range including both infrared and visible light. This is done by controlling the size, shape, chemical doping, and intercalation (placing atoms or ions in between the few-atom-thick layers of Si2Te3). This work will address fundamental questions of how to best create and control this unique material and why the properties of this material, especially the capability for intercalation, are so different for nanoparticles than they are for bulk-scale material. Colloidal silicon telluride stands out as a unique nanoparticle and will join the library of nanocrystal systems with promising nanoscale properties offering many applications in future technologies. This work will train undergraduate and graduate students for careers in science and engineering. Given the simplicity of growth, silicon telluride synthesis will be developed as an undergraduate laboratory experiment. With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Kristie Koski of the University of California, Davis is studying synthetic control of size, shape, hyper-branching, and surface functionalization of colloidal, luminescent silicon telluride (Si2Te3) nanoparticles. Nanoscale synthesis and control will be achieved by combining new synthetic pathways with systematic studies to enable a complete understanding of the nucleation and growth of this unique material, thereby producing control of the properties that are most critical for application: dimensionality and optically active electronic states. Hypothesis-driven strategies for control of morphology, hyper-branching, size, shape, and ligand-chemistry will be developed. Photoluminescence in Si2Te3 originates from deep defect trap states which are affected by size, shape, morphology, doping, surface-functionalization, and intercalant guests, where competing trap state energy is easily shifted with measurable photoresponse. Tunability of full spectrum optical properties of colloidal Si2Te3 nanoparticles using ionic (Li+, Mn+) and atomic intercalation will be established. This work will try to understand why colloidal nanoparticles uptake more intercalant guests at faster rates despite having bulkier ligands and surface charge that should affect initial stages of intercalation—addressing outstanding questions about intercalation at the nanoscale using in situ single-nanoparticle optical photoluminescence. Si2Te3 is the perfect testbed for such a study. It is easy to make, layered, and it has an easy-to-measure optical photoresponse (from 600 nm to the infrared) that is highly sensitive to morphology, doping, and intercalation. Chemically tunable photoresponse in silicon telluride nanoparticles will be demonstrated offering potential for future tunable IR photodetection. Undergraduate and graduate students will be trained for technical careers in science and engineering. 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 research project explores how high school teachers' comfort with ambiguity in instructional situations--called Tolerance for Ambiguity (T4A)--affects science teaching and learning. Building on previous work showing that open-ended, model-based lessons help students better understand science, the researchers found that teachers who were more comfortable with uncertain or open-ended situations were most successful in supporting student learning. The new study will examine how classroom activities unfold differently across different teachers' T4A, co-develop a professional learning program to help teachers become more comfortable with ambiguity, and test the impact of this program on teachers' instruction and student learning. Ultimately, the goal is to create more effective science classroom learning experiences by helping teachers embrace and make productive use of scientific uncertainty in ways that support deeper learning and sensemaking. The findings from this work will help improve how teachers implement high-quality science instructional materials. Findings also will directly inform efforts in science education more generally that involve engaging students in rigorous and ambitious scientific reasoning. This project builds on an NSF-funded project that examined the efficacy of the Model-Based Educational Resource (MBER), which develops scientific understanding by engaging high school students in modeling. This study demonstrated a significant positive effect of the program on students' abilities to reason with scientific models, and that teachers' T4A was a significant moderator on student achievement. The present study includes co-design of a professional learning intervention to increase teachers T4A; an observational study and teacher interviews with 10 teachers to document results and distinguish instructional moves related to T4A; a subsequent study of 60 teachers to further refine the observational coding scheme; and measure development for T4A and teacher confidence implementing open-ended tasks. The findings will contribute to both theory and practice by advancing understanding of how teacher dispositions influence science instruction and by developing practical strategies to help teachers manage ambiguity in support of deeper and more effective sensemaking. The research is foundational to instructional materials development, professional learning design, and pre-service science teacher education. This study is funded by the ECR program, which supports fundamental research in service of STEM education. 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 aim of this multi-disciplinary project is to better understand how plant cell division is regulated. During plant cell division, a novel structure called the cell plate forms and develops into the new cell wall that separates the mother cell into two new daughter cells. The creation of the cell plate and new cell wall during cell division is of fundamental importance for the growth of all plant life. This project will determine how the highly dynamic process of cell plate formation is regulated by hormonal signaling during plant cell division. The results of this project will provide a mechanistic, transformative understanding of hormonal signaling, cell division, and cell wall formation that will be applicable to a broad variety of plant species. Application of the project’s findings will guide bioengineering efforts towards more desirable and advantageous cell wall compositions, and improved plant growth and biomass utilization, to benefit society. Our successful educational animations will be updated with new findings and supplemented with a guidebook to continue engaging a broad audience. Junior scientists at the undergraduate, graduate, and postgraduate level will receive research training across a broad range of cutting-edge research methods in modern cell biology. All trainees will receive strong professional development support through tailored development plans. The overarching goal of this multi-disciplinary project is to dissect how cell plate formation is regulated by cytokinin (CK) hormonal signaling. In plant cytokinesis, de novo formation of a cell plate evolving into the new cell wall partitions the cytoplasm of the dividing cell. Cell plate formation involves highly orchestrated vesicle accumulation, fusion, membrane network maturation ,and time-critical polysaccharide deposition, including that of the transiently incorporated, flexible callose. This project aims to understand how cytokinin signaling interfaces with endomembrane trafficking and regulates the development of the new cell wall and thereby viability of all plant life. The specific aims of the project are: 1) Identification and characterization of callose synthase complexes in cell plate development; 2) Determination of how the cytokinin signaling pathway controls cytokinesis; 3) Characterization of cytokinin-regulated callose remodeling during cell plate development. Detailed characterization of the identified molecular players and their roles in cell plate assembly will make various transformative contributions to the field of hormone signaling and cytokinesis which will open translational applications in biomass control, and targeted improvements in cell wall engineering. This project is funded by the Cellular Dynamics and Function Program of the Division of Molecular and Cellular Biology. 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 US chocolate industry is nearly $24 billion in size. It depends on cocoa imported from a few tropical countries. Cocoa prices are vulnerable to extreme weather, disease outbreaks, political instability, and a growing global demand. The price reached an all-time high of $12,565 per metric ton in December 2024. Producing cacao in the US under controlled conditions could stabilize the supply chain. This project attempts to produce cacao from plant cells grown in low-cost reactor systems. This would guarantee a steady supply of cacao produced in the US. The project is focused on improving the production of cacao in plant cell cultures. It will also evaluate low-cost materials of construction for the reactor systems. The project will help in training students to participate in the future bioeconomy workforce. Manufacturing cacao would be expensive using current technology. An economic analysis of the technology required points to several key costs. Bioreactors are traditionally made of steel and are sterilized using high-pressure steam. Steel construction is expensive. High pressure steam is expensive, contributing to high annual operating costs. The productivity of batch cultivation of cacao is low, further contributing to high annual costs. To address these limitations, the team will pursue several strategies. High-density polyethylene (HDPE) bubble bioreactors will be custom designed. Alternate sanitization strategies will be evaluated. Process intensification strategies based on semicontinuous operation assisted by real-time in-line biomass monitoring will be developed, along with low-cost drying methods, to increase volumetric productivity and product quality. Technoeconomic analysis (TEA) and life cycle analysis (LCA) models will be developed to guide the process research and development efforts to ensure economic viability and sustainability. Results from this project directly apply to other plant cell and/or algal cell bioreactor-based processes, as well as other fermentation processes that require lower capital and operating costs, particularly microbial/fungal production of food and commodity industrial products. This project is being jointly supported by ENG/CBET/CBE and the BioMADE Manufacturing Innovation Institute. 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 Pathways to Enable Open-Source Ecosystems (POSE) project will enable CarDreamer, the first open-source ecosystem (OSE) focused on resources for the autonomous driving community of researchers. CarDreamer is a bridge that fosters collaboration across communities working to advance artificial intelligence (AI) technologies for autonomous driving. It addresses the needs of its stakeholders, ensuring a cohesive research and development pipeline. Through a variety of scoping and planning activities carefully designed for ecosystem discovery and community building, the CarDreamer OSE will engage potential users and content contributors and provide a suite of tools enabling them to develop their own AI functionalities and seamlessly integrate with other modules, thus catalyzing the sustainable, long-term growth of the OSE. This POSE project will develop CarDreamer as a comprehensive educational resource for embodied AI and the autonomous driving community. The project will help equip a workforce with the skills required to navigate the evolving challenges and opportunities presented by embodied AI and self-supervised learning (SSL) for autonomous driving. The flexible and modular design of the CarDreamer OSE enhances a concerted effort across communities in academia and industry, addressing the needs of different stakeholders in the autonomous driving community and ensuring an integrated research and development pipeline for world-model-based SSL. By providing hands-on experiences, the project invites a broad community, including K-12 students and educators, to engage with complex technological concepts. Engagements, such as hosting summer interns from community colleges, will position to the project to prepare the next-generation workforce in AI technologies for autonomous driving. 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 · 2025-07

This project studies contact and symplectic geometry, an area of mathematics studying key physical phenomena. In particular, the project develops new results and techniques to mathematically model, manipulate and classify the behavior of rays of light, in the form of geometric optics, and the evolution of temperature and equilibrium states, in the form of thermodynamics. The novel mathematical methods developed in this project rely on a geometric approach, based on the manipulation of a new type of diagrams introduced by the Principal Investigator, that bypasses brute force computations in favor of quicker and clearer qualitative understanding. The results of this project lead to more efficient predictions of the behavior of rays of light and the evolution of heat, thus allowing for a wider range of scientific applications. The project also contains a number of such applications, including results in algebra and combinatorics, as well as mathematical physics, via the quantization of classical systems. The broader impacts of this project include a series of intensive workshops and seminars aimed at training the current generation of early career mathematicians, and two new research monographs streamlining some of the most useful and recent developments in the area, making them accessible and usable to the broader scientific community. In a nutshell, the two major goals of the project are the study of Legendrian submanifolds and their Lagrangian fillings, and the development of applications of Lagrangian submanifolds. The project addresses geometric and algebraic properties of the moduli space of Lagrangian fillings of Legendrian submanifolds, and the construction and manipulation of combinatorial structures from Lagrangian submanifolds. The project includes new applications of these goals to cluster algebras, mathematical physics and algebraic combinatorics. The advent of sheaf-theoretic techniques has now opened a wide variety of questions on Legendrian submanifolds, specifically via the study of their moduli spaces of Lagrangian fillings. In this project, we study the classification of Lagrangian fillings of a given Legendrian submanifold, including the derived geometry of their moduli, incarnated by the space of sheaves singularly supported on that Legendrian. In addition, we develop of a series of constructions connecting the symplectic geometry of Lagrangian submanifolds to other areas of mathematics, including algebraic combinatorics and string theory. Specifically, the bridge between 3D plabic graphs and Lagrangian fillings, the combinatorics of higher-dimensional rulings for Legendrian fronts, and the construction of spectral networks from Betti Lagrangians. 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 · 2025-07

In the McMurdo Dry Valleys (MDV), the permanently ice-covered lakes are unusual aquatic environments that are home to unique communities of microbes. Mats of photosynthetic microbes grow on the floors of these lakes and are an important part of the ecosystems in MDV environments. They experience extreme seasonality that includes the challenge of months of winter darkness. Mats cannot photosynthesize during winter, but they do remain active, and this can impact their ability to respond when spring/summer conditions arrive. To understand these impacts, it is essential to study the seasonal differences in mat organism activity, particularly during winter and then during the transition to spring sunlight. This project aims to characterize winter season activity and chemistry in benthic microbial mats in Lake Fryxell, McMurdo Dry Valleys, Antarctica. To access winter-like conditions, temporary shades will be installed over mats to “extend” dark winter conditions into spring, when field access is possible. By sampling in extended winter and spring/summer this project will determine the changes in benthic mat community composition, identify interactions between cell activity and water chemistry, and understand the effects of spring photosynthesis initiation on mats that become anoxic over winter. The permanently ice-covered lakes of the McMurdo Dry Valleys (MDV) are home to unique communities of microbes. Benthic mats of photosynthetic cyanobacteria are present in these lakes and are an important part of the primary productivity for ecosystems in MDV environments. Winter darkness inhibits mat photosynthesis, leading to anoxic conditions, limited viable metabolisms, and changes in mat biogeochemistry. With the return of sunlight photosynthesis begins again and metabolism and biogeochemistry changes. To better understand the interdependencies and impacts of these transitions, the project will characterize seasonal microbial diversity and metabolic differences in benthic mats, and the ecosystem effects caused by spring oxygen production in Lake Fryxell, McMurdo Dry Valleys, Antarctica. To access winter-like conditions, temporary shades will be installed over mats to “extend” dark winter conditions into spring, when field access is possible. The project will characterize the metabolic changes in mat communities using metagenomic and metatranscriptomic methods, in conjunction with pore water dissolved oxygen and sulfide measurements from the environment during the winter/spring transition. 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 · 2025-07

Geometric and topological data analysis comprises a large collection of useful tools and methods in the modern landscape of data science with numerous applications in a broad variety of fields. Relevant concepts have evolved into versatile tools that are reshaping the manner in which we handle, scrutinize, and make sense of intricate data structures. This project aims at tightening the connections between geometric and topological data analysis on the one hand, and statistics and machine learning on the other. This goes along with the training of both graduate and undergraduate students, which is another integral part of the project. More specifically, the project will focus on theory and methodology based on core concepts in the field: the Euler characteristic, the graph Laplacians, and the heat kernel. Among others, this includes the development of large sample distribution theory for an appropriately weighted Euler characteristic process, and a novel two-sample testing procedure for isometry of manifolds based on the heat kernel signature. Crucially, the contributions of the project will result in novel insights catalyzing further developments, which will also benefit practitioners. The theoretical tools used in this project are a combination of tools from geometric probability theory, the analysis on Riemannian manifolds, and differential geometry. While these advanced topics lend themselves naturally to the training of graduate students in statistics, the overarching geometric nature of the project also allows for an exciting and meaningful involvement of advanced undergraduates. 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 · 2025-07

This research will contribute to the analysis of modern and complex data observed as functions or curves. One example of such data is provided by yield curves used in economics as an indicator for future growth, inflation and interest rate expectations, and investor sentiment. Since data of this type is often observed across time, there will be a focus on developing theoretically justified and empirically validated forecasting algorithms, which can find applications in many fields of inquiry including finance, where practitioners are interested in predicting the volatility curves of intraday tick-by-tick transaction data of financial assets. The research is therefore of immediate interest in areas of application and will further connect statistics and fields significantly relying on data-analytic tools. In addition, the research will advance mathematical and computational statistics. It will produce doctoral students, who are theoretically and practically versed in both statistics and an area of application. The training and involvement of undergraduate students is also included through regular coursework, independent study and projects. This research concerns the development of a comprehensive framework for the analysis of nonlinear and possibly non-Gaussian functional time series. Such functions naturally arise in a variety of contexts such as the modeling of cumulative intraday returns of financial assets. The research aims at providing a statistical foundation to analyze functional observations that exhibit non-linearity and are possibly heavy-tailed. Key outcomes to be achieved include new probabilistic results concerning the structure of nonlinear functional time series, the introduction of two new functional time series models as lead examples of the theoretical groundwork, namely a fully general version of generalized autoregressive conditional heteroscedastic processes as well as a novel random coefficient autoregressive model. These models are accompanied by a suite of inference procedures for further statistical analysis. Achieving the goals of the research projects will require non-standard approaches, as traditional sets of assumptions for the analysis of linear functional time series are expected to be of limited use in the setting studied under this research. 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 · 2025-06

Density Functional Theory (DFT) is a phenomenally successful approach to finding approximate solutions to the Schrodinger equation, the fundamental equation that describes the quantum behavior of atoms and molecules. The fundamental results of Kohn, Hohenberg, and Sham showed that the ground-state energy from the Schrodinger equation is a unique functional of the electron density and subsequently reduced solving a complicated many-body problem to finding a consistent solution to a set of single-particle equations. However, the exact density functional is not known and scientists have developed hundreds of density functional approximations (DFAs). Choosing a DFA is perhaps the most important decision when performing a DFT calculation. The project's novelties are in using formal methods to ensure that the implementation of a DFA is provably correct. DFT calculations are widely used in a number of scientific and engineering fields, including solid state physics, computational chemistry, and materials science. Consequently, the project's impacts are in improving the correctness of scientific software in these application domains. The key insight of the project is that each DFA is a mathematical function with a known, albeit complicated, analytical form, and, hence, can be analyzed using formal-methods techniques such as Satisfiability Modulo Theory (SMT) solvers. The project will automatically verify whether a DFA implementation satisfies DFT exact conditions (known analytical properties of the exact functional). The project will automatically verify numerical properties of a DFA implementation (generation of Not a Number (NaN) and infinities, and numerical instabilities slowing down convergence). The project will develop techniques to generate provably correct minimal edits to a DFA implementation that are guaranteed to be numerically stable and continue to satisfy the same exact conditions. By analyzing existing DFAs and developing new DFAs, the project will have tremendous impact in a number of scientific and engineering 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 · 2025-06

In today’s application-centric networking, the end user’s quality of experience is of paramount importance; for example, end users do not want to experience delays in website response or interruption in video downloads or video viewing. On the other hand, they do not always provide high quality of experience. Often, this is attributed to the network layer (e.g., congested link, packet loss); however, issues may also lie in the lower fiber optic communications layer. Optical fibers carry information with high signal quality and reliability; yet, at long distances, signal quality may degrade due to various phenomena, including fiber/amplifier aging, component anomalies, etc. To improve application performance and user experience, integrated end-to-end management is needed across the application, network, and physical (optical) layers. This approach can help network operators perform layer-specific root-cause analysis of the affected services, predict probable issues, and take preemptive actions to avoid poor service. This project addresses this critical need; it investigates cross-layer monitoring and analytics to correlate the effect of the optical layer on network-layer dynamics and vice-versa. This joint NSF-MeitY project will investigate machine-learning (ML) techniques for application-aware root-cause analysis and fault prediction. First, it will develop an optical testbed, which will be utilized by the ML model for anomaly detection and classification. It will implement routers for network-layer integration and monitoring agents at both optical and network layers. Second, a cross-layer analytics platform will be developed to achieve holistic observability from application layer to optical layer. It will build a correlator to map the impact of failures in optical layer (by simulating controlled anomalies in the optical testbed) on the performance of different applications at the network layer. The cross-layer correlator will employ ML-assisted tools to explain the root cause of service degradation at network layer. Finally, efficient strategies for preemptive application-aware fault remedy (such as re-routing of certain applications) will be developed. It will also study remedial actions at optical layer such as re-routing the affected lightpath, lighting up a new lightpath, etc. Strong international collaboration between UC Davis, IIIT-Delhi, and IIT-Madras will bring expertise and shared knowledge for effective problem solving in related research areas and verify the research outcomes on the IIT-Madras testbed. In summary, this project – with its cross-layer monitoring and analytics framework – will develop new insights in the discipline of reliable networking and contribute to the development of solutions for supporting robust future services. The project will provide excellent opportunity for training of graduate students, enhancing the educated workforce in STEM disciplines. 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 · 2025-06

This project will develop computational tools for designing new classes of high-performance mechanical metamaterials. Mechanical metamaterials are elastic materials engineered at the microstructure level to exhibit unique properties not found in nature. They have the potential to unlock new levels of performance in application domains like soft robotics, deployable structures, athletic gear, and prosthetics. However, designing microstructure geometries to create desired material properties poses significant challenges, especially for applications where the structures can undergo substantial deformations and self contact. In these settings, computationally intensive nonlinear simulation models must be used, and designing materials with controlled properties over their full range of possible deformations is prohibitively expensive with existing algorithms. The core aim of this research is to develop computational techniques to dramatically accelerate the simulation and design process for elastic metamaterials, making it practical to solve this challenging and important design problem. The project will also develop techniques for ensuring that the optimized metamaterials are as durable as possible and can be reliably manufactured on consumer-level 3D printers. The project will furthermore enhance STEM education by integrating these cutting-edge research topics into classroom lectures and facilitating outreach events where high school, undergraduate, and graduate students gain hands-on experience with computational design, numerical modeling and fabrication. The project will build a new computational framework for (1) fast periodic homogenization of nonlinear elasticity and (2) optimal design of elastic metamaterials to exhibit target properties over large deformations (finite regions of strain space). The central technical contribution is a suite of novel data-driven acceleration techniques based on adaptive high-dimensional interpolation, smart sampling, and machine learning that will enable solving metamaterial characterization problems to controlled accuracy at practical computational expense. This fast characterization method will be wrapped within an inverse design algorithm that is formulated as a shape optimization over a rich design space of smooth parametric lattice geometries. The design algorithm will incorporate physics-based manufacturability constraints and stress minimization objectives to ensure that optimized parts are robust to forces experienced during fabrication and use. The design tool will be evaluated on the task of creating metamaterials to emulate existing material models as well as producing exotic material behaviors like multistability and jamming. The performance of generated metamaterials will be confirmed with physical experiments, and the proposed acceleration schemes will be assessed on large-scale benchmark datasets that will be created as part of the project. Finally, a basic multiscale design tool for creating compliant mechanisms composed of spatially graded lattices will be developed using the generated metamaterials. The research will deliver powerful and accessible open-source computational design software, fast solvers for nonconvex optimization and sparse linear systems, and benchmark datasets to foster evaluation of future metamaterial design work. 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.