The shuttle effect of lithium polysulfides (LiPS) leads to fast capacity loss in lithium-sulfur batteries, which hinders the practical applications and makes the discovery of shuttle effect-suppressive sulfur host materials highly significant. Here, we proposed a machine learning (ML) method to rapidly and accurately predict the binding energies towards LiPS including Li₂S₄, Li₂S₆, and Li₂S₈ adsorbed on the surface of sulfur hosts with arbitrary configurations and active sites. As a case study, MoSe₂ was selected as a sulfur host to predict the binding energy when absorbing the LiPS. The ML method shows six orders of magnitude faster than the conventional density functional theory (DFT), with a low predicted mean absolute error (MAE) of 0.1 eV. Based on the transfer learning (TL), we demonstrated that the presented ML method can be transferred to other layered compounds with a similar AB₂ structure to MoSe₂, and can efficiently predict their binding strengths with hosts. WSe₂ was employed as a case to validate the TL method, with the results showing that MoSe₂ had a stronger binding strength than WSe₂ when adsorbing the LiPS, and only one-seventh of the ML training data was required. The impacts of different adsorption sites, configurations and distances on the binding energy were analyzed when LiPS is absorbed, which is of great significance to understand the adsorption mechanism of LiPS with hosts. The proposed work provides an efficient ML method to screen and discover new AB₂ typed two-dimensional layered materials for suppressing the shuttle effect in lithium-sulfur battery.

聚硫化锂（LiPS）的穿梭效应导致锂硫电池的快速容量损失，这阻碍了实际应用，并使得抑制穿​​梭效应的硫基质材料具有重大意义。在这里，我们提出了一种机器学习（ML）方法，以快速准确地预测与LiPS结合的能量，包括以任意构型和活性位点吸附在硫主体表面上的Li ₂ S ₄，Li ₂ S ₆和Li ₂ S ₈。 。作为案例研究，MoSe ₂硫被选作硫主体，以预测吸收LiPS时的结合能。ML方法显示出比常规密度泛函理论（DFT）快6个数量级，且预测平均绝对误差（MAE）仅为0.1 eV。基于转移学习（TL），我们证明了所提出的ML方法可以转移至AB ₂结构与MoSe ₂类似的其他层状化合物，并且可以有效地预测其与宿主的结合强度。以WSe ₂为例验证TL方法，结果表明MoSe ₂具有比WSe ₂更强的结合强度当吸附LiPS时，仅需要ML训练数据的七分之一。分析了不同的吸附位，构型和距离对LiPS吸附时结合能的影响，这对于理解LiPS对主体的吸附机理具有重要意义。所提出的工作提供了一种有效的ML方法来筛选和发现新的AB ₂型二维层状材料，以抑制锂硫电池的穿梭效应。