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

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.