Advanced Use Cases

Problem

Current electrolysis catalysts are expensive (platinum) or inefficient. Green hydrogen costs 3-5x more than fossil fuels due to catalyst limitations.

Solution

Discover earth-abundant, high-performance alternatives using quantum molecular simulation to model catalyst behavior at the quantum level and predict reaction pathways.

Impact

40-60% cost reduction

Market Size

$200B market by 2030

Expected Results

• •Enable 1 GW of additional green hydrogen capacity
• •$10B in cost savings for hydrogen producers
• •Replace platinum with abundant materials

Problem

Limited catalysts for converting CO₂ into useful products. Current methods are energy-intensive and economically unviable at scale.

Solution

Design selective catalysts for CO₂ reduction reactions using quantum algorithms to solve Schrödinger equations for catalyst-reactant systems.

Impact

Enable profitable CO₂ utilization

Market Size

$85B market by 2030

Expected Results

• •Capture 10M tons CO₂ annually
• •$5B in new revenue from CO₂ utilization
• •Make carbon capture economically viable

Problem

Chemical industry relies on energy-intensive, polluting processes. Traditional catalysts require high temperatures and pressures.

Solution

Develop room-temperature, selective catalysts using machine learning trained on quantum simulation datasets to identify optimal catalyst designs.

Impact

30-50% emission reduction

Market Size

$5T global chemical market

Expected Results

• •Reduce industrial emissions by 100M tons CO₂
• •50,000 new jobs in clean energy sector
• •Transform $5T chemical industry