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Here, hydrangea\u2010shaped heterodiatomic carbon superstructures (CHS) are designed using a solution phase manufacturing route, wherein machine learning workflow is applied to screen precursor\u2010matched solvent for optimizing solvent\u2010precursor interaction. Based on the established solubility parameter model and molecular growth kinetics simulation, ethanol as the optimal solvent stimulates thermodynamic solubilization and growth of polymeric intermediates to evoke CHS. Featured with surface\u2010active motifs and consecutive charge transfer paths, CHS allows high accessibility of zincophilic sites and fast ion migration with low energy barriers. A anion\u2212cation hybrid charge storage mechanism of CHS cathode is disclosed, which entails physical alternate uptake of Zn\n                    <jats:sup>2+<\/jats:sup>\n                    \/CF\n                    <jats:sub>3<\/jats:sub>\n                    SO\n                    <jats:sub>3<\/jats:sub>\n                    <jats:sup>\u2212<\/jats:sup>\n                    ions at electroactive sites and chemical bipedal redox of Zn\n                    <jats:sup>2+<\/jats:sup>\n                    ions with carbonyl\/pyridine motifs. Such a beneficial electrochemistry contributes to all\u2010round improvement in Zn\u2010ion storage, involving excellent capacities (231 mAh g\n                    <jats:sup>\u22121<\/jats:sup>\n                    at 0.5 A g\n                    <jats:sup>\u22121<\/jats:sup>\n                    ; 132 mAh g\n                    <jats:sup>\u22121<\/jats:sup>\n                    at 50 A g\n                    <jats:sup>\u22121<\/jats:sup>\n                    ), high energy density (152\u00a0Wh kg\n                    <jats:sup>\u22121<\/jats:sup>\n                    ), and long\u2010lasting cyclability (100\u00a0000 cycles). This work expands the design versatilities of superstructure materials and will accelerate experimental procedures during carbon manufacturing through machine learning in the future.\n                  <\/jats:p>","DOI":"10.1002\/smll.202405940","type":"journal-article","created":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T02:35:27Z","timestamp":1724466927000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning\u2010Regulated Solvent\u2010Precursor Interactions for Superior Zinc Storage"],"prefix":"10.1002","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3290-2116","authenticated-orcid":false,"given":"Qi","family":"Huang","sequence":"first","affiliation":[{"name":"Institute for Electric Light Sources School of Information Science and Technology Fudan University  Shanghai 200438 P. R. 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