High-Throughput NEB for Li-Ion Conductor Discovery via Fine-Tuned CHGNet Potential

Abstract

Solid-state electrolytes are essential in the development of all-solid-state batteries. While density functional theory (DFT)-based nudged elastic band (NEB) and ab initio molecular dynamics (AIMD) methods provide fundamental insights on lithium-ion migration barriers and ionic conductivity, their computational costs make large-scale materials exploration challenging. In this study, we developed a high-throughput NEB computational framework integrated with the fine-tuned universal machine learning interatomic potentials (uMLIPs), enabling accelerated prediction of migration barriers based on transition state theory for the efficient discovery of fast-ion conductors. This framework automates the construction of initial/final states and migration paths, mitigating the inaccurate barriers prediction in pretrained potentials due to the insufficient training data on high-energy states. We employed the fine-tuned CHGNet model into NEB/MD calculations and the dual CHGNet-NEB/MD achieves a balance between computational speed and accuracy, as validated in NASICON-type Li 1+x Al x Ti 2-x (PO 4 ) 3 (LATP) structures. Through high-throughput screening, we identified orthorhombic Pnma-group structures (LiMgPO 4 , LiTiPO 5 , etc.) which can serve as promising frameworks for fast ion conductors. Their aliovalent-doped variants, Li 0.5 Mg 0.5 Al 0.5 PO 4 and Li 0.5 TiPO 4.5 F 0.5 , were predicted to possess low activation energies, as well as high ionic conductivity of 0.19 mS/cm and 0.024 mS/cm, respectively.