Researchers at Lawrence Berkeley National Laboratory and collaborators have utilized machine-learning techniques to discover a high-performing material for film capacitors, essential for renewable energy and high-power applications. By screening a library of 50,000 polymers with neural networks, the team identified three promising candidates based on properties like heat resistance and energy storage density. Using click chemistry, these polymers were synthesized, and capacitors made from one exhibited record-breaking efficiency, durability, and thermal performance. The breakthrough demonstrates the power of AI in accelerating material discovery, and ongoing work includes designing generative models for further advancements in polymer-based energy storage technologies. The findings were published in Nature Energy.
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