Catalytic Application Testing For Accelerated Learning Chemistries Via High-Throughput Experimentation And Modeling Efficiently (Catalchem-E)

The CATALCHEM-E funding opportunity, issued by the Advanced Research Projects Agency–Energy (ARPA-E) within the U.S. Department of Energy, aims to radically accelerate the research and development (R&D) of heterogeneous catalysts through high-throughput experimentation (HTE) and advanced artificial intelligence/machine learning (AI/ML) modeling. ARPA-E was established to fund transformative energy technologies that can significantly advance the United States' energy security, economic competitiveness, and environmental goals. This funding opportunity aligns with ARPA-E’s mission to foster disruptive innovations by compressing traditionally long R&D timelines into significantly shorter, high-impact periods. The CATALCHEM-E program seeks to overhaul the conventional catalyst development cycle, which typically spans 10–15 years. This initiative promotes the creation of automated, closed-loop workflows that combine advanced HTE with AI/ML tools to achieve verifiable R&D acceleration by a factor of ten or more. The program unfolds in two 18-month phases. In Phase 1, teams are expected to validate workflow acceleration using predefined reference chemistries. In Phase 2, the developed workflows will be applied to discover new catalyst-reaction pairs relevant to energy and emissions goals. These workflows are expected to be rigorous, replicable, and generate high-quality, multiscale databases suitable for AI/ML training and inverse design approaches. The program encourages participation from multidisciplinary teams with expertise in HTE, catalyst manufacturing, technical catalyst performance testing, and AI/ML. Applicants must demonstrate access to essential infrastructure and submit letters of intent from manufacturing and testing partners, as well as database and HTE hardware providers. Key technical requirements include performance testing at industrial scales (e.g., >2000 hours of operation), kilogram-scale synthesis of technical catalysts, and development of AI-ready databases adhering to FAIR principles. Matching performance benchmarks and economic viability compared to current commercial catalysts are critical evaluation factors. The estimated total funding available is $35 million, shared between CATALCHEM-E and a related SBIR/STTR FOA. ARPA-E expects to issue approximately 10–12 awards with individual award sizes ranging from $2.5 million to $3.5 million. A mandatory cost share of 20% applies, with some exceptions and reductions available for nonprofit institutions, small businesses, and other specified entities. The program imposes a maximum indirect cost reimbursement cap of 15% of the total project cost. Additionally, a minimum of 5% of federal funding must be allocated to Technology Transfer and Outreach (TT&O) activities, unless waived. Applications follow a staged submission process starting with a required concept paper, followed by a full application upon invitation. Key deadlines include the concept paper due date of December 17, 2024, and the full application due on February 25, 2026. Replies to reviewer comments are due by March 16, 2026. Notifications of selection are anticipated in April 2026, with award funding expected by July 2026. The period of performance extends from July 2026 through July 2029. Eligible applicants include U.S. individuals, for-profit and nonprofit entities, academic institutions, FFRDCs, and certain consortia. Foreign entities may participate only with a U.S.-incorporated subsidiary or a waiver. All work must be performed within the United States unless a waiver is granted. Applicants must also comply with restrictions concerning entities of concern and ensure comprehensive disclosure of participating individuals and organizations. Support for applicants includes access to FAQs on the ARPA-E website, and questions may be submitted to [email protected]. Submission is via ARPA-E eXCHANGE, and components such as technical volumes, biosketches (in SciENcv format), budget justifications, and required forms must be submitted in compliance with outlined formatting and content guidelines. ARPA-E maintains substantial involvement in project execution, including milestone oversight and the ability to terminate underperforming projects.

Emphasize AI/ML model validation, industrial-scale testing, and workflow automation. Showcase access to infrastructure and quality databases.