RI$UWLƓFLDO,QWHOOLJHQFHLQ the energy transformation requires addressing key challenges, including data accessibility, skill gaps and regulatory frameworks.” “
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which brings together leaders from government, the energy sector, the tech industry and civil society to discuss these topics for the first time, will provide a space to kickstart and advance public-private dialogues on these subjects at a critical moment. Does AI represent a step-change in the speed of energy innovation? For energy analysts, a fundamental question is whether the application of AI will cause the rate of technology progress to deviate from current projections. In the field of semiconductors, Moore’s Law – an observation from the 1960s that the number of transistors in an integrated circuit doubles about every two years, which proved startlingly accurate for several decades – is well known. Similarly, for many energy technologies, it is common to project cost reductions for each doubling of cumulative deployment, known as the “learning rate.” However, progress in the semiconductor sector has slowed, and Moore’s Law has not been a good guide for technological development since
around 2010. Experts question whether the learning rate for a technology like electric vehicle batteries, which IEA analysis projects at 15%, can be maintained over future decades. Recent inflation in technology prices, partly caused by mismatches between supply and demand for critical material inputs, are a reminder that factors such as manufacturing capacity and trade can also impede the innovation process. Some analysts see AI as a means to keep current learning rate projections on track despite these concerns. Others see it as a more disruptive force that could make today’s rates look very conservative. To inform this debate, it is necessary to take a closer look at the specific ways in which AI could boost the pace of innovation. AI discoveries on energy-related materials are promising Finding a higher-performing material for a task, or one that does not contain certain undesirable inputs, has typically relied on human ingenuity and knowledge of how different compounds behave. But the number of possible
options is often vast. AI techniques are already excellent at solving problems by optimising for well-understood relationships across large and well-structured data sets. In July 2024, researchers from a US government laboratory and Microsoft published results of a study that used AI to assess 32.5 million possible new solid-state electrolytes for lithium-based batteries and found 23 new ones with the right characteristics. Scientists in Sweden recently screened 45 million potential new battery cathode molecules and found nearly 4,600 promising candidates. Other teams have achieved similar results, and one has pursued their findings through to synthesis and testing. Notably, these types of techniques are increasingly attracting financing: Anionics, an AI start-up, recently partnered with the battery manufacturing subsidiary of Porsche, while Mitra Chem has raised USD 80 million with its promise of shortening the lab-to-production timeline by over 90%. Recent breakthroughs have not
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THE FUTURE OF ENERGY
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