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oai:figshare.com:article/34021079 2026-09-29T04:06:45Z category_1 category_4 category_146 category_7 category_12 category_24 category_873 category_39 category_734 portal_63 item_type_6 month_year_09_2026

Polyhedral-Cluster Machine Learning Potential for Efficient and Accurate Modeling of Complex Li-Ion Cathodes Nuo Fang (25135085) Wujie Qiu (4754052) Shuyu Wu (772048) Shoutian Sun (3741193) Bin Yu (14464) Wenwei Tang (561956) Jifen Wang (15909533) Nian Ran (8340177) Jianjun Liu (28194) Biophysics Biochemistry Space Science Medicine Cell Biology Evolutionary Biology Chemical Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified work may provide soap descriptors encode path configurations demonstrate nonequilibrium defect environments machine learning potentials local coordination changes 99 mev å range local coordination delithiated distorted structures conventional local potential interpretable descriptor framework ion cathodes li ion cathode materials cathode materials ion migration distorted structure descriptor analysis cluster potential framework distortion framework connectivity range oxygen physically meaningful ordered rocksalt metal migration lowers redundancy lattice strain force predictions force mae feature number complex li combine short centered polyhedra accurate modeling 0 %, 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. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acs.jpclett.6c02477.s001 https://figshare.com/articles/journal_contribution/Polyhedral-Cluster_Machine_Learning_Potential_for_Efficient_and_Accurate_Modeling_of_Complex_Li-Ion_Cathodes/34021079 CC BY-NC 4.0