A paradigm for systematic screening and evaluation of artificial solid-electrolyte interfaces for lithium metal anodes: a computational study of binary selenides

Lithium metal is a promising anode material for achieving higher capacity than that of the commercial lithium-ion batteries. However, lithium metal anodes (LMAs) suffer from a series of vital issues caused by the unstable solid-electrolyte interfaces (SEIs). To deal with this issue, herein, a paradigm was proposed for the extensive screening and precise evaluation of artificial SEI materials for LMAs, i.e., the ideal SEI material can be obtained through a workflow of chemical stability assessments, electronic conductivity measurements, and accurate predictions of key parameters (Li affinity, structural stability, Li-ion conductivity, mechanical properties, and the capability to stabilize the electrolyte). The proposed paradigm was demonstrated by searching for the optimal SEI materials for LMAs from 76 binary selenides. First, five thermodynamically lithium-stable selenides (BaSe, CaSe, EuSe, SrSe, and YbSe) were identified as potential artificial SEI materials with appropriate negative cathodic limits (versus Li/Li+) and relatively good electronic insulation. Further performance predictions based on first-principles calculations highlight the excellent attributes of the YbSe SEI material, including (1) lower binding affinity to Li ions than Li metal, ensuring that Li ions cross the SEI and deposit on the lithium metal surface; (2) the fastest Li-ion conduction; (3) the strongest mechanical strength to prevent the lithium-dendrite formation; (4) the best structural stability against the lattice distortion caused by Li-ion adsorption; (5) the capability of hindering the decomposition of electrolyte and Li2S8 molecules. This paradigm can be widely applied to virtual screening of ideal SEI materials for LMAs.

Automated summary

This article proposes a method for obtaining ideal artificial solid-electrolyte interface (SEI) materials for lithium metal anodes (LMAs) through chemical stability assessments, electronic conductivity measurements, and accurate predictions of key parameters. The method was demonstrated by finding optimal SEI materials from 76 binary selenides for LMAs.