Data-Driven Insight into the Reductive Stability of Ion–Solvent Complexes in Lithium Battery Electrolytes

Lithium (Li) metal batteries (LMBs) are regarded as one
of the
most promising energy storage systems due to their ultrahigh theoretical
energy density. However, the high reactivity of the Li anodes leads
to the decomposition of the electrolytes, presenting a huge impediment
to the practical application of LMBs. The routine trial-and-error
methods are inefficient in designing highly stable solvent molecules
for the Li metal anode. Herein, a data-driven approach is proposed
to probe the origin of the reductive stability of solvents and accelerate
the molecular design for advanced electrolytes. A large database of
potential solvent molecules is first constructed using a graph theory-based
algorithm and then comprehensively investigated by both first-principles
calculations and machine learning (ML) methods. The reductive stability
of 99% of the electrolytes decreases under the dominance of ion–solvent
complexes, according to the analysis of the lowest unoccupied molecular
orbital (LUMO). The LUMO energy level is related to the binding energy,
bond length, and orbital ratio factors. An interpretable ML method
based on Shapley additive explanations identifies the dipole moment
and molecular radius as the most critical descriptors affecting the
reductive stability of coordinated solvents. This work not only affords
fruitful data-driven insight into the ion–solvent chemistry
but also unveils the critical molecular descriptors in regulating
the solvent’s reductive stability, which accelerates the rational
design of advanced electrolyte molecules for next-generation Li batteries.