Building Up an “Elemental Property—Adsorption Energy Descriptor—Decomposition Barrier” Three-Tier Model for Screening Biatom Catalysts in Sodium–Sulfur Batteries

Developing highly efficient catalysts for the Na₂S redox process and sodium polysulfide anchoring is becoming increasingly important for high-performance sodium–sulfur (Na–S) batteries. The recently emerged graphene-supported biatom catalysts (G-BACs) exhibit great potential for providing high activity in both discharging and charging processes. However, the fast screening of promising G-BACs for Na–S batteries is hindered by the formidable computational cost for calculating Na₂S decomposition barriers (E_b). Herein, this work develops an “elemental property—adsorption energy descriptor—decomposition barrier” three-tier model to accelerate this process and elucidates the origin of catalytic activity. It is found that E_b during the charging process is linearly correlated with the adsorption energy difference between the initial and final states of Na₂S decomposition (E_diff) for both homonuclear and heteronuclear transition metal G-BACs. This work further correlates E_diff with intrinsic properties of metal elements by machine learning approaches and unravels the most significant elemental feature to be the outer electron number. This work not only accelerates the design of highly efficient G-BACs in Na–S batteries based on the structure–activity relationship, but also provides a feasible strategy for the fast screening of catalysts for other electrochemical reactions for potential experimental design.