Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics | 24 567

Opportunity Information: Apply for 24 567

The Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics (MFS-SPEED) program is a National Science Foundation (NSF) cross-directorate grant opportunity designed to speed up the discovery, design, and eventual manufacturing of sustainable polymers by tightly integrating polymer science with modern data analytics. The call is positioned as a response to federal priorities laid out in the National Defense Authorization Act for FY2021 and the 2022 CHIPS and Science Act, signaling an emphasis on strategic, nationally relevant research capacity. NSF is running the program through its Directorates for Mathematical and Physical Sciences (MPS) and Technology, Innovation, and Partnerships (TIP), with additional involvement from major industry partners Procter and Gamble, PepsiCo, BASF, Dow, and IBM. In practice, that mix of NSF science directorates plus well-known corporate partners points to a program that wants deep fundamental research, but with a clear line of sight to scalable, real-world polymer development.

At its core, MFS-SPEED aims to build a data-centric research environment for polymers and advanced soft materials, where teams use state-of-the-art data science to shorten the time between an idea and a usable material. The solicitation highlights projects that develop new approaches for predicting polymer structure and properties, including relationships among chemistry, morphology, processing, and performance. A major focus is on extracting insights using data analytics, specifically including artificial intelligence and machine learning, to guide materials design and decision-making. The program also explicitly welcomes work that improves the efficiency and scalability of making monomers and polymers, whether by optimizing existing synthetic routes or inventing new ones. In other words, proposals are expected to connect the full chain from molecular design and property prediction to practical preparation methods, rather than treating computation, synthesis, and scale-up considerations as separate worlds.

Another central goal is workforce development. MFS-SPEED is not only about producing new polymer candidates; it is also about training researchers who can operate comfortably across polymer chemistry, soft matter physics, data science, and modern analytics. The program is looking for cross-disciplinary teams that can transcend traditional single-investigator or single-discipline boundaries, reflecting the reality that sustainable polymer innovation now depends on coordinated experimental, computational, and data-driven workflows. A competitive project under this call would typically involve collaborative research where data generation, curation, modeling, and experimental validation reinforce one another, and where trainees gain hands-on experience with those integrated methods.

From a funding and schedule standpoint, MFS-SPEED will support research grants with awards anticipated in FY24/FY25. Individual awards can be as large as $2 million total, with project durations of up to three years, and budgets should be aligned with the scope of work and the size of the collaborative team. NSF expects to make around six awards, which suggests a competitive program favoring well-justified, ambitious team efforts with a strong technical plan, clear data/analytics integration, and credible pathways to sustainable polymer outcomes. The opportunity is listed as a discretionary grant under NSF funding activity categories for science and technology and other research and development, with CFDA numbers 47.049 and 47.084. The funding opportunity number is 24-567, and the original closing date is 2025-01-16.

Eligibility is limited to specific types of U.S.-based organizations. Proposals may be submitted by U.S. institutions of higher education, including both two-year and four-year accredited colleges and universities (community colleges included), acting on behalf of their faculty. Non-profit, non-academic organizations are also eligible, such as independent museums, observatories, research laboratories, and professional societies located in the United States, as long as they are directly associated with educational or research activities. Federally recognized Tribal Nations are eligible as well, consistent with the Federally Recognized Indian Tribe List Act of 1994. The solicitation includes a specific note about international branch campuses of U.S. higher education institutions: if any project funding would support work at an international branch campus (including via subawards or consultants), the proposal must explain why performing that work abroad benefits the project and why the activities cannot be done at the U.S. campus.

Overall, MFS-SPEED is a targeted NSF program for teams aiming to reinvent how polymers are discovered and improved for sustainability, using advanced data analytics as the engine for faster learning cycles and better predictions. It encourages proposals that unite polymer synthesis and processing, property measurement, theory and simulation, and AI/ML-driven analytics into a coherent, shared, data-rich workflow, while also producing a workforce trained to operate at that intersection.

• The National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 47.049, 47.084.
• This funding opportunity was created on 2024-04-29.
• Applicants must submit their applications by 2025-01-16. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• The number of recipients for this funding is limited to 6 candidate(s).
• Eligible applicants include: Others.

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