Beyond Fluorination: A Golden Criterion Guided by Chemical Coordination‐Informed Machine Learning for High‐Voltage Electrolyte Design

ABSTRACT Fluorine chemistry has garnered attention for extending operating voltage limits of electrolytes through robust interfacial passivation owing to fluorine's strong electronegativity. However, confronted with solvent/salt/additive multicomponent induced vast combinatorial space, conventional high‐voltage electrolyte recipe design has been confined to reliance on fluorine content adjustments, resulting in inevitable trade‐off between oxidation stability and ion transport kinetics. Herein, we develop a Chemical Coordination‐Informed Molarity feature parsing approach embedded into machine learning for training adapted models. By building the one‐to‐one mapping between components and chemical‐coordination atomic molarities of a given recipe, the trained gradient boosting regression achieves a prediction of oxidation potential with MAE below 0.36 V. Demonstrating 2808 experiment operational candidates based on a ternary‐solvent blend, we reveal the pronounced role of mono‐coordinated fluorine and double‐bonded oxygen molarity ratio (F1/O1) for breaking the oxidative stability limit, and define a golden design criterion for guiding O1‐involved recipes: F1(≥8.19)/O1(≥13.39)[0.55, 1.10]. Following this, we validate three experimentally reported low‐fluoride recipes and identify two promising ones exhibiting oxidation potentials around 6.3 V vs. Li + /Li along with high ion‐transport 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.