Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage

He Li, Hongbo Zheng, Tianle Yue, Zongliang Xie, ShaoPeng Yu, Ji Zhou, Topprasad Kapri, Yunfei Wang, Zhiqiang Cao, Haoyu Zhao, Aidar Kemelbay, Jinlong He, Ge Zhang, Priscilla F. Pieters, Eric A. Dailing, John R. Cappiello, Miquel Salmeron, Xiaodan Gu, Ting Xu, Peng Wu, Ying Li (), K. Barry Sharpless () and Yi Liu ()
Additional contact information
He Li: Lawrence Berkeley National Laboratory
Hongbo Zheng: The Scripps Research Institute
Tianle Yue: University of Wisconsin–Madison
Zongliang Xie: Lawrence Berkeley National Laboratory
ShaoPeng Yu: The Scripps Research Institute
Ji Zhou: University of Wisconsin–Madison
Topprasad Kapri: The Scripps Research Institute
Yunfei Wang: University of Southern Mississippi
Zhiqiang Cao: University of Southern Mississippi
Haoyu Zhao: University of Southern Mississippi
Aidar Kemelbay: Lawrence Berkeley National Laboratory
Jinlong He: University of Wisconsin–Madison
Ge Zhang: The Scripps Research Institute
Priscilla F. Pieters: University of California, Berkeley
Eric A. Dailing: Lawrence Berkeley National Laboratory
John R. Cappiello: The Scripps Research Institute
Miquel Salmeron: Lawrence Berkeley National Laboratory
Xiaodan Gu: University of Southern Mississippi
Ting Xu: Lawrence Berkeley National Laboratory
Peng Wu: The Scripps Research Institute
Ying Li: University of Wisconsin–Madison
K. Barry Sharpless: The Scripps Research Institute
Yi Liu: Lawrence Berkeley National Laboratory

Nature Energy, 2025, vol. 10, issue 1, 90-100

Abstract: Abstract The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.

Date: 2025
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DOI: 10.1038/s41560-024-01670-z

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