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, reducing inaccuracies in barrier prediction in pre-trained potentials caused by the insufficient training data on high-energy states. We employed the fine-tuned CHGNet model in NEB/MD calculations and the dual CHGNet-NEB/MD achieved a balance between computational speed and accuracy, as validated in NASICON-type Li_1+xAl_xTi_2−x(PO₄)₃ (LATP) structures. Through high-throughput screening, we identified orthorhombic Pnma-group structures (LiMgPO₄, LiTiPO₅, etc.) which can serve as promising frameworks for fast ion conductors. Their aliovalent-doped variants, Li_0.5Mg_0.5Al_0.5PO₄ and Li_0.5TiPO_4.5F_0.5, showing low activation energies, were predicted to possess high ionic conductivities of 0.20 mS cm⁻¹ and 0.022 mS cm⁻¹, respectively.