AI-assisted discovery of high-temperature dielectrics for energy storage

Rishi Gurnani, Stuti Shukla, Deepak Kamal, Chao Wu, Jing Hao, Christopher Kuenneth, Pritish Aklujkar, Ashish Khomane, Robert Daniels, Ajinkya A. Deshmukh, Yang Cao, Gregory Sotzing and Rampi Ramprasad ()
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Rishi Gurnani: Georgia Institute of Technology
Stuti Shukla: University of Connecticut
Deepak Kamal: Georgia Institute of Technology
Chao Wu: University of Connecticut
Jing Hao: University of Connecticut
Christopher Kuenneth: Georgia Institute of Technology
Pritish Aklujkar: University of Connecticut
Ashish Khomane: University of Connecticut
Robert Daniels: University of Connecticut
Ajinkya A. Deshmukh: Institute of Materials Science, University of Connecticut
Yang Cao: University of Connecticut
Gregory Sotzing: University of Connecticut
Rampi Ramprasad: Georgia Institute of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industry-grade polymer dielectrics are flexible polyolefins or rigid aromatics, possessing high energy density or high thermal stability, but not both. Here, we employ artificial intelligence (AI), established polymer chemistry, and molecular engineering to discover a suite of dielectrics in the polynorbornene and polyimide families. Many of the discovered dielectrics exhibit high thermal stability and high energy density over a broad temperature range. One such dielectric displays an energy density of 8.3 J cc−1 at 200 °C, a value 11 × that of any commercially available polymer dielectric at this temperature. We also evaluate pathways to further enhance the polynorbornene and polyimide families, enabling these capacitors to perform well in demanding applications (e.g., aerospace) while being environmentally sustainable. These findings expand the potential applications of electrostatic capacitors within the 85–200 °C temperature range, at which there is presently no good commercial solution. More broadly, this research demonstrates the impact of AI on chemical structure generation and property prediction, highlighting the potential for materials design advancement beyond electrostatic capacitors.

Date: 2024
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DOI: 10.1038/s41467-024-50413-x

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