Data Science to Advance Chemical and Materials Sciences | DE FOA 0002474

Opportunity Information: Apply for DE FOA 0002474

The Department of Energy (DOE) Office of Science, through the Basic Energy Sciences (BES) program, is seeking new grant applications under the funding opportunity titled "Data Science to Advance Chemical and Materials Sciences" (DE-FOA-0002474). The central purpose of this opportunity is to push chemical and materials research forward by deeply integrating modern data science approaches, especially artificial intelligence and machine learning, into BES-supported scientific disciplines. The program is aimed at speeding up discovery and tackling long-standing, difficult problems in basic energy sciences by using data-driven methods in ways that traditional approaches cannot.

This FOA is designed specifically for teams of investigators rather than single-investigator projects, with an emphasis on synergy across computational, experimental, and theoretical efforts. DOE is looking for proposals where these components reinforce each other, for example by using experiments to generate high-value datasets, theory to encode physical constraints or interpret results, and computation and ML to identify patterns, build predictive models, and guide the next round of measurements or simulations. The supported science must fall within BES research areas housed in the divisions of Chemical Sciences, Geosciences, and Biosciences (CSGB) and Materials Sciences and Engineering (MSE), meaning proposals should be clearly anchored in fundamental chemical and materials phenomena relevant to energy science rather than purely applied product development.

A key expectation is that the research is science-based and genuinely data-driven, using AI/ML and related methods as enabling tools to solve fundamental challenges that would be impractical or impossible to address otherwise. The FOA highlights the integration of uncertainty quantification alongside AI/ML, signaling that DOE is not only interested in accurate predictions but also in understanding confidence, error, and model limitations, particularly when systems operate under changing or dynamic conditions. Successful projects are expected to advance fundamental understanding of properties and processes in chemical and materials systems and move the field toward reliable predictability of function and behavior, especially in regimes where conditions vary over time, environment, or operating state.

In practical terms, strong applications would likely demonstrate a clear plan for how data will be generated, curated, and used; how models will be trained and validated; how physical insight will be incorporated so results are scientifically interpretable; and how uncertainty and robustness will be quantified to support trustworthy predictions. The opportunity is open to unrestricted eligible applicants. It is a discretionary grant program under CFDA 81.049, with an award ceiling of $1,200,000 per award. The FOA was created on March 19, 2021, and the original application closing date was June 8, 2021, with the DOE Office of Science listed as the issuing agency.

• The Office of Science in the science and technology and other research and development sector is offering a public funding opportunity titled "Data Science to Advance Chemical and Materials Sciences" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 81.049.
• This funding opportunity was created on 2021-03-19.
• Applicants must submit their applications by 2021-06-08. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Each selected applicant is eligible to receive up to $1,200,000.00 in funding.
• Eligible applicants include: Unrestricted.

Apply for DE FOA 0002474

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