Insider Brief
- The U.S. Department of Energy’s ARPA-E has awarded $34 million to 12 projects to accelerate industrial catalyst development by combining AI with self-driving laboratory systems, the agency said.
- The CATALCHEM-E program aims to cut catalyst development timelines from about 10 years to one by integrating machine learning, AI-guided design and high-throughput experimentation into automated discovery workflows.
- The initiative targets up to 10x faster validation of catalysts used in fuels and chemicals production and is part of a broader push to apply AI and advanced computing to strengthen U.S. industrial competitiveness and energy systems.
The U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) has awarded $34 million to 12 projects aimed at accelerating industrial catalyst development by combining artificial intelligence with self-driving laboratory systems.
The funding, issued under ARPA-E’s Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program, targets a reduction in catalyst development timelines from roughly a decade to about one year by integrating machine learning, AI-guided design and high-throughput experimentation into automated discovery workflows, according to DOE.
The program focuses on improving the speed and efficiency of designing catalysts used in fuels and chemicals production, including systems that convert oil, gas and other feedstocks into widely used industrial products, with a goal of achieving up to a tenfold increase in development and validation speed.
The DOE said the approach replaces traditional methods that rely on sequential computational screening and lab validation with continuous, iterative systems that combine prediction, synthesis and testing, allowing researchers to evaluate catalyst performance in weeks rather than years.
The initiative is part of a broader push to apply AI and advanced computing across energy innovation, with the goal of strengthening U.S. manufacturing, energy systems and industrial competitiveness through faster commercialization of new technologies, the department noted.
Selected projects include:
University of Wisconsin-Madison – AI-FIXCAT
$2,835,000 — The University of Wisconsin-Madison will develop AI-driven workflows to design catalysts that convert ethanol into higher-value fuels and chemicals, integrating automated lab systems with industrial-scale testing. The project aims to link AI models with laboratory systems to make catalyst design more accessible for commercial use.
Ames National Laboratory – A-TEAM
$2,520,000 — Ames National Laboratory will use AI models and robotic synthesis to develop low-precious-metal catalysts for hydrocarbon processing, targeting reduced energy use and improved manufacturing efficiency. The workflow combines simulation through pilot-scale validation to ensure industrial relevance.
North Carolina State University – Self-Driving Lab
$2,992,500 — North Carolina State University is building a human-AI-robot teaming platform to discover catalysts that convert biomass and waste liquids into hydrogen-rich syngas. The system integrates self-driving lab capabilities, automated synthesis and modeling to enable scalable deployment.
National Laboratory of the Rockies – SYNTH-ON
$2,835,000 — The National Laboratory of the Rockies is developing an AI-enabled automated workflow using high-throughput flow synthesis to accelerate catalyst discovery and produce thousands of samples daily. The system will initially target reverse water-gas shift reactions before expanding to broader chemical production.
University of Connecticut – REACT
$2,916,450 — The University of Connecticut will develop AI-guided workflows to discover electrocatalysts that convert methane into alcohols for fuel and chemical production. The project uses high-throughput experimentation and commercial-scale validation to accelerate deployment.
Pacific Northwest National Laboratory – Trinity AI Platform
$2,722,500 — Pacific Northwest National Laboratory is building an automated AI workflow to identify and scale electrocatalysts that convert carbon dioxide into ethanol. The system advances top-performing candidates to industrial testing to improve real-world translation.
Idaho National Laboratory – KINETIC
$2,992,500 — Idaho National Laboratory is combining robotic synthesis with advanced testing to develop catalysts for converting hydrocarbons into high-value fuels and chemicals. The project focuses on understanding catalyst behavior over time to improve industrial adaptability.
University of Rochester – CATALYST
$2,992,500 — The University of Rochester is developing an AI-enabled workflow using large language models to accelerate catalyst discovery for converting carbon dioxide into methanol and ethanol. The approach uses text-based representations of synthesis and reactions to speed material discovery.
Argonne National Laboratory – Catalyst Design Foundry
$2,771,100 — Argonne National Laboratory is building a data-driven catalyst discovery platform combining AI, high-throughput experimentation and curated datasets to convert waste carbon into chemicals and energy carriers. The effort aims to scale AI-driven design across U.S. chemical manufacturing.
Lawrence Berkeley National Laboratory – PANDORA
$2,835,000 — Lawrence Berkeley National Laboratory is developing AI-driven reactor arrays and surface chemistry tools to design catalysts that convert carbon dioxide into industrial chemicals. The project focuses on overcoming scaling challenges from lab to kilogram-scale production.
Oxylus Energy – SPRINT-EC
$2,955,391 — Oxylus Energy is building an AI-accelerated workflow to discover electrocatalysts that convert carbon dioxide into methanol using machine learning models trained across multiple experimental stages. The system aims to predict catalyst performance at commercial scale.
P2 Science – HEAT FACTORY
$2,834,879 — P2 Science is developing an automated robotics and machine learning system to accelerate catalyst discovery for converting plant-based feedstocks into liquid fuels. The project targets lower-energy processes and fuels compatible with existing infrastructure, including aviation.
