AI4Capture: Using Artificial Intelligence to Design Better Materials for Carbon Capture
Through AI4Capture, CORC aims to bridge the gap between computational design and real-world deployment - paving the way for smarter, faster, and more sustainable carbon capture technologies
The development of advanced materials is crucial for tackling climate change, especially when it comes to carbon capture.
Over the past decade, researchers have created vast databases of nanoporous materials, particularly Metal-Organic Frameworks (MOFs). These materials are known for their tunable structures and exceptional potential for gas absorption. The 2025 Nobel Prize in Chemistry was awarded to these materials. Since their invention, over 80,000 MOFs have already been synthesized, but computational techniques suggest that over 100 trillion could theoretically exist.
However, exploring this immense chemical space through traditional experimentation or brute-force simulations is impossible.
Researchers Berend Smit and Susana Garcia will join CORC through the AI4Capture project and combine AI, data, and materials science to design and optimize materials for CO₂ capture more efficiently.
The AI4Capture project aims to use artificial intelligence (AI) to speed up this search. It builds on a digital platform that combines computer simulations with economic and environmental analysis. This platform will enable researchers to test how different materials perform across various carbon capture case studies, such as CO₂ capture from power plants, steel, and cement production.
The aim for AI4Capture is to also add new tools to make this process faster and smarter:
- Digital Twins – computer models that quickly predict how materials will perform in real-world carbon capture systems.
- AI-driven material discovery – algorithms that find or even design new MOFs that could work best for certain applications.
- Synthesis recommenders – AI tools that suggest how to actually make these materials in the lab in a sustainable way.
Together, these tools will help researchers identify the best material and process combinations much faster than traditional methods. This aligns perfectly with our mission to use digital technologies for discovering and scaling up new materials, helping move carbon capture technologies from the lab into the real world.