The Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program is a funding opportunity issued by the Advanced Research Projects Agency – Energy (ARPA-E), a division of the U.S. Department of Energy. ARPA-E was created under the America COMPETES Act and aims to advance the economic and energy security of the United States by promoting innovative and potentially transformative energy technologies. The agency supports early-stage research and development efforts that seek to establish new learning curves, especially those with a high degree of technological risk and a potential for significant market disruption.
The CATALCHEM-E initiative specifically targets innovation in heterogeneous catalyst research and development (R&D), a field critical to the fuel and chemical industries. Traditional catalyst R&D workflows typically take 10 to 15 years to progress from conceptual material design to pilot-scale testing. This program seeks to compress that timeline to just 12 to 18 months by integrating advanced high-throughput experimentation (HTE) with cutting-edge artificial intelligence (AI) and machine learning (ML) techniques. These new workflows are intended to significantly accelerate the discovery, development, and optimization of catalysts used in producing low-carbon fuels and chemicals.
Projects funded through CATALCHEM-E will span two phases, each lasting 18 months. Phase 1 focuses on developing accelerated workflows using reference chemistries that are already validated at commercial scale. Phase 2 leverages those tools for novel catalyst-reaction discovery and co-design. Applications must propose plans for both phases. The program emphasizes creating AI-enabled closed-loop or otherwise disruptive workflows that enable inverse catalyst design, incorporating real-world technical catalyst performance testing, simulation, and database development.
The scope of allowable activities includes synthesis of technical catalysts at the kilogram scale, their validation under industrial conditions, generation of FAIR (Findable, Accessible, Interoperable, and Reusable) datasets, and development of multi-scale modeling techniques. Awardees must also build integrated catalysis databases and develop AI/ML models that can both predict catalyst performance from material properties and suggest new compositions from performance data. Required application components include concept papers, full applications with detailed technical volumes, budgets, workflow schematics, and biosketches, among other materials. Concept paper deadlines have passed, and the full application deadline is February 25, 2026, at 9:30 AM ET.
ARPA-E requires all applicants to register and submit applications via the ARPA-E eXCHANGE portal. Applications submitted via other means will not be reviewed. Full applications must demonstrate eligibility, include signed letters of intent from catalyst manufacturers and data service providers, and show compliance with technical performance metrics defined in the NOFO. Awards are cooperative agreements or other types depending on applicant type, with cost-sharing requirements in place. The anticipated period of performance runs from July 2026 to July 2029.
ARPA-E anticipates awarding approximately 10–12 grants between this program and its companion SBIR/STTR FOA, with awards ranging from $2.5 to $3.5 million and an estimated total program funding of $35 million. Applicants are encouraged to form interdisciplinary teams that include expertise in HTE, AI/ML, catalyst manufacturing, and technical testing. The program encourages proposals that challenge conventional practices and enable scalable, manufacturable solutions aligned with national goals for energy efficiency and greenhouse gas reduction.
Highlight strong AI/ML integration and closed-loop workflow design; ensure data quality and high-throughput capability are demonstrated.
