High-throughput assessment of two-dimensional electrode materials for energy storage devices

The ultra-large surface-to-mass ratio of two-dimensional (2D) materials has made them an ideal choice for electrodes of compact lithium (Li)-ion batteries and supercapacitors; however, only a small fraction of the massive 2D material space has been investigated for such applications. Here, combining explicit-ion and implicit-solvent formalisms, we develop an automated, first-principles-based, high-throughput computational framework to assess thousands of such materials. We define four descriptors to map �computationally soft� single-Li-ion adsorption to �computationally hard� multiple-Li-ion-adsorbed configuration located at global minima for insight finding and rapid screening. Leveraging this large dataset, we also develop crystal-graph-based machine learning models for the accelerated discovery of potential candidates. A reactivity test with commercial electrolytes is further performed for wet experiments. Our holistic approach, which predicts both Li-ion storage and supercapacitive properties and hence identifies various important electrode materials that are common to both devices, may pave the way for next-generation energy storage systems. © 2021 The Author(s)