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

Engineering and Technology

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

R. Gurnani, S. Shukla, et al.

This groundbreaking research, conducted by Rishi Gurnani and colleagues, harnesses artificial intelligence and polymer chemistry to discover a polynorbornene dielectric that significantly outperforms existing options, boasting an energy density of 8.3 J cc⁻¹ at 200 °C. The team reveals exciting pathways for enhancing these materials further, showcasing the transformative potential of AI in materials design.

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Playback language: English
Abstract
Electrostatic capacitors are crucial for energy storage, but their energy density, especially at high temperatures, needs improvement. This research uses artificial intelligence (AI), polymer chemistry, and molecular engineering to discover high-temperature dielectrics in the polynorbornene and polyimide families. A discovered polynorbornene dielectric exhibits an energy density of 8.3 J cc⁻¹ at 200 °C, significantly exceeding commercially available alternatives. The study also explores pathways to further enhance these materials for demanding applications, demonstrating AI's impact on materials design.
Publisher
Nature Communications
Published On
Jul 19, 2024
Authors
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, Rampi Ramprasad
Tags
electrostatic capacitors
energy density
artificial intelligence
high-temperature dielectrics
polynorbornene
polyimide
materials design

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