Polyhedral-Cluster Machine Learning Potential for Efficient and Accurate Modeling of Complex Li-Ion Cathodes

Abstract Li-ion layered cathodes undergo complex structural evolution during delithiation, where local coordination changes, transition-metal migration, and oxygen-framework distortion are coupled across multiple length scales. Machine learning interatomic potentials can enable efficient atomic modeling of these processes, but conventional local descriptors often struggle to capture medium-range structural correlations without introducing redundant features. Here, a polyhedral-cluster potential (PCP) is proposed for Li–Mn–Ti–O cathode materials to combine short-range local coordination and medium-range oxygen-framework connectivity in a compact representation. SOAP descriptors encode the local environments within oxygen-centered polyhedra, while O–O-mediated cluster features describe interpolyhedral correlations. Compared with a conventional local potential, PCP improves DFT energy and force predictions for both ordered rocksalt-like and delithiated distorted structures, reducing the force MAE of the distorted structure from 101.22 to 41.99 meV Å–1. Descriptor analysis further shows that PCP decreases the feature number from 952 to 250 and lowers redundancy from 45.6% to 18.0%, while increasing the contribution of physically meaningful O–O correlations. Applications to equation-of-state (EOS) and Li-ion migration-path configurations demonstrate that PCP reproduces DFT energy trends under lattice strain and nonequilibrium defect environments. This work may provide a compact and interpretable descriptor framework for machine learning potentials of complex Li-ion cathode materials.