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Theory-guided data science for optimizing electrolyte and interphase in lithium metal battery

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PI:  Jian Qin, Chemical Engineering

The ever-growing demand for energy-dense storage necessitates an accelerated search for novel electrolytes and protective electrode coatings that enhance the safety and lifetime of lithium metal batteries. We aim at developing a data-driven, physics-informed correlation between fundamental material properties and battery performance based on information gathered from high-throughput molecular simulations and continuum transport analyses. By iteratively training the model, beneficial properties of electrolytes will be identified, facilitating the development of safe, reliable lithium metal batteries.