Automated Pipeline for High-Throughput Screening of 2D Electrode Materials for Alkali Metal-Ion Batteries

Two-dimensional materials exhibit exceptional properties, making them attractive candidates for battery electrodes. However, evaluating their performance across large data sets, experimentally or via density functional theory, is resource-intensive. To address this, we present a scalable, high-throughput automated framework that employs a universal machine learning potential to expedite the evaluation of MT 2 -type (M: metals, T: terminal functional groups) electrode materials for Li-, Na-, and K-ion batteries. Beginning from the unit cell, the pipeline identifies adsorption sites, simulates stepwise ion insertion and removal, calculates voltage profiles, and tracks structural changes. Through rigorous screening, we identify 39, 36, and 15 promising electrode materials for Li +, Na +, and K +, respectively. To elucidate ion-host interactions, we further analyzed the role of terminal groups and metal layers on ion adsorption. This approach has the potential to serve as a fast, reliable, and chemically informed tool for evaluating 2D materials, supporting experimental synthesis while minimizing trial-and-error.