# Machine-learning models help discover a material for film capacitors with record-breaking performance

**URL:** <https://community.materialssquare.com/t/machine-learning-models-help-discover-a-material-for-film-capacitors-with-record-breaking-performance/1013>\
**Category:** News\
**Created:** [December 6, 2024, 6:15pm UTC](https://community.materialssquare.com/t/machine-learning-models-help-discover-a-material-for-film-capacitors-with-record-breaking-performance/1013 "2024-12-06T18:15:13Z")\
**Posts on this page:** 1\
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**Author:** ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)\
**Post date:** [December 6, 2024, 6:15pm UTC](https://community.materialssquare.com/t/machine-learning-models-help-discover-a-material-for-film-capacitors-with-record-breaking-performance/1013/1 "2024-12-06T18:15:13Z")

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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_.

For more details, please continue reading the full article under the following link:

> **[Machine-learning models help discover a material for film capacitors with...](https://techxplore.com/news/2024-12-machine-material-capacitors.html)**
>
> The Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and several collaborating institutions have successfully demonstrated a machine-learning technique to accelerate the discovery of materials for film capacitors—crucial...

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In general, if you enjoy reading this kind of scientific news articles, I would also be keen to connect with fellow researchers based on common research interests in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( [www.matsq.com](http://www.matsq.com) ), designed for streamlining the execution of materials and molecular atomistic simulations!

Best regards,

Dr. Gabriele Mogni  
Technical Consultant and EU Representative  
Virtual Lab Inc., the parent company of the Materials Square platform  
Website: [Home | Virtual Lab Inc.](http://www.virtuallab.co.kr/en/)  
Email: gabriele@simulation.re.kr

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