Publication date: 22nd July 2026
Lithium (Li) metal is widely considered one of the most promising anode candidates for next-generation batteries. However, its commercialization is hindered by interphase instability. The presence of a native passivation layer and the formation of an inhomogeneous, electrochemically derived solid electrolyte interphase (SEI) lead to uneven Li deposition during cycling. This results in dendrites formation and continuous consumption of the Li reservoir, ultimately causing rapid capacity fading.
To address these challenges, we explore vacuum-based approaches to validate machine learning derived nucleation simulations and develop advanced Li metal anodes. First, vacuum deposited metallic interlayers are employed as well-defined platforms to validate machine learning-derived interatomic potential (MLIP) simulations, enabling improved understanding of Li nucleation behavior [1].
In a sperate approach, vacuum thermal evaporation is used to fabricate ultra-pure Li metal anodes [2], followed by the deposition of protective coatings within the same chamber. This process enables precise interface control and the formation of a composite halide artificial SEI. Through a co-evaporation method, controlled doping of a LiF-based artificial SEI is achieved and confirmed by cryogenic transmission electron microscopy (cryo-TEM). When implemented in practical pouch cells with a LFP cathode (2 mAh cm-2), the coated Li anodes exhibit excellent electrochemical stability, maintaining over 350 cycles at 0.5C and 1C.
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