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

He Li; Hongbo Zheng; Tianle Yue; Zongliang Xie; Shao Peng 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; Yi Liu

doi:10.1038/s41560-024-01670-z

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

He Li, Hongbo Zheng, Tianle Yue, Zongliang Xie, Shao Peng 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 WuYing Li, K. Barry Sharpless, Yi Liu

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Journal	Nature Energy
DOIs	https://doi.org/10.1038/s41560-024-01670-z
State	Accepted/In press - 2024
Externally published	Yes

Funding

H.L., Z.X., P.F.P., M.S., T.X. and Y. Liu acknowledge the support from the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under contract number DE-AC02-05CH11231 within the Inorganic/Organic Nanocomposites Program (KC3104). K.B.S. acknowledges the support from the National Science Foundation (CHE-1610987). Y. Li acknowledges the support from the Air Force Office of Scientific Research (AFOSR) through the Air Force\u2019s Young Investigator Research Program (FA9550-20-1-0183; programme manager: M.-J. Pan and D. Barbee). P.W. acknowledges the support from the National Institutes of Health (R35GM139643). X.G. acknowledges the support from the US Department of Energy, Office of Science, Office of Basic Energy Science under award number DE-SC0022050. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under contract number DE-AC02-05CH11231. We thank T. Xu, P. Liu and Z. Peng for their advice on simulations. We thank S. W. Shelton, V. Alto\u00E9, A. M. Schwartzberg, S. Zhang, H. Zhang, A. Gashi, L. M. Klivansky and A. Pham for instrumental and technical support. We thank J. Zhang, C. Yang and M. Qi for their discussion on experimental results.

Funders	Funder number
Air Force Office of Scientific Research
U.S. Department of Energy
Office of Science
Division of Materials Sciences and Engineering	DE-AC02-05CH11231, KC3104
Basic Energy Sciences	DE-SC0022050
U.S. Air Force	FA9550-20-1-0183
National Science Foundation	CHE-1610987
National Institutes of Health	R35GM139643

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10.1038/s41560-024-01670-z

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Li, H., Zheng, H., Yue, T., Xie, Z., Yu, S. P., Zhou, J., Kapri, T., Wang, Y., Cao, Z., Zhao, H., Kemelbay, A., He, J., Zhang, G., Pieters, P. F., Dailing, E. A., Cappiello, J. R., Salmeron, M., Gu, X., Xu, T., ... Liu, Y. (Accepted/In press). Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage. Nature Energy. https://doi.org/10.1038/s41560-024-01670-z

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title = "Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage",

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.",

author = "He Li and Hongbo Zheng and Tianle Yue and Zongliang Xie and Yu, {Shao Peng} and Ji Zhou and Topprasad Kapri and Yunfei Wang and Zhiqiang Cao and Haoyu Zhao and Aidar Kemelbay and Jinlong He and Ge Zhang and Pieters, {Priscilla F.} and Dailing, {Eric A.} and Cappiello, {John R.} and Miquel Salmeron and Xiaodan Gu and Ting Xu and Peng Wu and Ying Li and Sharpless, {K. Barry} and Yi Liu",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive licence to Springer Nature Limited 2024.",

year = "2024",

doi = "10.1038/s41560-024-01670-z",

language = "English",

journal = "Nature Energy",

issn = "2058-7546",

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Li, H, Zheng, H, Yue, T, Xie, Z, Yu, SP, Zhou, J, Kapri, T, Wang, Y, Cao, Z, Zhao, H, Kemelbay, A, He, J, Zhang, G, Pieters, PF, Dailing, EA, Cappiello, JR, Salmeron, M, Gu, X, Xu, T, Wu, P, Li, Y, Sharpless, KB & Liu, Y 2024, 'Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage', Nature Energy. https://doi.org/10.1038/s41560-024-01670-z

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T1 - Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage

AU - Li, He

AU - Zheng, Hongbo

AU - Yue, Tianle

AU - Xie, Zongliang

AU - Yu, Shao Peng

AU - Zhou, Ji

AU - Kapri, Topprasad

AU - Wang, Yunfei

AU - Cao, Zhiqiang

AU - Zhao, Haoyu

AU - Kemelbay, Aidar

AU - He, Jinlong

AU - Zhang, Ge

AU - Pieters, Priscilla F.

AU - Dailing, Eric A.

AU - Cappiello, John R.

AU - Salmeron, Miquel

AU - Gu, Xiaodan

AU - Xu, Ting

AU - Wu, Peng

AU - Li, Ying

AU - Sharpless, K. Barry

AU - Liu, Yi

PY - 2024

Y1 - 2024

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AB - 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.

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JF - Nature Energy

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

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