Machine Learning‐Accelerated Design of High‐Efficient Lithium Adsorbents for Salt Lake Brines

Abstract The urgent demand for lithium resources has been advancing the development of highly efficient lithium extraction from brines. Aluminum‐based lithium adsorbent, as the only one used in the Salt Lake industry, is limited in further improving the extraction efficiency due to the low adsorption capacity. Here, we develop a high‐throughput screening framework via interpretable machine learning (ML) to rapidly determine high‐performance modification strategies for the aluminum‐based lithium adsorbent, avoiding the huge workload of traditional trial‐and‐error doping experiments in view of multiple doping schemes and the unique trade‐off between performance and structural stability. Relying on the recommended modifications, a series of doped adsorbents are prepared and the structure stability and cyclic adsorption performance are verified, which match well with the uncovered correlation between dopant features and adsorption capacity. Experimental validations confirm the screened doped one exhibits an increased stable adsorption capacity by nearly 40% in various types of brine. These results indicate that ML‐accelerated approach can significantly promote the upgrading of lithium resource adsorption industry in Salt Lakes.