  Two-dimensional (2D) materials have received considerable attention as
possible electrodes in Li-ion batteries (LIBs), although a deeper understanding
of the Li adsorption behavior as well as broad screening of the materials space
is still needed. In this work, we build a high-throughput screening scheme that
incorporates a learned interaction. First, density functional theory and graph
convolution networks are utilized to calculate minimum Li adsorption energies
for a small set of 2D metallic materials. The data is then used to find a
dependence of the minimum Li adsorption energies on the sum of ionization
potential, work function of the 2D metal, and coupling energy between Li+ and
substrate. Our results show that variances of elemental properties and density
are the most correlated features with coupling. To illustrate the applicability
of this approach, the model is employed to show that some fluorides and
chromium oxides are potential high-voltage materials with adsorption energies <
-7 eV, and the found physics is used as the design principle to enhance the Li
adsorption ability of graphene. This physics-driven approach shows higher
accuracy and transferability compared with purely data-driven models.
