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Can we find ways to lock away carbon at the scale needed to fight climate change? There are lots of promising ideas which can make significant impacts at scale, such as ocean fertilisation, ocean alkalinity enhancement, enhanced rock weathering with croplands 鈥� but field trials at scale are slow, expensive and come with potential environmental risks.

Now, Manchester researchers are turning to physics-informed AI, to model how the global carbon cycle behaves and to test the potential of different carbon removal strategies virtually. Their approach offers more flexible predictions than traditional numerical models and can estimate uncertainties where data is missing.

Crucially, these AI models also deliver results far faster and with a lower computational burden. As project lead Dr Peyman Babakhani explains: 鈥淔ield experiments, especially for ideas such as ocean fertilisation, are costly and slow. With the advent of physics-informed AI, we can replace or facilitate such experimental campaigns with predictive models that can incorporate a more accurate representation of physical processes than common numerical models and are also faster. This enables us to study proposed CO鈧� removal methods at scale.鈥�

One theory the team are exploring is the use of engineered nanoparticles to make ocean fertilisation more effective. Studies suggest that nanoparticles of iron, silica or aluminium could boost plankton growth, extend bloom lifetimes and increase the amount of carbon that sinks. However, such methods carry their own costs and risks that need to be evaluated in the laboratory and in silico before field trials.

The AI-powered models might soon be the key to testing ambitious climate solutions before we take them into the real world, helping us to more efficiently combat climate change.