Yale Spinouts Capture Two Spots in ARPA-E’s $34M Climate Innovation Program
Two Yale-affiliated startups are among just 12 teams nationwide selected for a new $34 million ARPA-E program advancing “self-driving labs” for energy and industrial innovation. The awards signal Yale’s growing role at the forefront of AI-powered chemistry, while also reinforcing Connecticut’s emergence as a regional hub where advanced research, startups, and infrastructure converge to accelerate climate solutions.
This award comes through the U.S. Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E), which funds high-risk, high-reward energy technologies with the potential for transformative impact. Unlike venture capital, ARPA-E funding is non-dilutive, meaning companies receive support without giving up equity, allowing them to advance early-stage breakthroughs while preserving ownership. The $34 million program supports 12 teams nationwide developing next-generation “self-driving labs” that integrate AI, robotics, and automated experimentation to dramatically accelerate the discovery and scaling of industrial catalysts. For startups, this type of funding is both a financial catalyst and a strong signal of technical validation at the national level.
Yale ARPA-E Funded Projects
Oxylus Energy – Branford, CT
Project SPRINT-EC: Syngas Production via Rapid Inverse-design Network for Tailored Electrochemical
Catalysts - $2,955,391
Oxylus Energy will develop an AI-accelerated workflow to discover electrocatalysts for the conversion of carbon
dioxide to methanol. The project will use machine learning models that learn from many experiments
simultaneously, from rapid initial screens all the way through testing in commercial electrolyzers to predict
which catalysts will perform best at scale.
P2 Science, Inc. – Woodbridge, CT
HEAT FACTORY: Highly Energetic Advanced Turpentine Fuels Accessed with Catalytic Transformations
Optimized Robotically - $2,834,879
P2 Science aims to dramatically speed up the discovery of catalysts for converting plant-based feedstocks, such
as oils and resins from pine trees and citrus waste, into high-performance liquid fuels. The project will combine
advanced robotics with machine learning in an automated system to rapidly test thousands of catalyst
candidates. This approach will allow the team to identify catalysts that perform under milder, more efficient
conditions, enabling the production of fuels compatible with existing engines, including in aviation.