Topic #: CW26-T18 · Branch: DOE · Status: Open
The Department of Energy's (DOE) Basic Energy Sciences (BES) program supports fundamental research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels. By focusing on the discovery of new materials and the design of new chemical processes, BES provides the essential foundations for innovative energy technologies. These research disciplines serve as a critical knowledge base and an agent of change, supporting vital DOE missions across energy, the environment, and national security to achieve a secure energy future.
Posted: 10/30/2026 · Opens: 10/30/2026
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The Department of Energy's (DOE) Basic Energy Sciences (BES) program supports fundamental research to understand, predict, and ultimately control matter and energy at the electronic, atomic, and molecular levels. By focusing on the discovery of new materials and the design of new chemical processes, BES provides the essential foundations for innovative energy technologies. These research disciplines serve as a critical knowledge base and an agent of change, supporting vital DOE missions across energy, the environment, and national security to achieve a secure energy future.
Super intelligence (SI) and machine learning (ML) offer an opportunity to accelerate these discoveries by enabling researchers to uncover and integrate underlying chemical and physical principles across complex parameter spaces. This topic focuses on utilizing SI/ML to develop predictive frameworks and data-driven platforms that can guide the design, synthesis, and characterization of advanced materials and chemical processes. Achieving this ambitious goal requires first addressing the foundational challenge of managing the complex, heterogeneous data needed to train such predictive models. Therefore, this topic supports R&D that addresses the intertwined challenges of developing predictive frameworks and the data infrastructure required to enable them.
The BES program is seeking proposals that address the specific application area identified below, as well as enabling SI-powered data management frameworks. Proposals must explicitly address at least one of the goals below to be responsive to this topic, but applicants are encouraged to address both goals. A successful Phase I project should de-risk the core technical challenges, for example by demonstrating the successful ingestion of heterogeneous data into a prototype schema and showing a preliminary predictive relationship between that data and a key membrane property.
Areas of Interest Specific areas of interest include the following:
a. SI/ML-Guided Molecular Membrane Development for CMM/REE Separations: The separation of critical minerals and materials (CMM) and rare-earth elements (REE) using organic or organometallic polymeric membranes typically relies on three fundamental mechanisms: (1) complexation of CMM or REE ions with extractants embedded in the membrane matrix, (2) adsorption of CMM or REE ions onto surface-active sites on a membrane, and (3) membrane rejection of CMM or REE ions or complexes utilizing organic functional groups. This topic seeks to optimize separation selectivity and extraction or rejection rates by leveraging SI for membrane synthesis, experimental design, and data analysis.
Goals and Metrics • The overarching goal of this topic is to develop physical or hybrid data/physical models and design protocols that predict membrane structure and performance, supported by experimental validation of a selected subset of predicted structures. Successful projects must lead to hybrid physical/SI models that incorporate these fundamental mechanisms to design feasible, scalable membrane structures. These structures must be viable for large-scale processes and complex systems (e.g., mine tailings, reprocessed materials, seawater). Recent literature (see references 1 and 2) highlights opportunities to transition fundamental research into applied technologies for CMM and REE separation from complex solutions. • Platform materials should consist of synthesizable organic or organometallic polymers functionalized to optimize selectivity, throughput, and stability. An SI supervisory algorithm should down-select candidate structures based on synthesizability and chemical stability in solution, utilizing empirical data from established structural families such as metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs). Additionally, the models and design protocols must be extensible with the ability to identify parameters and features that distinguish between closely related structural families.
Not of Interest Topics focused on engineering design, sensors, or routine synthesis and fabrication are excluded from this topic.
References 1. National Academies of Sciences, Engineering, and Medicine, Meeting Future U.S. Mineral Resource Needs: The Role of the U.S. Geological Survey Mineral Resources Program (The National Academies Press 2019), https://doi.org/10.17226/29068. 2. National Academies of Sciences, Engineering, and Medicine, A Research Agenda for Transforming Separation Science (The National Academies Press 2019), https://doi.org/10.17226/25421.
b. SI-Powered Data Management Platforms: Develop flexible, SI-enabled data platforms to support the synthesis and characterization workflows of molecular membranes structures described above.
Goals and Metrics • The platform must be able to ingest and manage heterogeneous data types along with unstructured metadata. • A highly desirable feature is the inclusion of SI-assisted tools for metadata population, such as parsing information from photos of lab notebooks or transcribing audio notes. The platform should provide a suite of standard machine learning analyses (e.g., clustering, embedding) and, most importantly, allow users to easily search, extract, and use the curated data to build targeted SI/ML applications. • The final product is expected to adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. • Proposals seeking to develop a data management platform with a clear and direct application to a specific synthesis and characterization target application are strongly encouraged.