Grants

Department of Public Health

COMMUNITY DEVELOPMENT BLOCK GRANT CORONAVIRUS (CDBG-CV)

Chicago Department of Transportation

COMMUNITY DEVELOPMENT BLOCK GRANT CORONAVIRUS (CDBG-CV)

CDM Smith, Inc.

Management and Business Professionals and Administrative Services

NSF

The astronomical community devotes significant resources to ground-based and space-based astronomical surveys—systematic explorations of vast regions of space and time. The resulting catalogs provide “family portraits” of diverse cosmic populations, including asteroids and trans-Neptunian objects in the solar system, nearby stars and their exoplanets, distant galaxies and active galactic nuclei, and transient objects such as gamma-ray bursts, fast radio bursts, or supernovae. A research group at Cornell University will construct a “Cosmic Demographics Toolkit” (CDT), a widely applicable suite of conventional and modern computational tools accessible and appealing to the broad community of astronomers analyzing survey data. The project includes astrostatistics research producing methods with new capabilities, and development of software tools in an open-source, well-documented Python package. The resulting toolkit will help astronomers extract the best results from astronomical surveys. The researchers and their students will contribute to the training of the next generation of astronomers and computational scientists by participating in the Summer Schools on Statistics for Astronomy and Physics hosted by Penn State’s Center for Astrostatistics (CASt) and by producing videos communicating key aspects of advanced data analysis to the broader public. The CDT will include several demonstration applications, addressing important prototypical demographics problems such as estimating luminosity functions of galaxies, fast radio bursts, and gamma-ray bursts, and size distributions of trans-Neptunian objects. It will also address discovering correlations and scaling laws among the properties of astronomical objects. The software will be written in Python as new components of an open source package called Inference, which supports a variety of statistical inference tasks arising in astronomy. It will include hierarchical Bayesian modeling, nonparametric techniques, censored data methods, and Bayesian survival analysis methods. The project will take advantage of the Rubin Science Platform to accelerate development and tailor many capabilities to analyses using data from the Legacy Survey of Space and Time being undertaken by the Vera C. Rubin Observatory. 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

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Mary Kraft of the University of Illinois Urbana-Champaign is developing new computational visualization and analysis tools for depth profiling secondary ion mass spectrometry (SIMS) data. The distributions of various molecules within a sample influence its properties and function. Thus, knowledge of the molecular distribution within a sample facilitates understanding the material’s functions, and ultimately, designing new functional materials with advanced capabilities. Three-dimensional (3D) images of the molecular distributions within a sample may be acquired with depth profiling secondary ion mass spectrometry (SIMS). However, the 3D SIMS images acquired from contoured samples are distorted along the z-axis, and this distortion hinders their interpretation. Professor Kraft and her team plan to tackle this problem by developing software tools that use only SIMS depth profiling data to correct this distortion and produce accurate 3D images of component distribution within contoured samples. This research will entail the development of computational strategies for extracting height information from secondary ion intensities collected from organic and inorganic materials, accuracy assessment algorithms, and user-friendly interfaces that facilitate usage of these tools by others in academia and industry. The proposed research will produce new software tools that enable the user to create accurate component-specific 3D SIMS images of contoured organic and inorganic samples using any SIMS depth profiling dataset. These new computational tools would improve both the accuracy of SIMS 3D depth profiling images and their interpretation. Ultimately, this research could increase understanding of structure-function relationships in biological and synthetic materials, which could enable the design of new materials with advanced functions. The project will train graduate and undergraduate students in materials characterization using SIMS depth profiling, MATLAB coding, computational data visualization, image processing, image interpretation, semiconductor fabrication and characterization, cell culture, and polymer synthesis. This training will prepare students for careers in our semiconductor, electronics and pharmaceutical sectors. The computational tools and SIMS depth profiling datasets generated by this research will serve as STEM education tools. 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

The ability to accurately predict multiscale fluid dynamics phenomena is essential for accurate weather forecasting, climate modeling, design of fusion energy devices and numerous other practical applications. However, the predictive modeling of multiscale phenomena is a challenging problem. Computer simulations solve the relevant equations at specific points that form a grid. Simulations on coarse grids can be made today, but they require accurate models of physics below the scale of the computational grid, i.e., between points on the grid. No general and systematic approach to construction of such sub-grid-scale models currently exists to capture all of the flow physics. The objective of this project is therefore to develop a systematic approach to modeling multiscale phenomena at a desired level of resolution. The fundamental advances resulting from the project will also impact a range of other applications in science (e.g., dynamics of accretion disks, forest/brush fires, pollution transport) and engineering (e.g., aircraft design, combustion and hypersonic vehicles). The project will also educate and train several graduate and undergraduate students in state-of-the-art numerical modeling techniques supplemented by novel machine-learning architectures, providing them with skills directly transferable to jobs in research and development, applied engineering applications, and national security applications. Traditional, analytic coarse-graining approaches for modeling multiscale problems often lead to infinite hierarchies of equations, with numerous ad hoc assumptions employed to close the truncated system of equations. The resulting models lack generalizability and are fundamentally incapable of correctly describing fluxes of physical quantities from small scales to large scales. Models constructed using deep learning, on the other hand, lack the interpretability needed to understand the mechanisms responsible for generating structure at multiple scales. This challenge will be addressed by developing a systematic machine learning framework combining relevant, limited domain knowledge and plentiful, representative, and well-resolved data generated using experiments and/or numerical simulations. This new framework will allow construction of explicit mathematical models of multiscale phenomena without relying on empirical assumptions that may or may not be justified in particular geometries or under particular conditions. The explicit analytical form of an inferred model will allow straightforward interpretation of various terms and enable identification of the dominant mechanisms responsible for transport of physical quantities of interest between scales. 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

Heterogeneous catalysts are solid materials that facilitate the transformation of raw chemicals into useful products. Commonly, catalysts are metals with atoms arranged in ordered, repeating patterns—called crystalline structures. However, recent findings show that when these atoms are arranged randomly, in a so-called amorphous structure, the catalysts more effectively convert the raw chemicals into the desired product. This discovery opens the door to more energy-efficient and cost-effective chemical production, yet scientists still do not fully understand why the lack of atomic order improves the reaction rates. This project seeks to uncover how atomic disorder in amorphous catalysts increases reaction rates and selectivity, using the hydrogenation of carbon dioxide, i.e. the conversion of carbon dioxide and water into valuable fuels, over copper as a test case. By exploring how carbon dioxide and water interact with both ordered (crystalline) and disordered (amorphous) copper surfaces, the project aims to identify the atomic structures that make reactions faster and more efficient. Highly accurate computer simulations of atomic behavior and molecular transformation will map copper atom structure characteristics to chemical reactions and their barriers. Thus, it will provide the structure-activity relationships needed to develop new high performance amorphous catalysts. This work supports the NSF mission by deepening our understanding of fundamental chemical processes on complex materials and contributes to national interest by providing strategies to reduce the energy and cost of chemical manufacturing. It also advances education through the training of graduate students in catalysis and computational modeling and the organization of outreach activities that introduce students to engineering research. Despite their promise, amorphous catalysts are far less studied than their crystalline counterparts due to the difficulty of analyzing their complex disordered structures. This project aims to determine how disordered surfaces, water arrangement on said surfaces, and metal composition affect the activity and selectivity of amorphous copper catalysts in the electrocatalytic reductive conversion of carbon dioxide into valuable, higher-order (C2+) hydrocarbons. Using a machine-learning-accelerated computational approach, the researchers will develop highly accurate models based on quantum mechanical calculations. These models will calculate the minimum energy pathways of elementary reaction steps of electrochemical CO2 hydrogenation. The resulting minimum energy path reaction energetics and product distributions will reveal the effects of amorphous copper surface structures and the associated local water arrangements on catalytic performance. This data will be used to build structure–activity relationships, guiding the design of next-generation amorphous catalysts. This project will advance the broader field of catalysis by clarifying how catalytic site distribution of amorphous materials, and water structure influence reaction pathways. These findings have significant implications for the many reactions where water is present, and structures are disordered. Even more broadly, the computational approach developed here provides a generalizable method for studying amorphous materials, supporting their study and use in a wide range of applications such as energy storage, low-energy separations, and corrosion-resistant systems. 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

This Computation and Data-Enabled Science and Engineering (CDS&E) project seeks to support research the explores computationally efficient generation of the process-structure-property (PSP) map linking manufacturing process parameters to the resulting solidification microstructure. This understanding, which has a strong influence on the properties of additively manufactured materials, is critical to the design of metals with superior properties and can also be leveraged in an inverse problem framework to optimize process parameters for desired properties. However, the vast range of length and time scales inherent in the manufacturing process makes constructing PSP maps computationally prohibitive. This research seeks to address this challenge by developing a computationally efficient surrogate model for solidification, significantly accelerating both forward and inverse problems on the process-structure (PS) linkage. By enhancing the computational efficiency of manufacturing process parameter optimization, this looks to drive technological innovation, strengthen the US economy, and support workforce development by engaging graduate and middle-school students in STEM learning, cultivating the next generation of engineers and scientists. This project seeks to develop a physics-based, data-driven reduced-order model (ROM) for predicting microstructures evolution in binary alloy solidification. The proposed non-intrusive ROM reduces the computational cost of high-dimensional models by projection-based model reduction utilizing nonlinear manifolds and sparsity-inducing operator learning to capture transport- dominated phenomena across the solid-liquid interface. The research will: (1) develop a model reduction framework tailored for nonlinear transport-dominated processes, (2) construct a parametric ROM that maps process parameters to microstructure attributes, and (3) establish an inverse problem framework for optimizing process parameters. By accelerating the Process-Structure linkage in PSP maps, this work looks to advance the design of high-performance materials, and has broad applicability to other engineering problems, including crack propagation, solute distribution and wave propagation. 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

The Cosmic Microwave Background (CMB) is the relic radiation leftover from the explosive beginning of the Universe, known as the Big Bang. Analysis of the CMB suggests that the early Universe was smooth and homogeneous; whereas observations reveal that today it is mostly empty space filled with galaxies of different shapes and sizes. One of the most promising observational probes is 21 cm radiation from neutral hydrogen that was present in the early Universe. Researchers at Brown University will develop novel machine learning (ML) and artificial intelligence (AI) methods to model and interpret this cosmological 21 cm emission, providing insights into the nature of the first stars, galaxies, and the growth of cosmic structure. These ML/AI methods will have significant impact on any field where key information is encoded in image-wide patterns. The project will also provide training for a graduate student who will be deeply involved in the project and three undergraduate students as part of a continuing research experience program that provides flexible scheduling to accommodate familial, military, or other commitments. Measurements of highly redshifted cosmological 21 cm emission from neutral hydrogen during the epoch of reionization present the best way to study star and galaxy formation in the early Universe, because the cosmological 21 cm signal traces the neutral intergalactic medium and encodes the detailed properties of all the sources producing the ionizing radiation (i.e., stars and galaxies). The proposed ML/AI techniques will incorporate new interpretability and generalization methods that will make use of counterfactual data, which are inputs that produce significantly different outputs in the predictive models that are being explained. A framework to leverage the results to make predictive models more robust and generalize better across different physical simulations will then be developed. This work will provide an advanced tool to 21 cm cosmologists that is expected to improve robustness and trustworthiness of their predictive models. 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

This project is co-funded by the Chemical Mechanism, Function, and Properties Program, the Chemistry of Life Processes Program in the Chemistry Division, and the NSF Office of Advanced Cyberinfrastructure. In this project, Professor Zhongyue John Yang of the Department of Chemistry at Vanderbilt University is investigating the origins of wrapping chirality in lasso peptides, which are a class of knotted, interlocked molecules with growing promise in biotechnology and therapeutics. Although lasso peptides can theoretically form in either left- or right-handed wrapping configurations, nature has exclusively selected the right-handed form, a phenomenon that remains mechanistically unclear. This project combines cutting-edge high-throughput protein modeling and machine learning to uncover why this chiral preference exists and whether it can be re-engineered. Lasso peptides are ribosomally synthesized and post-translationally modified peptides that adopt a mechanically interlocked “lariat” shape. Despite the theoretical possibility of both left- and right-handed wrapping topologies (l-LaPs and r-LaPs), only right-handed forms have been identified in nature. This research investigates two hypotheses: (1) that r-LaPs are thermodynamically and kinetically preferred over l-LaPs, and (2) that lasso peptide cyclases selectively catalyze right-handed pre-lasso conformations. To evaluate these hypotheses, the project will develop high-throughput quantum mechanics (QM) and classical molecular dynamics (cMD) workflows to quantify the energy landscapes and entropy profiles of both l- and r-LaPs (Aim 1), and apply multiscale QM/MM and machine learning techniques to model enzyme-substrate interactions and identify cyclase mutants that can potentially generate l-LaPs (Aim 2). These computational pipelines are built on the PI’s previously developed modeling tool, LassoHTP and LassoPred, enabling systematic benchmarking across diverse lasso peptide-enzyme systems. The results will advance understanding of molecular determinants of peptide chirality and refine multiscale modeling strategies for complex, topologically unique biomolecules. 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

NON-TECHNICAL SUMMARY Designing new materials often requires extensive computer simulations to explore how a material’s internal structure influences its physical properties. This can be very costly, especially when materials have random or disordered features. While machine learning (ML) can speed up some parts of the design process, it does not always provide clear physical insights, such as why the connectivity of one material phase strongly affects overall conductivity or how certain patterns might enhance stiffness. This project addresses the challenge of making material design both efficient and explainable by focusing on the concept of N-point correlation functions (NPCFs). An NPCF is a statistical measure describing how different regions of a material relate to one another. By systematically learning which NPCFs matter most for a given property, the research will provide both accurate predictions and clear insights about the structure-property relationship. In addition to improving the design of smart composites for soft robotics, the methods developed will lay the foundation for broader applications in biological systems, climate modeling, and other areas where disordered structures play a critical role. The educational activities developed through this project will help K-12 students understand material structure-property relationships through hands-on experiments and contribute to training the next generation of researchers at the intersection of physics, mathematics, and computational science. TECHNICAL SUMMARY This project investigates how to identify concise and complete microstructure representations for two-phase, disordered heterogeneous materials. Since these microstructures can be treated as random fields, the research focuses on N-point correlation functions (NPCFs) as the core representation. Although machine learning models can approximate structure-property mappings, they often do not reveal the underlying physical causes. To address this, the project leverages a strong contrast expansion formalism, which links material properties to microstructure morphology through physics-based governing equations, such as partial differential equations (PDEs). By interpreting linear and nonlinear effective properties as expansions in terms of Green’s functions and NPCFs, the research team will develop new computational algorithms to learn both the necessary Green’s functions and the relevant NPCFs directly from data. These algorithms will provide a physics-driven mechanism for down-selecting which NPCFs are most critical, leading to an explainable and more efficient representation for microstructure reconstruction and property prediction. To demonstrate practical value, the project will apply these methods to the design of a bi-phase composite with specific mechanical and thermal transport properties relevant to soft robotics. By uncovering how different morphological features affect performance, the research aims to streamline the design process for materials that must satisfy coupled mechanical and thermal requirements. Beyond this specific application, the methods and principles developed will be broadly applicable to other random fields where PDE-driven properties dominate, such as multi-phase materials, biological systems, climate science, and cosmology. In addition to advancing knowledge in materials science, the project has a broader impact on data science by prompting new ideas for efficiently representing and modeling random fields. Just as insights into sequence modeling led to transformational language models, and insights into graph structures enabled advanced drug discovery methods, this research explores whether NPCFs can serve as essential building blocks for future architectures that handle large-scale or high-dimensional random fields. The developed educational activities will also contribute to training the next generation of researchers at the intersection of physics, mathematics, and computational science. STATEMENT OF MERIT 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

This Computational and Data-Enabled Science and Engineering (CDS&E) project aims to advance national prosperity and workforce development by enabling research that seeks to develop more efficient and cost-effective design of mechanical systems through automation. Traditional engineering design relies heavily on human expertise and extensive training, making it labor-intensive and expensive - especially in the context of preparing the next-generation workforce for the manufacturing sector. This research supports a shift toward data-enabled, computer-aided design by addressing a fundamental challenge: quantifying the amount of useful information obtained from a single mechanical test. By developing a theory that measures how much information each test provides, this project looks to lay the groundwork for automated design, testing, and analysis of materials and structures. The outcomes will benefit both industry and education by lowering training costs and enabling smarter, faster decision-making through artificial intelligence. The research project seeks to introduce a new concept called mechanics informatics - a theoretical foundation for learning material properties from a single, optimized test instead of many. Using advanced information engineering methods, the research team will design specimens that produce the most informative data under complex loading conditions. A new inverse learning algorithm will be developed, combining finite element simulations and Bayesian optimization to iteratively identify unknown mechanical parameters. The approach also includes uncertainty quantification using Gaussian processes to account for variations in measurements and model choices. Initially, the framework will be applied to aluminum alloy sheets to learn their plastic deformation behavior, then extended to other sheet metals produced by new manufacturing technologies and fiber-reinforced composites to study elastic properties under uncertain conditions. This work seeks to open new avenues for integrating AI into engineering, while promoting hands-on learning and collaboration with industry. 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

Maps of intergalactic hydrogen, understood through large-scale computer simulations, can reveal how the Universe began. The research team from the University of California, Riverside will use simulations and observations of intergalactic hydrogen to help understand what caused the Universe to expand in the crucial first nanosecond of its existence. The investigators will also measure the mass of the neutrino, the last detected subatomic particle without a known mass, and attempt to understand why different measurements of the current speed with which the Universe is expanding disagree. This project will provide training and research opportunities for graduate and undergraduate students in computational cosmology, and it will support professional development for high school physics teachers. The team will use the PRIYA simulation suite, a comprehensive model for the hydrogen distribution that combines simulations at different resolutions to achieve unprecedented dynamic range. PRIYA has demonstrated a consistent primordial power spectrum measurement between the cosmic microwave background and the Lyman-alpha forest, and has found matter clustering in agreement with weak lensing surveys but lower than the cosmic microwave background, revealing a S8 tension at redshift two. The PRIYA simulations possess the resolution and accuracy to analyze DESI hydrogen data, whose small-scale information will dramatically improve cosmological parameter constraints. The project will combine the PRIYA simulation suite with DESI data and cosmic microwave background data from Planck to address three key goals: measuring the sum of neutrino masses, constraining spectral index running, and testing whether early dark energy models can resolve the Hubble tension. Forecasts suggest this work will detect the first cosmological evidence for non-zero neutrino mass and improve existing constraints on spectral index running. The team will also incorporate high-resolution data from KODIAQ/SQUAD, enabling simultaneously constrained analysis of both cosmology and thermal history. The investigators will further develop the wavelet scattering transform they pioneered as a statistic for Lyman-alpha forest cosmology, which preliminary results suggest may improve cosmological constraints by an order of magnitude. 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

Separating a mixture of many chemical species into its constituents is a common operation in the chemical industry. One separation method is to adsorb molecules of the desired species onto the surface of an adsorbent. Later, the same molecules can be desorbed and recovered. The efficiency of the separation could be improved by modifying the adsorbent for each desired separation. Promising adsorbents can be identified by screening vast “libraries” of possible candidates using a computer. However there is a major hurdle: the attractive forces between the desired molecule and each candidate adsorbent in the library are unknown. This project will develop machine learning tools to rapidly predict the forces. These predictions will be used to identify optimal adsorbents for some industrially important separation processes. The results of this project will improve the efficiency of adsorptive separation processes. Additional benefits will come from outreach to high school students through summer research programs, to professionals via online courses, and to broad audiences through an educational video. Chemisorption is the adsorption of a molecule on a surface accompanied by the formation of chemical bonds. Chemisorption is relevant to many separation processes that are based on adsorption/desorption using an adsorbent surface. A major ongoing research effort is the computational design of adsorbents that can selectively adsorb desirable species out of a mixture. A crucial step in such computations is to develop interaction potentials to describe the chemisorption between the molecule and the surface. This project seeks to reduce the computational effort to develop such interaction potentials. Simulations will be conducted to obtain interaction potentials between specific molecules and adsorbent surfaces. These data will be fed to a machine learning model to predict the interaction potential between the same molecules and other adsorbent surfaces. The central hypothesis to be tested is that adsorbent surfaces can be treated as “building blocks” so that the interaction potentials are transferrable from one adsorbent to another with similar building blocks. This project will dramatically boost data generation capabilities to train artificial intelligence frameworks for adsorbent discovery. The project will rapidly screen hundreds of thousands of metal organic frameworks to identify adsorbents that can separate mixtures of small molecules such as ethane/ethylene, ammonia/nitrogen/hydrogen, etc. Separation of such mixtures is immensely important in the chemical industry. 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

Turbulent motion of fluids appears in many important settings, including weather systems, ocean currents, aircraft flows, wind energy, and industrial mixing. Turbulence looks extremely complicated with swirls of many different sizes. This project addresses a fundamental puzzle: why does violent, intense turbulence often result in little net energy transfer between swirls of different sizes? The reason is that energy does not move one way. Instead, it moves forward and backward between large and small swirls, resembling a tug-of-war where both sides pull hard, but the rope barely moves. The detailed mechanisms of energy transfer are not captured in simplified statistical tools often used to study turbulence. By analyzing large amounts of data from lab experiments and computer simulations with data-enabled learning, this research will uncover the hidden rules that control this balance. The results will help build better models for predicting weather and designing safer engineering systems. The project will also train students in engineering, data science and physics, and provide free software tools to the public. The project will support the use of AI and machine learning in fluid dynamics and will benefit advanced manufacturing of transportation vehicles. Current turbulence theory relies on simplified statistical tools, leaving the mechanisms of energy transfer across scales only partially understood. To address this critical gap, this project develops a data-enabled, physics-guided framework to uncover and model the processes controlling the cascade. The research focuses on two fundamental but unresolved phenomena: "self-competition," where strong physical-space advection paradoxically suppresses scale-to-scale energy transfer, and "weak asymmetry," where intense forward and backward flux events nearly cancel to produce a weak net cascade. The overarching goal is to quantify these mechanisms and translate this physical understanding into improved predictive models for Large Eddy Simulation. To achieve this, the project will analyze large, time-resolved datasets from three-dimensional Direct Numerical Simulations and high-resolution two-dimensional laboratory experiments. The methodology employs advanced data-driven diagnostics to identify the origins of the net energy flux. These insights will then be operationalized in a diagnostic-aware spatio-temporal Graph Neural Network framework to create accurate, interpretable subgrid-scale closures. Results will reveal a mechanistic understanding of “self-competition” and “weak asymmetry” phenomena and provide a principled pathway to regularize turbulence models. The project will produce open software and curated datasets, train graduate and undergraduate students at the interface of physics and data science and deliver transferable methods for other complex multiscale systems in engineering, physics, and beyond. 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

This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research grant supports fundamental research to enable automatic discovery of constitutive laws for soft functional composites using symbolic artificial intelligence (AI). Soft functional composites are essential to emerging technological and economic areas such as stretchable and wearable electronics, soft robotics, and sensing and actuation. Conventional methods for constitutive law discovery are usually time-consuming, inefficient, and often ineffective for complex cases. This project will significantly accelerate the constitutive law discovery process by leveraging symbolic AI technology not only for soft functional composites but also other material classes. The software tools developed will be made available to the research and education communities through a website. This project will also train undergraduate and graduate students, including underrepresented groups, workforce in the transdisciplinary area of AI, mechanics, and materials science. The project will establish a symbolic AI framework for discovering physically interpretable constitutive laws of soft functional composites, involving general tensor functions of constitutive laws, both scalar and tensor-based operators, and statistical analysis for noisy data. The project will establish general tensor functions of constitutive laws for different anisotropic materials using modified representation and symmetry theories. The symbolic-AI architecture will be built upon genome representation, physics constraints, symbolic tree search, and statistic modeling, and will be applied to discover and interpret mechano-physical constitutive laws of soft functional composites with various microstructures. The tool and the insights it provides will expedite the design and applications of soft functional composites, and eventually other material classes. 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

This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research project will contribute to the progress of science and the advancement of national prosperity by developing a framework for the inverse design and fabrication of multiphase composite materials with tailored mechanical properties. Despite recent advances in the deployment of machine learning techniques to materials science, the creation of materials with desired mechanical properties in multiple loading directions remains a significant challenge. This research plans a new data-driven framework to understand the relationship between material architecture and mechanical behavior, facilitating the design of nonlinear materials for a wide range of applications such as lightweight structures, shock absorbers, and aerospace components. This research will be integrated with educational and outreach programs aimed at attracting underrepresented groups to engineering and improving undergraduate and graduate learning in data-driven science and engineering. High school students and the public will be introduced to data-driven material design and applications in collaboration with a local museum and science center. This collaborative research will create and test a new physics-informed deep learning (PIDL) framework to tailor the multidirectional or multi-objective mechanical properties of exotic composite materials. It will utilize the principles of PIDL to build a data-efficient and physically interpretable surrogate model of structure-property relationships for multiphase composite materials. This research will formulate constitutive equations for constituents in voxel-based composite materials and incorporate them into a forward physics-informed convolutional neural network model. A novel multi-objective inverse physics-informed conditional diffusion model will be developed to reveal the property-structure correlation between a multiphase composite material’s bulk mechanical properties and its architecture, combining macroscopic and microscopic data to enhance model accuracy and robustness. Finally, the designed materials will be additively manufactured and tested, with validation through advanced additive manufacturing, X-ray imaging, and multiaxial testing. 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.

NCIPC - National Center for Injury Prevention and Control

Project Summary Interpersonal community violence (ICV; e.g., assaults, homicides, violence between groups, and threats/use of weapons) is a public health crisis among American Indian/Alaska Native (AI/AN) peoples including youth in the U.S. Lakota youth who reside on the rural and impoverished Pine Ridge Reservation (PRR) are no exception. Despite research documenting contextual (e.g., structural racism, colonization and multiple historical traumas), risk (e.g., alcohol and drug use), and protective (e.g., cultural identity) factors for ICV among AI/AN youth, we know little about how to prevent ICV among AI/AN youth. A potentially promising approach to prevent ICV in tribal communities is crime prevention through environmental design CPTED. CPTED integrates physical (e.g., lighting) and, more recently social (e.g., community engagement) strategies to prevent crime, including ICV. Busy Streets Theory (BST) is a conceptualization of CPTED focused on local community engagement that fosters community ownership and allows for tailoring physical changes to community interests and culture, and creating positive social capital. BST aligns strongly with Lakota cultural teachings. In response to CDC RFA- CE-25-021 (Objectives 1, 2, and 3), the purpose of the current study is to examine how a place-based, community-led, and culturally CPTED activities may prevent and reduce ICV on the PRR. Using community- based participatory action research (CBPAR), we will develop (Aim 1) the CPTED project, preliminary entitled The Wancantognaka Project (The Generosity Project; TGP). In addition to culturally grounded CPTED projects (e.g., community gardens with traditional Lakota methods and foods, building inipis (sweat lodges) for ceremony), TGP will include program sessions for youth (ages 13 to 19) taught by Lakota adults, including Elders. Further, using the RE-AIM framework, we will collect implementation and community acceptance and perceived impact data and document costs (Aim 2). We will also assess youth outcomes (Aim 3) via surveys (baseline, immediate post, and 6- and 12- month follow-ups) that assess risk (e.g., 7 C’s of positive youth development, cultural identity, social support) and protective (e.g., alcohol and drug use, violent behaviors) factors. Finally, using police incident data, we will assess the impact of CPTED on rates of community violence (Aim 4). For Aims 3 and 4, we will utilize a longitudinal quasi-experimental design in which the community on the PRR implementing CPTED will be compared on youth (ages 13 to 19; N=320; 160 per community) and community outcomes (crime rates 48 bi-weekly intervals pre TGP and 24 bi-weekly intervals post TGP) to another community on the PRR (similar in terms of demographics and crime rates) not receiving CPTED. The proposed study is highly aligned with the Healthy People 2030 priority area of injury and violence prevention as well as the NCIPC’s goal to conduct rigorous research to expand and advance an understanding of approaches to prevent ICV and to eliminate racial inequities in risk for ICV.

NCIPC - National Center for Injury Prevention and Control

Abstract The proposed project aims to test the short- and long-term effects of a hospital-initiated, community-integrated intervention and examine how social contexts influence its adoption and sustained effects. This study will be conducted in collaboration with the Spirit of Charity Trauma Center to implement a hospital-initiated motivational interviewing (MI) approach augmented by firearm safety training (FST) and monthly case management. We will employ a randomized control trial, to test the efficacy of the prevention approach (MI/FST) compared to treatment as usual (TAU) control condition. The scientific premise is that the intervention will be more effective in changing firearm-related behaviors and beliefs than the control at 6-months while meeting the needs of victims of violent injury. Firearm violence disproportionately occurs in black communities and hospitalized victims of firearm injury are at increased risk for violent injury reoccurrence. This calls for community-integrated, evidence-based approaches that identify critical barriers to prevention, including the need to test the effectiveness of hospital- initiated violent injury prevent program (HVIP) and examine how social contexts influence HVIP adoption and sustained effects. This proposal is quite feasible to complete because it leverages our ongoing community- integrated youth HVIP through our Violence Prevention Institute, including a CDC-supported Youth Violence Prevention Research Center (U01CE003384), currently being conducted in youth aged 18-24 in New Orleans. Our aims are to 1) establish the effects (at 6 months) of an expanded hospital-initiated, community-integrated intervention on firearm related behaviors and beliefs amongst adults ages 16-34, 2) to establish the effects (at 6-months) of the MI/FST intervention on meeting the needs of traumatically injured victims of violence that allow them to avoid violent injury recurrence, and 3) to understand and analyze stakeholders’ perspectives and experiences with the MI/FST intervention components and its implementation. Results will continue and expand a novel HVIP aimed at mitigating recurrent firearm injuries in New Orleans—particularly in largely black communities—and inform future studies on the root issues underlying firearm violence recurrence nationwide. This research is significant because it will expand our understanding of the efficacy of community-based HVIPs in New Orleans that decrease the risk of recurrent violent injury in a growing and underappreciated, high-risk population that has already been hospitalized with a firearm injury. This proposal addresses the RFA’s first primary objective by testing the effectiveness of an innovative approach with the potential for immediate and near immediate benefits to reduce community violence and racial/ethnic inequities in risk for community violence.

NCIPC - National Center for Injury Prevention and Control

PROJECT SUMMARY/ABSTRACT Community violence (CV) is a pressing public health crisis in the United States (US), disproportionately affecting youth and young adults (YYA) ages 10-34. Racial and ethnic minority YYA experience a hugely disproportionate burden of CV, with non-Hispanic Black YYA nearly 10 times more likely to die from homicide compared to their non-Hispanic white peers. Despite recognition of violence as a public health issue, many jurisdictions rely on law enforcement to respond to crises and conflicts among individuals and between community members. Yet the use of law enforcement can escalate conflicts between community members (particularly between YYA) that could otherwise remain nonviolent, introduce risks of violence and arrest to YYA, and contribute to racial and ethnic disparities in enforcement. To prevent CV while mitigating these risks, some US cities have adopted non- enforcement community-centered safety models (CCSMs) to use community responders to de-escalate crises and conflicts before violence occurs and address underlying behavioral and social drivers of CV. CCSMs— including co-response models, which send trained community responders to calls for service alongside police; alternative response models, which send trained community responders to calls for service without police; and community violence interventions, though which trained community members proactively intervene in conflicts before escalation to violence—represent promising approaches to prevent CV among YYA. However, limited evidence exists on the effectiveness of CCSMs to prevent CV among YYA, the factors influencing the success or failure of CCSMs, and how CCSM modalities impact CV across diverse communities. This study will fill that gap. Aim 1 will use an implementation science approach to characterize CCSM operations in 15 US jurisdictions, documenting program characteristics, funding mechanisms, and scope to facilitate impact evaluation. Aim 2 will use an augmented synthetic control approach to estimate the overall and modality-specific impacts of CCSMs on a range of CV outcomes among YYA. Aim 3 will use feature importance analysis with Bayesian additive regression trees to explore which CCSM operational factors drive strong or weak estimates of CCSM effectiveness. This innovative research is directly responsive to the CDC’s Violence Prevention Research Priority to evaluate the effectiveness of community-level change strategies to reduce multiple forms of violence. This research is directly responsive to Research Objective 1 of the current NOFO through its focus on the evaluation of an innovative but understudied intervention to prevent CV with the potential for immediate benefit after implementation. In sum, by generating critical evidence about CCSMs, this project has the potential to reduce CV among YYA, inform policymakers as they design and deploy CCSMs in local jurisdictions, and promote health equity through innovative, evidence-based prevention strategies. The multidisciplinary investigator team will work with a Community Advisory Board to maximize the impact and reach of the research.

NCIPC - National Center for Injury Prevention and Control

PROJECT SUMMARY/ABSTRACT Social media (SM) use is associated with disengagement from school and poor mental health, and can facilitate inter-personal and community violence, including cyber-bullying which is highest among LGBTQ+ adolescents (25%) and female (21%) students. Digital apps have been used to document and share recordings of fights, create escalation of conflicts, and be used to create direct and indirect threats. Given these negative impacts, a number of school districts across the country have implemented policies that monitor, restrict, or ban cellphone use in schools. Policies vary greatly both in their objectives and enforcement. Despite the ubiquity of such policies, there are no studies that explore how effective cellphone bans are at reducing school- and community-based violence let alone their potential unintended consequences such as limiting the ability of students to report concerns about themselves or others. The proposed project will examine school cellphone policies in Michigan using a mixed method approach to assess both implementation and impact, with a particular focus on how such policies differentially impact communities of color across the state. This research falls under CDC Research Objective #1 (effectiveness research to evaluate innovative approaches for reducing community violence) and has direct policy implications for districts in Michigan and across the country seeking to restrict school cellphone use. Using a combination of interview, survey, and administrative data sources, researchers will describe state-level variability in district student cellphone policies, understand the barriers to and facilitators of implementation and estimate their impact on indicators of community violence, including fights in school, school discipline, and police incidents. The impact analysis will utilize several quasi-experimental methods including difference-in-difference and comparative interrupted time designs to estimate causal impacts of the policy. Leveraging rich administrative data on school- and community-based violence along with youth demographics and community characteristics from the Michigan Department of Education and the Michigan State Police will allow researchers to control for a variety of potentially confounding factors and study critical outcomes at the school level. Importantly, access to this type of individual-level outcome data will allow the researchers to convincingly assess if and how cellphone policies differentially affect racial/ethnic groups and other important subgroups (e.g., urban, suburban and rural schools). Moreover, this data will allow us to study how the cellphone policies impact mental health and academic outcomes and examine whether these effects mediate impacts on violence.

NCIPC - National Center for Injury Prevention and Control

ABSTRACT Community gun violence (assault or homicide) is the leading cause of firearm injury and death in urban youth in the United States, yet upstream primary prevention efforts are lacking.1-3 Risk factors for community gun violence include aggression and violent conflict resolution, gun carrying, and living in socially and economically disadvantaged neighborhoods with high prevalence of violence.27-30 Youth are not always equipped to make smart or safe decisions regarding fighting, retaliation, and gun use. Additionally, they are easily influenced by social perceptions regarding gun carrying, especially vulnerable in communities where they could easily be victims of violence.20 Mentorship and conflict resolution skills for high-risk youth provided by school-based or community violence interruption programs are effective at reducing community gun violence; however, these services are limited resource-intensive.9-11,36,37 We propose development of a video-based program (“Rise Up Against Gun Violence”) that is informed by youth ambassadors with lived experience and leverages the expertise of violence interrupters to influence adolescents to avoid risky behaviors that lead to community gun violence. The video topics and messaging will be developed by the youth ambassadors and a multidisciplinary team of content experts, and refined through community feedback. The series will focus on risk-reduction topics such as non-violent conflict resolution, avoiding retaliation, and posting safely on social media, and will deliver hard facts about the dangers of firearm injuries and other salient topics that youth identify. Our study design includes youth ambassadors and violence interrupters from two cities with high levels of community gun violence (Houston and Memphis) to work with experts to develop a series of videos. The video program intervention will be tested with youth cohorts (community testers) in diverse settings in high-risk communities in both cities, and a mixed-methods approach will assess attitude change about the consequences of gun carrying and use (primary outcome), behavior change in gun carrying and fighting, increased awareness regarding risks of gun use, and establishment of adult support (secondary outcomes). Our proposal is a novel and innovative, multi-faceted approach, a “plug- and-play” program that could be disseminated to diverse community youth programs to utilize as part of their programming to tackle primary prevention of the leading public health problem for American youth.

NCIPC - National Center for Injury Prevention and Control

1 A substantial body of research has demonstrated the long-term negative impacts of adverse childhood 2 experiences (ACEs). Recently, growing evidence has highlighted the beneficial effects of positive childhood 3 experiences (PCEs) on both health and socioeconomic outcomes, as well as their potential to mitigate the 4 harm caused by ACEs. Despite this, disparities persist, with children from marginalized communities being 5 more likely to experience ACEs and less likely to have access to PCEs. This underscores the urgent need for 6 policies that prevent ACEs, promote PCEs, and address inequities. One potential solution is paid family and 7 medical leave (PFML), which enables parents to take time off from work to care for newborns, or sick family 8 members while retaining a portion of their income. PFML provides both economic security and an opportunity 9 for parents to bond with their children. To date, 14 states have either already implemented or are preparing to 10 introduce PFML policies. Using a difference-in-differences (DiD) statistical approach, this study aims to 11 rigorously assess the impact of PFML on two ACEs, abusive head trauma (AHT) and poor maternal mental 12 health, and two PCEs, family relationships and family resilience. To strengthen the robustness of our findings, 13 we will conduct a placebo falsification analysis with a triple-differences approach. In addition, the National Child 14 Abuse and Neglect Data Systems (NCANDS) annual reports on child maltreatment will be used as a 15 secondary measure of child abuse and neglect in the first year of life. Utilizing the NCANDS data, will 16 supplement the findings on the impact of PFML on AHT, as the frequency of AHT hospitalizations is low. The 17 study will also analyze the effects of PFML on racial/ethnic and socioeconomic disparities in these outcomes. 18 Additionally, specific elements of PFML policies, such as benefit duration and the presence or absence of 19 progressive wage replacement and job protection provisions will be analyzed to evaluate their effects on ACEs 20 and PCEs. As more states consider or implement PFML, this research will provide valuable insights into the 21 effectiveness of such policies in preventing ACEs, fostering PCEs, and elucidate the importance of specific 22 policy features. The exploratory aim will evaluate whether the effectiveness of PFML in preventing ACEs, 23 promoting PCEs, and reducing racial, ethnic, and socioeconomic disparities varies depending on the presence 24 of other state-level family-supportive policies that bolster family income, reduce expenses, and offer 25 reasonable accommodations for pregnant or postpartum workers.

NCIPC - National Center for Injury Prevention and Control

Project Summary/Abstract This study brings together researchers and subject matter experts from NORC at the University of Chicago and George Washington University with experience using innovative, integrated mixed research methods that combine systems and complexity informed evaluation (SCIE) and culturally responsive equity evaluation (CREE) approaches to evaluate community-level primary prevention interventions. In this project, the research team will evaluate the Mississippi-based program, LINK. LINK is a community-level initiative to build community capacity to prevent childhood adversity and maltreatment and promote health equity in Mississippi by strengthening local primary prevention ecosystems of social and economic services and supports. This project will fill a gap in the current community change literature by identifying the effectiveness of LINK— an existing primary prevention approach that addresses community-level conditions and strengthens economic supports to families—in preventing adverse childhood experiences (ACEs) at the county level. This objective will be met by accomplishing the following aims: Aim 1: To evaluate the systems-strengthening capabilities of the community approach, LINK, and identify variations in LINK implementation and structure across Mississippi counties Aim 2: To evaluate the impact of LINK on county-level child abuse and neglect (CAN) and domestic violence (DV) rates Aim 3: To evaluate the role of community capacity as a mediator of the effect of LINK on county-level CAN and DV To address Aim 1, this study will administer a survey of community capacity across the state of Mississippi, document and describe LINK implementation, and conduct a thorough program and policy document and data review. To meet Aim 2, this study will conduct county fixed effects outcome models using child welfare administrative data to determine the effectiveness of LINK in reducing rates of child abuse and neglect and domestic violence at the county level across Mississippi. To address Aim 3, the results of the state-wide survey will be used to measure community capacity at the county-level, and this measure will be included in outcome models as a mediator of the impact of LINK on CAN and DV rates. Finally, to address Aim 1 & 2, qualitative, semi-structured interviews will be conducted to gain a deeper understanding of the outcomes identified in previous analyses, and LINK’s connection to those outcomes, through a process called outcome harvesting. These interwoven study activities are rigorous and equitable, ensuring community voice is present at all phases of the research with a focus on understanding the role of building community capacity in prevention of adversity and promotion of health equity at the community level.