Meng, N. et al. Ultrahigh β-phase content poly(vinylidene fluoride) with relaxor-like ferroelectricity for high energy density capacitors. Nat. Commun. 10, 4535 (2019).
Google Scholar
Tawade, B. V. et al. Polymer-grafted nanoparticles with variable grafting densities for high energy density polymeric nanocomposite dielectric capacitors. JACS Au 3, 1365–1375 (2023).
Google Scholar
Wu, X., Chen, X., Zhang, Q. M. & Tan, D. Q. Advanced dielectric polymers for energy storage. Energy Storage Mater. 44, 29–47 (2022).
Google Scholar
Zha, J.-W. et al. Polymer dielectrics for high-temperature energy storage: constructing carrier traps. Prog. Mater. Sci. 140, 101208 (2023).
Google Scholar
Luo, H. et al. Interface design for high energy density polymer nanocomposites. Chem. Soc. Rev. 48, 4424–4465 (2019).
Google Scholar
Zhang, Q. et al. High-temperature polymers with record-high breakdown strength enabled by rationally designed chain-packing behavior in blends. Matter 4, 2448–2459 (2021).
Google Scholar
Chen, J. et al. Engineering the dielectric constants of polymers: from molecular to mesoscopic scales. Adv. Mater. (2023).
Wu, C. et al. Flexible temperature‐invariant polymer dielectrics with large bandgap. Adv. Mater. 32, 2000499 (2020).
Google Scholar
Feng, Q.-K. et al. Recent progress and future prospects on all-organic polymer dielectrics for energy storage capacitors. Chem. Rev. 122, 3820–3878 (2022).
Google Scholar
Yang, M. et al. Polymer nanocomposite dielectrics for capacitive energy storage. Nat. Nanotechnol. 19, 588–603 (2024).
Google Scholar
Liu, X.-J., Zheng, M.-S., Chen, G., Dang, Z.-M. & Zha, J.-W. High-temperature polyimide dielectric materials for energy storage: theory, design, preparation and properties. Energy Environ. Sci. 15, 56–81 (2022).
Google Scholar
Song, J. et al. Alicyclic polyimides with large band gaps exhibit superior high-temperature capacitive energy storage. Mater. Horiz. 10, 2139–2148 (2023).
Google Scholar
Ren, W. et al. Metallized stacked polymer film capacitors for high-temperature capacitive energy storage. Energy Storage Mater. 65, 103095 (2024).
Google Scholar
Dong, J. et al. Scalable high-permittivity polyimide copolymer with ultrahigh high-temperature capacitive performance enabled by molecular engineering. Adv. Energy Mater. 14, 2303732 (2024).
Google Scholar
Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).
Google Scholar
Martin, T. B. & Audus, D. J. Emerging trends in machine learning: a polymer perspective. ACS Polym. Au 3, 239–258 (2023).
Google Scholar
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, 54 (2017).
Google Scholar
Kuenneth, C. & Ramprasad, R. polyBERT: a chemical language model to enable full machine-driven ultrafast polymer informatics. Nat. Commun. 14, 4099 (2023).
Google Scholar
Liu, Y. et al. Generative artificial intelligence and its applications in materials science: current situation and future perspectives. J. Materiomics 9, 798–816 (2023).
Google Scholar
Shen, Z.-H. et al. Machine learning in energy storage materials. Interdiscip. Mater. 1, 175–195 (2022).
Chen, L. et al. Polymer informatics: current status and critical next steps. Mater. Sci. Eng. R: Rep. 144, 100595 (2021).
Google Scholar
Dave, A. et al. Autonomous discovery of battery electrolytes with robotic experimentation and machine learning. Cell Rep. Phys. Sci. 1, 100264 (2020).
Google Scholar
Boyd, P. G. et al. Data-driven design of metal–organic frameworks for wet flue gas CO2 capture. Nature 576, 253–256 (2019).
Google Scholar
Kang, Y., Park, H., Smit, B. & Kim, J. A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks. Nat. Mach. Intell. 5, 309–318 (2023).
Google Scholar
Luo, Y. et al. MOF synthesis prediction enabled by automatic data mining and machine learning. Angew. Chem. Int. Ed. 134, e202200242 (2022).
Google Scholar
Rosen, A. S. et al. High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration. npj Comput. Mater. 8, 112 (2022).
Google Scholar
Rao, Z. et al. Machine learning–enabled high-entropy alloy discovery. Science 378, 78–85 (2022).
Google Scholar
Yao, Y. et al. High-entropy nanoparticles: synthesis-structure-property relationships and data-driven discovery. Science 376, 6589 (2022).
Google Scholar
Wang, R. et al. Designing tailored combinations of structural units in polymer dielectrics for high-temperature capacitive energy storage. Nat. Commun. 14, 2406 (2023).
Google Scholar
Tao, L. et al. Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning. Chem. Eng. J. 465, 142949 (2023).
Google Scholar
Wu, C. et al. Dielectric polymers tolerant to electric field and temperature extremes: integration of phenomenology, informatics, and experimental validation. ACS Appl. Mater. Interfaces 13, 53416–53424 (2021).
Google Scholar
Yang, C., Flynn, J. P. & Niu, J. Facile synthesis of sequence‐regulated synthetic polymers using orthogonal SuFEx and CuAAC click reactions. Angew. Chem. Int. Ed. 57, 16194 (2018).
Google Scholar
Geng, Z., Shin, J. J., Xi, Y. & Hawker, C. J. Click chemistry strategies for the accelerated synthesis of functional macromolecules. J. Polym. Sci. 59, 963–1042 (2021).
Google Scholar
Kolb, H. C., Finn, M. G. & Sharpless, K. B. Click chemistry: diverse chemical function from a few good reactions. Angew. Chem. Int. Ed. 40, 2004–2021 (2001).
Google Scholar
Homer, J. A. et al. Sulfur fluoride exchange. Nat. Rev. Methods Prim. 3, 58 (2023).
Google Scholar
Li, H. et al. High-performing polysulfate dielectrics for electrostatic energy storage under harsh conditions. Joule 7, 95–111 (2023).
Google Scholar
Gao, B. et al. Bifluoride-catalysed sulfur (VI) fluoride exchange reaction for the synthesis of polysulfates and polysulfonates. Nat. Chem. 9, 1083–1088 (2017).
Google Scholar
Kim, H. et al. Chain-growth sulfur(VI) fluoride exchange polycondensation: molecular weight control and synthesis of degradable polysulfates. ACS Cent. Sci. 7, 1919–1928 (2021).
Google Scholar
Wang, H. et al. SuFEx-based polysulfonate formation from ethenesulfonyl fluoride–amine adducts. Angew. Chem. Int. Ed. 56, 11203 (2017).
Google Scholar
Tao, L., Varshney, V. & Li, Y. Benchmarking machine learning models for polymer informatics: an example of glass transition temperature. J. Chem. Inf. Model. 61, 5395–5413 (2021).
Google Scholar
Liu, Y., Guo, B., Zou, X., Li, Y. & Shi, S. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater. 31, 434–450 (2020).
Google Scholar
Liu, Y., Zhao, T., Ju, W. & Shi, S. Materials discovery and design using machine learning. J. Materiomics 3, 159–177 (2017).
Google Scholar
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
Google Scholar
Landrum, G. RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling. v.2022.9.5 (2022); https://www.rdkit.org
Damewood, J. et al. Representations of materials for machine learning. Annu. Rev. Mater. Res. 53, 399–426 (2023).
Google Scholar
Atz, K., Grisoni, F. & Schneider, G. Geometric deep learning on molecular representations. Nat. Mach. Intell. 3, 1023–1032 (2021).
Google Scholar
Tao, L., Chen, G. & Li, Y. Machine learning discovery of high-temperature polymers. Patterns 2, 100225 (2021).
Google Scholar
Kim, C., Chandrasekaran, A., Huan, T. D., Das, D. & Ramprasad, R. Polymer genome: a data-powered polymer informatics platform for property predictions. J. Phys. Chem. C. 122, 17575–17585 (2018).
Google Scholar
Zhu, W., Li, F., Liu, J., Ma, X. & Jiang, X. Nucleophilic construction of sulfate bond: a simplified access to polysulfates and polysulfonates. React. Chem. Eng. 4, 2074–2080 (2019).
Google Scholar
Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminformatics 1, 8 (2009).
Google Scholar
Wu, S. et al. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Comput. Mater. 5, 66 (2019).
Google Scholar
Deshmukh, A. A. et al. Flexible polyolefin dielectric by strategic design of organic modules for harsh condition electrification. Energy Environ. Sci. 15, 1307–1314 (2022).
Google Scholar
Chen, J. et al. Ladderphane copolymers for high-temperature capacitive energy storage. Nature 615, 62–66 (2023).
Google Scholar
Qin, H. et al. High-temperature polymer dielectrics with superior capacitive energy storage performance. Chem. Eng. J. 461, 142068 (2023).
Google Scholar
Chen, J. et al. Aromatic-free polymers based all-organic dielectrics with breakdown self-healing for high-temperature capacitive energy storage. Adv. Mater. 35, 2306562 (2023).
Google Scholar
Zhang, G., Li, Q., Allahyarov, E., Li, Y. & Zhu, L. Challenges and opportunities of polymer nanodielectrics for capacitive energy storage. ACS Appl. Mater. Interfaces 13, 37939–37960 (2021).
Google Scholar
Li, Q. et al. High-temperature dielectric materials for electrical energy storage. Annu. Rev. Mater. Res. 48, 219–243 (2018).
Google Scholar
Zhang, T. et al. Recent progress in polymer dielectric energy storage: from film fabrication and modification to capacitor performance and application. Prog. Mater. Sci. 140, 101207 (2023).
Google Scholar
Li, Q. et al. Flexible high-temperature dielectric materials from polymer nanocomposites. Nature 523, 576–579 (2015).
Google Scholar
Wei, J. & Zhu, L. Intrinsic polymer dielectrics for high energy density and low loss electric energy storage. Prog. Polym. Sci. 106, 101254 (2020).
Google Scholar
Zha, J.-W., Zheng, M.-S., Fan, B.-H. & Dang, Z.-M. Polymer-based dielectrics with high permittivity for electric energy storage: a review. Nano Energy 89, 106438 (2021).
Google Scholar
Li, H. et al. Dielectric polymers for high-temperature capacitive energy storage. Chem. Soc. Rev. 50, 6369–6400 (2021).
Google Scholar
Zhang, Z., Wang, D. H., Litt, M. H., Tan, L.-S. & Zhu, L. High-temperature and high-energy-density dipolar glass polymers based on sulfonylated poly(2,6-dimethyl-1,4-phenylene oxide). Angew. Chem. Int. Ed. 57, 1528–1531 (2018).
Google Scholar
Li, Z. et al. Probing electronic band structures of dielectric polymers via pre-breakdown conduction. Adv. Mater. (2024).
Ju, T. et al. Effect of glass transition temperature on enhanced dielectric breakdown strength and lifetime of multilayer polymer films. ACS Appl. Mater. Interfaces 16, 795–806 (2024).
Google Scholar
Yang, M. et al. Roll-to-roll fabricated polymer composites filled with subnanosheets exhibiting high energy density and cyclic stability at 200 °C. Nat. Energy 9, 143–153 (2024).
Google Scholar
Liu, Y. et al. Data quantity governance for machine learning in materials science. Natl. Sci. Rev. 10, nwad125 (2023).
Chen, H., Chen, J. & Ding, J. Data evaluation and enhancement for quality improvement of machine learning. IEEE Trans. Reliab. 70, 831–847 (2021).
Google Scholar
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