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Large language model guides discovery of catalysts for clean energy tech | EurekAlert!
Researchers at Tohoku University developed ChatHEA, a domain-specific AI assistant that combines large language models with high-throughput experimentation to accelerate discovery of high-entropy alloy catalysts for fuel cells. The framework identified FeCoCuPtIr as a superior oxygen-reduction catalyst that exceeds U.S. DOE 2025 activity targets and outperforms commercial platinum catalysts.
This Wire brief sits within Fusion42's coverage of Climate Tech. Wire is Fusion42's founder-focused intelligence feed: each story is connected to the funds and startups it names — every one with a live profile on Raise or Scout — so founders can follow the capital and the momentum behind the headline rather than just the headline itself. Wire analysis is one of the live surfaces Arthur, Fusion42's AI co-founder, reasons over.
The Wire takeaway
AI-guided catalyst discovery framework demonstrates concrete path to reduce precious-metal dependency in fuel cells while meeting regulatory energy-density targets—actionable for deeptech/cleantech founders building materials-as-a-service or catalyst optimization platforms.
Read the full story at eurekalert.org →
Topics: Climate Tech · llm-materials-science · fuel-cell-catalysts · high-entropy-alloys · oxygen-reduction · ai-experimentation