Abstract: The present disclosure relates to the technical field of computational materials science, and in particular to a method and a system for rapidly predicting a gas adsorption property of a material based on artificial intelligence. The present disclosure innovatively adopts a simulation-experiment-integrated descriptor, namely gas adsorption isotherm, combined with an innovatively proposed query sampling strategy. The present disclosure can acquire the adsorption property of a material for one or more other gases only by inputting the adsorption data of the material for one gas acquired through an experiment or simulation. Therefore, the present disclosure reduces the research and development costs of adsorption materials and improves research and development efficiency.

Abstract: The present disclosure provides an ultra-high stability phenazine-based electrolyte for an alkaline aqueous organic redox flow battery (AORFB), and a flow battery, and belongs to the technical field of electrochemical energy storage. The electrolyte includes a cyclic supramolecule with a rigid polyhydroxy conical cavity structure that is hydrophobic internally and hydrophilic externally, a phenazine-based organic compound, and an alkaline aqueous solution. The cyclic supramolecule is a cyclodextrin or a derivative thereof. The cyclodextrin or the derivative thereof can improve the reaction kinetics of the phenazine-based organic compound and enhance the utilization and stability. The electrolyte has the advantages of easy availability of raw materials, easy operations, and low cost, and can be used to produce alkaline AORFBs with small polarization, high capacity utilization efficiency, prominent rate performance, high energy efficiency, and excellent stability.