Accelerated stable structure prediction of Li-intercalated bilayer graphene using a data-efficient deep learning framework

Abstract

Replacing bulk graphite with few-layer graphene is a promising route toward improving both the storage capacity and rate capability of fast-charging Li-ion batteries. As the simplest layered graphene system, bilayer graphene (BLG) provides an ideal model for uncovering the structural origins of these improvements, which in turn requires accurate identification of its thermodynamically stable structures. However, predicting stable Li-intercalated structures requires exhaustive exploration of a vast configurational space using density functional theory (DFT), which rapidly becomes computationally prohibitive for large supercells. Here, we present DESSP, a data-efficient deep learning framework for accelerated stable structure prediction in Liintercalated BLG. DESSP combines a genetic-algorithm-based search pipeline with a universal machine-learning interatomic potential (MLIP) used as a surrogate for DFT, enabling broad yet efficient exploration of the potential energy surface. Representative structures sampled along the search trajectories are then selectively labeled with DFT to train a high-fidelity MLIP, while a distillation-based strategy further improves predictive accuracy. The resulting model achieves near-DFT accuracy, generalizes effectively to larger supercells, and reproduces the thermodynamic convex hull at substantially lower computational cost, providing a scalable framework for studying nanoscale layered electrodes in batteries.