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If discovery is accelerated but testing and commercialisation are not, then half the challenge will stay unaddressed. 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 such 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. The time to consider policy context is now There is clear potential for AI to enhance and accelerate innovation to tackle a wide range of energy technology challenges. There are exciting examples of this happening already, but the full potential of AI in this area will not be realised unless governments focus on some key emerging issues upfront. To drive scientific discovery towards the most impactful outcomes, there is a need to invest in searchable databases that follow common protocols and are widely accessible, including by interconnecting laboratories across international
SIMON BENNET Simon Bennett covers new technology analysis in the International Energy Agen- cy’s Energy Technology Policy Division, leading work on innovation tracking, poli- cy and outlooks. He previously worked at the European Commission’s DG Energy and holds MSc degrees in chemistry and environmental technology. His doctorate in energy policy is from Imperial College. THOMAS SPENCER Thomas has been at the IEA since 2021, where he works on decarbonisation path- ways, climate negotiations and digitalisa- tion. He was one of the lead authors on the 2023 report “Net Zero Roadmap: A Global Pathway to Keep the 1.5 °C Goal in Reach”. He holds an MSc in Carbon Management from the University of Edinburgh.
This article originally appeared in November 2024: IEA (2024), How will artificial intelligence transform energy innovation?, IEA, Paris https://www.iea.org/ commentaries/how-will-artificial-intelligence-transform-energy-innovation, Licence: CC BY 4.0 decision-making by software for controlling new technologies can likewise reduce risks and add value for their users. The benefits will be shared by all countries, their innovators, investors and firms if efforts are anticipated, directed and cooperative. borders. Investments in skills and equipment will also be required, and policy makers can guide efforts to the most pressing technological needs. To support commercialisation, policy makers should also consider how to make new digital tools widely available to innovators and help investors adjust to the resulting reductions in project risk. At the same time, the computing and energy needs of AI for these important tasks, as well as potential risks such as those related to intellectual property, must be discussed in multilateral fora. If successful, AI will not only accelerate and improve innovation outcomes but also deliver economic competitiveness, too. Once new products are ready for market, analysis with AI of data generated by new products can raise their value to consumers. Better
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THE FUTURE OF ENERGY
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