{"id":65386,"date":"2024-11-25T10:52:14","date_gmt":"2024-11-25T18:52:14","guid":{"rendered":"https:\/\/energi.media\/?p=65386"},"modified":"2024-11-25T10:52:14","modified_gmt":"2024-11-25T18:52:14","slug":"how-will-artificial-intelligence-transform-energy-innovation","status":"publish","type":"post","link":"https:\/\/energi.media\/news\/how-will-artificial-intelligence-transform-energy-innovation\/","title":{"rendered":"How will artificial intelligence transform energy innovation?"},"content":{"rendered":"

This article was published by the International Energy Agency<\/a> on Nov. 24, 2024.<\/em><\/p>\n

Artificial intelligence is an innovation that catalyses further innovation<\/h4>\n

Like the steam engine and electricity, artificial intelligence (AI) is a general-purpose technology that could profoundly transform the global economy and the world\u2019s energy system. Though key uncertainties remain, it stands to have major impacts. High on the list is its potential role in accelerating innovation.<\/p>\n

Impressive technological advancements \u2013 both incremental and radical \u2013 have helped drive down the cost of key energy technologies in recent years. But to achieve global energy security and emissions goals, existing clean energy technologies need to keep improving, and\u00a0novel energy technologies<\/a>\u00a0must reach the market. AI will enhance the capacity and creativity of scientists in generating and testing new ideas. But for AI-accelerated innovation to really deliver for the energy sector, policymakers and the scientific community need to build a common understanding of the most promising applications and key enablers \u2013 and address critical gaps.<\/p>\n

This is a key focus of the IEA\u2019s new workstream on\u00a0energy and AI<\/a>, which also involves analysing how the adoption of AI will affect electricity consumption by\u00a0data centres\u00a0<\/a>and how AI can be applied to optimize complex parts of energy systems, such as electricity networks<\/a>. The upcoming\u00a0Global Conference on Energy & AI<\/a>, which is bringing 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.<\/p>\n

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Does AI represent a step-change in the speed of energy innovation?<\/h4>\n<\/div>\n
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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\u2019s Law \u2013 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 \u2013 is well known. Similarly, for many energy technologies, it is common to project cost reductions for each doubling of cumulative deployment, known as the \u201clearning rate.\u201d<\/p>\n

However, progress in the semiconductor sector has slowed, and Moore\u2019s 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 per cent, can be maintained over future decades. Recent inflation in technology prices<\/a>, 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.<\/p>\n

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\u2019s 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.<\/p>\n

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Early examples of AI discoveries on energy-related materials are very promising\u2026<\/h4>\n<\/div>\n
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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 optimizing for well-understood relationships across large and well-structured data sets.<\/p>\n

In July 2024, researchers from a US government laboratory and Microsoft\u00a0published results<\/a>\u00a0of a study that used AI to assess 32.5\u00a0million possible new solid-state electrolytes for lithium-based batteries and found 23 new ones with the right characteristics. Scientists in Sweden recently\u00a0screened<\/a>\u00a045\u00a0million potential new battery cathode molecules and found nearly 4\u00a0600 promising candidates. Other teams have achieved\u00a0similar results<\/a>, and one has pursued their findings through to\u00a0synthesis and testing<\/a>. Notably, these types of techniques are increasingly attracting financing:\u00a0Anionics<\/a>, an AI start-up, recently partnered with the battery manufacturing subsidiary of Porsche, while\u00a0Mitra Chem<\/a> has raised USD\u00a080\u00a0million with its promise of shortening the lab-to-production timeline by over 90 per cent.<\/p>\n

Recent breakthroughs have not only been battery-related. Researchers using AI tools have also found they can engineer enzymes for\u00a0biofuel synthesis<\/a>, predict high-yielding\u00a0biofuel feedstocks<\/a>, identify industry-beating\u00a0catalysts for hydrogen-producing electrolyzers<\/a>\u00a0and generate materials for\u00a0carbon dioxide (CO2<\/sub>)<\/a>\u00a0capture<\/a>. And as AI becomes an increasingly indispensable part of the research process for energy technologies, innovators will be also benefit from developments in adjacent areas, including improved robotics and automation. A\u00a0recent study<\/a> of the impact of using AI tools in an industrial research setting showed a 39 per cent increase in patenting by the company in under two years.<\/p>\n

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Appears in:\u00a0 How will artificial intelligence transform energy innovation?
Sources:\u00a0 Toner-Rodgers (2024), as modified by the IEA.\u00a0 To see an animated version of this graph, click here<\/a>.<\/figcaption><\/figure>\n
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\u2026but major obstacles, such as data availability, remain<\/h4>\n<\/div>\n
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Still, serious challenges must be overcome before AI techniques can fulfil their full potential on innovation. One key issue is data availability. The datasets used today have incomplete information about possible materials and represent a restricted subset of molecules or reactions.<\/p>\n

The development of massive, structured, specialized datasets to train AI models, such as such as the Materials Project<\/a>\u00a0and\u00a0Cambridge Structural Database<\/a>, is underway, but they must be further expanded if real-world scientific problems are to be solved. While the creation of \u201csynthetic data<\/a>\u201d to train models can overcome some of the data gaps, there is no substitute for experimental data, and the fastest route to large and reliable experimental datasets is co-operation between laboratories, including at the international level. The\u00a0Mission Innovation M4E platform<\/a>\u00a0is an example of an international initiative that could demonstrate how governments can support common protocols and jointly curated data.<\/p>\n

Another challenge is finding ways for AI to optimize results for more than just a narrow set of characteristics and incorporate considerations that are essential for a material to be integrated into a functional product. Today, substantial human checking and testing is still required \u2013 for example, to assess performance at different temperatures or interactions with all other components of a device. Also, working out the recipe for manufacturing the materials designed by AI can create considerable follow-on work. Having AI perform these more complex tasks appears feasible, but it leads to high computational requirements<\/a>\u00a0and costs that must be assessed.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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If discovery is accelerated but testing and commercialization are not, then half the challenge will stay unaddressed<\/h4>\n<\/div>\n
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Identifying a new material for an energy application via a computer-based method is less than half of the innovation task. Prototyping, followed by commercialization, mass manufacturing and widespread market uptake, can take years or even decades. Yet other AI-related tools in development could compress these timetables, too.<\/p>\n

One is known as the self-driving lab.\u00a0The A-Lab<\/a> at the US Department of Energy\u2019s Lawrence Berkeley National Laboratory contains a series of robots that, since February 2024, can synthesize 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.<\/p>\n

For large, complex systems, a computer-based aid known as a \u201cdigital twin\u201d 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\u00a0nuclear fusion<\/a>, 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\u00a0industrial decarbonisation technologies<\/a>,\u00a0geothermal energy<\/a>.\u00a0synthetic fuel processes<\/a>\u00a0and\u00a0CO2<\/sub>\u00a0capture and storage<\/a>.<\/p>\n

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 commercializing products and services.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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The time to consider the policy context is now<\/h4>\n<\/div>\n
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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.<\/p>\n

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 borders. Investments in skills and equipment will also be required, and policy makers can guide efforts to the most pressing technological needs. To support commercialization, policy makers should also consider how to make new digital tools widely available to innovators and help investors to 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.<\/p>\n

If successful, AI will not only accelerate and improve innovation outcomes but also deliver economic competitiveness, too. Once new products are ready for market,\u00a0analysis with AI<\/a>\u00a0of\u00a0data generated by new products<\/a>\u00a0can raise their value to consumers. Better\u00a0decision-making<\/a>\u00a0by 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.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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 <\/p>\n","protected":false},"excerpt":{"rendered":"

This article was published by the International Energy Agency on Nov. 24, 2024. Artificial intelligence is an innovation that catalyses further innovation Like the steam engine and electricity, artificial intelligence (AI) is a general-purpose technology [Read more]<\/a><\/p>\n<\/div>","protected":false},"author":6,"featured_media":65392,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"give_campaign_id":0,"footnotes":""},"categories":[1],"tags":[120,41,14,565,121,901,115],"class_list":{"0":"post-65386","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-news","8":"tag-clean-energy","9":"tag-energy","10":"tag-featured","11":"tag-iea","12":"tag-innovation","13":"tag-science","14":"tag-technology"},"acf":[],"yoast_head":"\nartificial intelligence - Thoughtful Journalism About Energy's Future<\/title>\n<meta name=\"description\" content=\"Artificial intelligence is a general-purpose technology that could profoundly transform the global economy and the world\u2019s energy system.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/energi.media\/news\/how-will-artificial-intelligence-transform-energy-innovation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"artificial intelligence - 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