Artificial intelligence will transform energy innovation, but we haven’t yet grasped the scale of the impacts

Identifying a new material for an energy application via a computer-based method is less than half of the innovation task. Prototyping, followed by commercialisation, mass manufacturing and widespread market uptake, can take years or even decades. Yet other AI-related tools in development could compress these timetables, too.

One is known as the self-driving lab. The A-Lab at the US Department of Energy's Lawrence Berkeley National Laboratory contains a series of robots that, since February 2024, can synthesise the energy storage chemicals predicted by computer calculations to offer major performance improvements. This self-driving laboratory can process up to 100 times more samples per day than a human-run equivalent.

For large, complex systems, a computer-based aid known as a "digital twin" can significantly reduce the costs and risks of design and scale-up. Digital twins, which are virtual representations of all the elements of a specific facility or process, have been used to optimise manufacturing for over a decade but are now being powered by AI and applied to innovation. In sectors such as nuclear fusion, they are helping design and test equipment. The hope is that the costs of complex engineering design will be sharply reduced, particularly for expensive, first-of-a-kind projects. This could be a significant fillip for innovators of industrial decarbonisation technologies, geothermal energy. synthetic fuel processes and CO₂ capture and storage.

However, difficulties also persist in applying AI to this phase of the innovation process. Currently, these tools are not all widely accessible to innovators in the scale-up stage. Additionally, skills gaps could be an issue in a fast-moving field, while responsive regulatory and standards frameworks will be necessary to support and accommodate new approaches to testing and commercialising products and services.