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However, confronted with solvent\/salt\/additive multicomponent induced vast combinatorial space, conventional high\u2010voltage electrolyte recipe design has been confined to reliance on fluorine content adjustments, resulting in inevitable trade\u2010off between oxidation stability and ion transport kinetics. Herein, we develop a Chemical Coordination\u2010Informed Molarity feature parsing approach embedded into machine learning for training adapted models. By building the one\u2010to\u2010one mapping between components and chemical\u2010coordination atomic molarities of a given recipe, the trained gradient boosting regression achieves a prediction of oxidation potential with MAE below 0.36\u00a0V. Demonstrating 2808 experiment operational candidates based on a ternary\u2010solvent blend, we reveal the pronounced role of mono\u2010coordinated fluorine and double\u2010bonded oxygen molarity ratio (F1\/O1) for breaking the oxidative stability limit, and define a golden design criterion for guiding O1\u2010involved recipes: F1(\u22658.19)\/O1(\u226513.39)[0.55, 1.10]. Following this, we validate three experimentally reported low\u2010fluoride recipes and identify two promising ones exhibiting oxidation potentials around 6.3\u00a0V vs.\u00a0Li\n                    <jats:sup>+<\/jats:sup>\n                    \/Li along with high ion\u2010transport kinetics for further assessments. This work demonstrates customizable feature engineering in yielding intelligent materials design principles for reconciling multiple target performance that are usually mutually exclusive.\n                  <\/jats:p>","DOI":"10.1002\/anie.1244287","type":"journal-article","created":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:46:56Z","timestamp":1781369216000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Fluorination: A Golden Criterion Guided by Chemical Coordination\u2010Informed Machine Learning for High\u2010Voltage Electrolyte Design"],"prefix":"10.1002","volume":"65","author":[{"given":"Kai","family":"Guo","sequence":"first","affiliation":[{"name":"State Key Laboratory of Materials for Advanced Nuclear Energy &amp; School of Materials Science and Engineering Shanghai University Shanghai 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Key Laboratory of Materials for Advanced Nuclear Energy &amp; School of Computer Engineering and Science Shanghai University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing National Laboratory For Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4512-7860","authenticated-orcid":false,"given":"Da","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Materials for Advanced Nuclear Energy &amp; School of Materials Science and Engineering Shanghai University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8988-9763","authenticated-orcid":false,"given":"Siqi","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of 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